PhD Thesis by Yu-Hsuan Juan by Yu-Hsuan Juan

Analysis of urban wind energy potential around high-rise buildings in close proximity using computational fluid dynamics Yu-Hsuan Juan

Bouwstenen

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Analysis of urban wind energy potential around high-rise buildings in close proximity using computational fluid dynamics

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de rector magnificus prof.dr.ir. F.P.T. Baaijens, voor een commissie aangewezen door het College voor Promoties, in het openbaar te verdedigen op dinsdag 8 oktober 2021 om 11:00 uur

door

Yu-Hsuan Juan

geboren te Taipei, Taiwan

Dit proefschrift is goedgekeurd door de promotoren en de samenstellingvan de promotiecommissie is als volgt: voorzitter: prof.dr. B.J.F. Colenbrander e 1 promotor: prof.dr.ir. B. Blocken prof.dr. A.S. Yang (National Taipei University of Technology) copromotor: dr. H. Montazeri leden: prof.dr.ir. J.L.M. Hensen prof.dr. H.C. Chuang (National Taipei University of Technology) prof.dr. Y.C. Shih (National Taipei University of Technology) adviseurs: dr.ir. A. Rezaeiha

Het onderzoek of ontwerp dat in dit proefschrift wordt beschreven is uitgevoerd in overeenstemming met de TU/e Gedragscode Wetenschapsbeoefening

Analysis of urban wind energy potential around high-rise buildings in close proximity using computational fluid dynamics Yu-Hsuan Juan Eindhoven University of Technology October 2021

The research presented in this thesis was conducted in a dual degree between Eindhoven University of Technology and National Taipei University of Technology. It was funded by the European Commission's Framework Program Horizon 2020, through the Marie Curie Innovative Training Network (ITN) AEOLUS4FUTURE - Efficient harvesting of the wind energy, Ministry of Science and Technology, Taiwan, ROC (Contract No. 107-2917-I-027-001), Ministry of Education Republic of China (Taiwan), and National Taipei University of Technology.

Be water.

Acknowledgements This thesis is presented as my final part of the double PhD program at Eindhoven University of Technology and National Taipei University of Technology. It would not have been possible without the help and support of a long list of people. First and foremost, I am extremely grateful to my supervisors, prof. Bert Blocken and prof. An-Shik Yang for their invaluable advice, continuous support, and patience during my PhD study. I would like to express my great appreciation to Bert, and thank him for his responsiveness that brought me to the Netherlands, as well as for inspiring and guiding my way through this unpredictable PhD journey in the unit Building Physics and Services. I have learned a lot from his expertise and inspired from his dedication to work and academic excellence. I am grateful for being part of his mission to make his group’s research on Urban Physics a well-established international reference in the field. I am also very thankful to An-Shik, who has always encouraged me since the start of my master's degree. He is much more than a supervisor to me and more of a father that guides me throughout the journey. Without his support and advice on research and career planning, I could not have come this far. Secondly, I would like to extend my sincere thanks to my co-supervisor, dr. Hamid Montazeri, for all his patient support, careful instruction, ever encouragement, and motivating guidance with a perfect blend of insight and humor. His insightful feedback pushed me to sharpen my thinking and brought my work to a higher level. I am also deeply grateful to my daily supervisor, dr. Abdolrahim Rezaeiha for his feedbacks and support. He offered much advice and insight throughout my research from multiple different perspectives. I am very grateful to him for that. Additionally, thanks to my committee members, Jan L.M. Hensen, HoChiao Chuang, Yang-Cheng Shih, and chairman prof. dr. Bernard Colenbrander, who offered guidance and support. Last but not least, I would like to thank all my talented colleagues and friends in our research group: dr. Twan van Hooff, dr. Alessio Ricci, dr. Stefanie Gillmeie, dr. Luyang Kang, Feiyu Geng, Anjali Krishnan, Mutmainnah Sudirman, Sadra Sahebzadeh, Josip Žužul, , Xing Zheng, Lili Xia, Samy Iousef, Anto Moediartianto, Zhijun Wang, Qin Peng, Muxi Lei, Claudio Alanis Ruiz, Thijs van Druenen, dr. Paul Mannion, dr. Katarina Katic, dr. Yasin Toparlar, dr. Fabio Malizia, dr. Adelya Khayrullina, for their cooperation and their hard work. It has been a great memory to me that I can join this big family. Then, I would like to extend my gratitude to my previous supervisor at The Hong Kong Polytechnic University, Hong Kong, prof. Chih-Yung Wen. He offered much advice and insight throughout my work. I learned a lot from him and I enjoyed every single moment of working with you. Additionally, I would like to express gratitude to dr. Hao Zhang, dr. Xuelin Zhang, dr. Zhengtong Li, dr, Heriberto Saldivar Massimi, dr. Asiri U. Weerasuriya, dr. Li-Yu Tseng, dr. Jyun-Rong Zhuang for their treasured support which was influential in shaping my research methods and critiquing my results. I would like to express my sincere gratitude to European Innovative Training Network (ITN) Project AEOLUS4FUTURE, funded by the Marie Sklodowska-Curie Actions (MSCA) in Horizon 2020 for letting me be part of this incredible network. Additionally, this PhD journey would not have been possible without the financial support of the Ministry of Education of

Acknowledgements Taiwan, the Ministry of Science and Technology of Taiwan, and National Taipei University of Technology to whom I am sincerely grateful. I am blessed with many dear friends, Ya-Chun Tsao, David Otto, Francesc Sastre, Antonio Sánchez Barrera, Berk Tamac, Afsaneh Sadeghi, Yu-Hsuan Wang and Han-Wen Chang I have met in the Netherlands and they are now a big part of my life. It is their kind help and support that have made my study and life in Eindhoven a wonderful time. I also want to thank Wan-Yi Chen, Yi-Pin Chen, Yun Tseng, Hiu-Pan Wong, Fu Lin You, Kuan-Pin Chen, Yao-Chen Kuan, and Kelly Chen for always believing in me and encouraging me during this challenging period. Thank you to Chi Lok Tsang for taking such beautiful photographs as my thesis cover and all the support. Thank you to all my lover, Shu-Wei Liu, Zhe-Yu Huang, Hiu-Sui Chan, and Yu Shang, who endured this long process and dark times with me, always offering support and love. Finally, I could not have completed this dissertation without the support of my friends, Pin-Yi Nu, Yuan Cheng, Wei-En Chen and Sungwoo Shon, who provided stimulating discussions as well as happy distractions to rest my mind outside of my research. Most importantly, I would like to express my gratitude to my parents with their unconditional love, Jen-Kuang Juan and Chung-Lin Tan. Without their tremendous understanding and encouragement in the past few years, it would be impossible for me to complete my study. I could not have asked for a more spectacular support system.

Table of contents

Acknowledgements.................................................................................................................................. i Table of contents ................................................................................................................................... iii Summary 1 1

Introduction........................................................................................................................................ 5 1.1 Problem statement .............................................................................................................. 5 1.2 Aim and objectives ............................................................................................................. 8 1.3 Methodology ....................................................................................................................... 8 1.4 Thesis outline ...................................................................................................................... 9

Urban wind energy potential: Impact of building arrangement and height ........................ 11 2.1 Introduction ....................................................................................................................... 12 2.2 CFD validation study ....................................................................................................... 15 2.2.1 Wind-tunnel measurements.............................................................................. 15 2.2.2 CFD validation: computational domain and grid .......................................... 16 2.2.3 CFD validation: other computational settings ................................................ 17 2.2.4 CFD validation: grid-sensitivity analysis ........................................................ 19 2.2.5 CFD validation: turbulence model sensitivity analysis ................................. 19 2.3 List of cases ........................................................................................................................ 22 2.4 CFD simulations ............................................................................................................... 22 2.4.1 Computational domain and grid...................................................................... 22 2.4.2 Other computational settings............................................................................ 23 2.4.3 Grid-sensitivity analysis .................................................................................... 23 2.5 Results ................................................................................................................................ 24 2.5.1 Impact of passage width between the two upstream buildings (w) ............ 24 2.5.2 Impact of streamwise distance between upstream and downstream buildings (d) ........................................................................................................ 28 2.5.3 Impact of height difference between upstream and downstream buildings (ΔH) ...................................................................................................................... 31 2.5.4 Impact of wind turbine type/orientation ........................................................ 34 2.5.5 Impact of wind direction ................................................................................... 36

Table of contents 2.6 2.7

Limitations of the study ................................................................................................... 37 Conclusions ........................................................................................................................ 38

Urban wind energy potential: Impacts of building corner modifications ............................ 39 3.1 Introduction ....................................................................................................................... 40 3.2 CFD validation study ....................................................................................................... 43 3.2.1 Description of the experiment ........................................................................... 43 3.2.2 CFD validation: computational settings and results ...................................... 44 3.3 Test cases ............................................................................................................................ 46 3.4 CFD simulations ................................................................................................................ 47 3.4.1 Computational domain and grid ...................................................................... 47 3.4.2 Computational settings and parameters .......................................................... 47 3.4.3 Grid-sensitivity analysis .................................................................................... 48 3.5 Results................................................................................................................................. 50 3.5.1 Impact of building corner shape ....................................................................... 50 3.5.2 Impact of chamfer length ................................................................................... 56 3.5.3 Impact of corner radius ...................................................................................... 57 3.5.4 Impact of wind turbine type and orientation .................................................. 59 3.5.5 Impact of wind direction ................................................................................... 62 3.6 Limitations of the study ................................................................................................... 63 3.7 Conclusions ........................................................................................................................ 63

Urban wind energy potential: Impacts of urban density and layout ..................................... 65 4.1 Introduction ....................................................................................................................... 66 4.2 CFD validation study ....................................................................................................... 69 4.2.1 Turbulence model sensitivity analysis ............................................................. 71 4.3 CFD simulation ................................................................................................................. 72 4.3.1 Description of urban scenarios.......................................................................... 72 4.3.2 Computational domain and grid ...................................................................... 74 4.3.3 Computational settings ...................................................................................... 75 4.3.4 Grid-sensitivity analysis .................................................................................... 76 4.4 Results................................................................................................................................. 76 4.4.1 Impact of urban density ..................................................................................... 77 4.4.2 Impact of building corner shape ....................................................................... 83 4.4.3 Impact of urban layout ....................................................................................... 90 4.4.4 Impact of wind direction ................................................................................... 90 4.5 Discussion .......................................................................................................................... 91 4.6 Conclusions ........................................................................................................................ 93 Appendix ..................................................................................................................................... 93

Urban wind energy potential for a realistic high-rise urban area........................................... 97 5.1 Introduction ....................................................................................................................... 98 5.2 Studied area - highly urbanized areas of Hong Kong ................................................ 103 5.2.1 Wind energy resources in Hong Kong ........................................................... 103 5.2.2 Local Meteorological Data ............................................................................... 103

5.3 5.4

5.5

5.6 5.7

On-site wind measurements ......................................................................................... 104 CFD simulations ............................................................................................................. 106 5.4.1 Computational domain and grid.................................................................... 106 5.4.2 Governing equations and boundary conditions........................................... 107 Results and discussion ................................................................................................... 110 5.5.1 Comparison of CFD predictions with field-measurement results ............. 110 5.5.2 Wind resource assessment indices: power density and turbulence intensity 111 5.5.3 Wind power estimation ................................................................................... 112 Discussion ........................................................................................................................ 128 Conclusions ..................................................................................................................... 129

Discussion ...................................................................................................................................... 131

Summary and Conclusions.......................................................................................................... 133

References ............................................................................................................................................. 137 Biography .............................................................................................................................................. 151 List of publications ............................................................................................................................. 153

Table of contents

Summary Among the various forms of renewable energy, wind energy has become one of the most popular resources. Particularly, the use of wind turbines in urban environments has attracted increasing attention. In view of sustainable development and urban expansion, high-rise buildings are turning into the mainstream of city advancements in consequence of the rising urban population density and limited land availability in large parts of the world, especially in East-Asia. For compact urban areas, the urban wind energy potential can be improved through early urban design planning, as urban density, layout, building geometry and arrangement strongly influence the urban wind conditions. The spacing between the buildings, which is usually very small compared to the building height for compact high-rise building areas, can significantly increase the mean wind speed and the wind energy potential because of the wellknown concentration effect. Therefore, the impact of building geometry and arrangement are of particular interest to be characterized in view of improving the urban wind energy potential. Computational fluid dynamics (CFD) has been recognized as an effective and affordable tool for the assessment of urban wind energy potential. Therefore, in this thesis, CFD simulations, validated with wind-tunnel experiments, are used to analyze the urban wind energy potential around high-rise buildings in close proximity In Chapter 2, the urban wind energy potential is assessed for generic high-rise buildings in close proximity focusing on the building arrangement and height, including (i) the passage width between the upstream buildings (w) varying from 0.15B to 0.75B, (ii) the streamwise distance between the upstream and the downstream buildings (d) varying from 0.15B to 0.6B, and (iii) the height difference between the upstream and the downstream buildings (ΔH) varying from -0.3H to 0.3H, and (iv) wind turbine type and orientation for the typical horizontal axis wind turbines (HAWTs), the typical (vertically-mounted) vertical axis wind turbines (VAWTs), and the horizontally-mounted VAWTs. The study investigates the wind field around four identical fullscale high-rise buildings, with a building height-to-building side length ratio of H/B = 4.5 and H/w = 30, in a 2×2 array. A CFD validation study examines the predictiven capabilities of five commonly-used Reynolds-averaged Navier-Stokes (RANS) turbulence models, i.e., standard k-ε model (SKE), realizable k-ε model (RKE), and renormalization group k-ε model (RNG), shearstress transport k-ω model (SST) and reynolds stress model (RSM), as compared to the windtunnel experimental data. Wind tunnel measurements of the 3-component flow velocity and turbulence intensity are performed using a Cobra probe for the validation of the reduced-scale CFD simulations. The results show reasonable agreement for the mean streamwise velocity and turbulence intensity between the predictions of the RSM turbulence model and the experimental data. The results show that (i) the wind power density along the upstream passage increases by decreasing w, increasing d and for an equal building height (ΔH = 0); (ii) high w values (> 0.15B), low d values (< 0.6B), and low upstream building heights (ΔH < 0) can realize high wind power densities between the downstream buildings; (iii) the horizontally-mounted VAWT is the most promising option for wind energy harvesting with a maximum of 37% higher wind power density, as compared to the HAWT and the VAWT in the passage between the buildings and on the rooftop. In Chapter 3, the analysis is focused on the impact of different building corner modification parameters on the available wind power density and the reference turbulence intensity (Iref), including (i) the impacts of the building corner shape (i.e. sharp, chamfered, and rounded shapes), (ii) the impact of the chamfered corner length (l) varying from 0.05B to 0.2B, (iii) the rounded

Summary

corner radius (r) varying from 0.05B to 0.2B, (iv) the impact of the wind turbine type and orientation (i.e., HAWT, VAWTs, and horizontally-mounted VAWTs) for the aforementioned 2×2 building array. The findings reveal that (i) there are noticeable influences of the corner modification on the realization of higher wind power density up to 1167%, and lower total turbulence intensity down to 50%, on three prospective regions along the passage between the buildings, on the building roofs, and beside the buildings; (ii) the rounded corner is the most favorable building corner shape yielding a maximum of 337% higher wind power densities, as compared to the sharp corner, with a comparatively significant reduction of 52% in total turbulence intensity on the roof under both normal and oblique wind directions; (iii) by increasing the chamfer length to 0.20B or rounded corner radius to 0.20B, wind power density can be dramatically enhanced up to 110% and 350%, respectively, besides the high-rise buildings; (iv) for all three aforementioned building corner shapes, the horizontally-mounted VAWTs yield a maximum of 24% higher wind power densities than others in the passage between the buildings and on the rooftop, while typical (vertically-aligned) VAWTs enhance a maximum of 43% higher wind power densities than others besides the buildings. In Chapter 4, the urban wind energy potential is assessed for a 6×6 array of generic high-rise buildings. A comprehensive parametric study is conducted to analyze the impacts of urban morphologies on the power density and the Iref, including (i) urban densities altered from compact to sparse urban layouts, (ii) building corner shapes of sharp and rounded corners, (iii) urban layouts of in-line and staggered patterns, and (iv) wind directions of 0° and 45° for a 6×6 array of generic high-rise buildings. The findings indicate that (i) decreasing urban plan area density (λp) reduces the unacceptable turbulence areas with relatively higher wind power density on the roof; (ii) round corners can produce elevated power densities up to 201% and 150% than those of sharp corners for those selected areas beside the building and on the roof, respectively; (iii) the urban layout of staggered pattern leads to poorer wind energy potentials with reduced wind power densities and escalated turbulence intensities than the in-line pattern; (iv) even under the oblique wind direction of 45°, increasing urban plan area density to the compact urban layout (λp = 0.76) can increase the power densities up to 268%, especially for the round corners. Chapter 5 performs wind resource assessment around a realistic compact high-rise urban area in Central, Hong Kong using full-scale CFD simulations. This study has fully considered the effects of local climate, urban landscape, complex building shapes, and existent obstacles in the complicated urban areas on the local wind velocity, power density, and total turbulence intensity to pinpoint the appropriate sites for turbine installation. The wind resource assessment is focused on different building features of existing high-rise buildings, including (i) building shape (i.e., cuboid with sharp or round edges, rounded, octagonal star-shaped, and triangular shapes), (ii) roof shape (i.e., flat, doom-like, pyramid-like, prism-like roofs), (iii) presence or absence of upstream obstacles, (iv) arrangements of the integrated building complex, and (v) layouts of parallel high-rise buildings. On-site measurements of the wind velocity, the wind direction, and the total turbulence intensity are conducted at 16 monitored points using the 3D ultrasonic anemometers and the thermal flow velocity probe during the daytime over 7 days for the validation of the CFD simulations in both winter and summer periods. The results show that (i) the CFD validation of wind speed, wind direction, and total turbulence intensity are in reasonable agreement with the on-site measurement data in summer and winter with differences of less than 20%; (ii) for the various building shapes, the building geometry with round edges achieves higher power density and lower turbulence around the two lateral sidewalls than its with sharp edges; (iii) for the various roof shapes, the curved leading edge of the doom-like roof shows up to 80% higher wind power density than flat shape roof; (iv) for the impact of upstream obstacles, one upstream high-rise building reduces 80% wind power density and rises the turbulence intensities over 16% of the downstream building; (v) for integrated building complexes with a narrow

Introduction

passage width , the building arrangement of the orthogonal divergent channel with aligned incoming flows induces relatively the highest power density and lowest turbulence intensity in between the passage than others with oblique incoming flows; (vi) for deep street canyons between parallel high-rise buildings (i.e., H/w > 2), oblique incoming flows cause the promising installation location to appear around lateral outer corners. As a general conclusion, it is found that the arrangement of the high-rise buildings in an array and their geometrical characteristics are very influential factors on the available wind power density and the level of turbulence intensity in the potential locations for wind energy harvesting on the building roofs, in the passage between the buildings and besides them. As a result, early considerations in the initial architectural design of high-rise buildings play a significant role to realize the viability of urban wind energy harvesting for high-rise buildings.

Summary

Introduction 1.1

Problem statement

Climate change has become a critical issue to our environment in the last decades. According to NASA, the global average surface temperature in 2020 shows it to be the hottest year on record[1]. Cities are responsible for about 75% of the greenhouse gas emissions resulting in global warming, while consuming two-thirds of the world's energy. As the main cause of climate change, cities are also the most affected areas of human society. Rapid urbanization can further intensified this process via the urban heat island effect and air pollution. Therefore, it is essential to make cities an integral part of the solutions to address climate change. An environmental approach using sustainable urban design is essential to reduce climate change and its consequences. Implementation of renewable energy strategies in cities is imperative for sustainable development. More than 80% of the cities having set a renewable energy target are located in Europe and North America. As technology continues to advance, renewable energy will become ever more cost-efficient, accessible, and sustainable. In 2020, renewable energy sources made up 38% of electricity in the EU-27, while wind supplied 14% of Europe’s electricity[2]. Among different forms of renewable energy, wind energy has turned into one of the fastest-growing renewable energy resources. Particularly, the use of wind turbines in urban environments has attracted increasing attention [3-5]. Urban airflow can be locally accelerated to boost wind speeds and energy yields when it passes between or over buildings. However, the wind tends to be more turbulent and less predictable in urban areas, due to the complexity and the heterogeneous terrain roughness in the built environment. This causes inevitable challenges for wind energy potential assessment [6, 7] and constitutes some unavoidable uncertainty for the deployment and operation of wind turbines and, thus, necessitates special attention [8, 9]. To investigate urban wind energy, the wind resource assessment requires knowledge of the local wind speed, turbulence intensity, and wind direction at the potential regions for incorporating wind turbines into buildings. Generally, the most suitable potential installation regions are (a) on the roof [10-12], (b) along the passages between buildings [13, 14], and (c) besides the buildings, as shown in Fig. 1.1. The passage between the buildings, which is usually very small as compared to the building height for the case of compact high-rise building areas, can significantly increase the wind speed and the wind energy potential because of the wellknown concentration effect [15, 16]. Moreover, the pressure gradients across the windward and leeward sides and along the roofs of buildings can induce strong wind shears and circulation. Therefore, high-resolution and accurate urban wind resource assessments are crucial for providing the detailed information regarding the potential installation sites [17, 18]. Because the urban wind environment is substantially affected by many factors, it is believed that an effective urban design strategy during the early urban design planning can contribute as a climatic control solution to optimize the wind conditions for sustainable development of wind energy.

Chapter 1

Figure 1.1. Potential regions of incorporating wind turbines into buildings

Concerning the type of wind turbine, the vertical axis wind turbine (VAWT) and the horizontal axis wind turbine (HAWT) are the two most well-known types. Many studies emphasized that the VAWT is highly advantageous over the HAWT primarily due to its potential power gain in vertically skewed airflows and omni-directionality [19-21]. To further evaluate the wind energy potential considering other turbine types, three different combinations of wind turbine type and orientation are illustrated in Fig. 1.2. Note that the relative velocity component for each turbine type is different as indicated. a)

The typical HAWT: the HAWT is positioned with its axis in the horizontal direction. The corresponding power density is calculated using the mean streamwise velocity component (U), as the normal component to the rotor plane. The typical VAWT: the VAWT is positioned with its axis in the vertical direction. The corresponding power density is calculated using the vector sum of the streamwise and lateral velocity components ( U 2 + V 2 ). The horizontally-mounted VAWT: the VAWT is positioned with its axis in the horizontal direction. The corresponding power density is calculated using the vector sum of the streamwise and vertical velocity components ( U 2 + W 2 ).

Due to the different relative velocity components for each turbine type and its orientation, the power density can be different for different turbines at diverse possible installation regions. Therefore, a detailed investigation is required to provide insight into the impacts of the wind turbine type and orientation on the wind power density and acceptable turbulence intensity.

Figure 1.2. Schematic of wind turbine type and orientation.

Introduction

In consideration of sustainable development and urban expansion, in several parts of the world, compact cities with high-rise buildings are turning into the mainstream of city advancements because of many benefits, including increased land accessibility, opposing urban sprawl, less car dependency therefore lesser emission of pollutants, lower carbon footprint, and renewable resource-intensive development [22-25]. Until 2020, 1924 buildings above 200 m have been completed around the world. The city that has the most skyscrapers is Hong Kong, which is an extreme case of a high-density city. To formulate a sustainable development solution, a compact city with high-rise buildings requires cautious and proper design considerations to work in harmony with different design impacts in order to enhance wind energy growth for a clean environment. For such compact urban areas, the urban wind energy potential can be significantly improved by varying the morphological parameters, such as the urban layout [26-28], the urban density [29-31], the building geometry [32-34], and the building corner modification [35-37], etc. Fig. 1.3 illustrates a schematic of morphological parameters in a high-rise building array.

Figure 1.3. Schematic of morphological parameters in a high-rise building array.

In recent decades, many studies have focused on the effects of wind over an isolated building or a cubic array in conjunction with the essential environmental aerodynamics issues. With increasing urbanization, the renewed interest of high-rise buildings and their building array for flow complexities and urban ventilation has resulted in more research [38-40] while leading to the further necessity of understanding the urban wind energy harvesting and improvement of their available wind energy potential. Nevertheless, the compactness of high-rise buildings has received much less attentions than that of typical medium-dense urban layouts. Towards optimal design of compact high-rise urban areas, this thesis, therefore, is required to investigate the aforementioned impacts of the morphological parameters on improving the urban wind energy potential at the potential regions for incorporating wind turbines into building groups.

Chapter 1

As an effective tool for the early-stage urban design, investigators commonly conduct computational fluid dynamics (CFD) simulations to analyze the effect of morphological parameters. CFD simulation has been recognized as an efficient, affordable and suitable tool for systematic parametric analysis of urban wind energy potential. It can fully control the initial and boundary conditions to obtain whole flow field data on all relevant parameters in the computational domain, therefore allowing for variations in the morphological parameters for parametric analysis. It can also be used to quantitatively assess the design strategies for different conceptual architectural layouts in urban planning. Concerning the wind-tunnel experiments, parametric studies of that type are highly costly and time-consuming, while on-site measurements are simply impossible to conduct such extensive parametric studies. However, the accuracy and reliability of CFD simulations are crucial concerns and need to be validated with wind tunnel experiments [41, 42] or on-site measurements [43, 44]. An extensive literature review reveals that very limited studies focused on wind energy potential assessment for high-rise building arrays. Most studies have been performed for generic urban configurations with idealized geometry, while the geometry of real urban environments is much more complex. Although many studies have been conducted to evaluate the urban wind energy potential, accurate models for multiple high-rise buildings in compact cities are still lacking. It is vital that CFD simulations require a systematic study of different morphological parameters on the wind energy potential around compact high-rise buildings in both generic urban configurations and real urban case studies. By achieving the objectives of this research, it can be more effective to investigate wider design alternatives through early urban design planning, and thereby devise the optimal configuration of high-rise buildings in compact cities for enhancing the urban wind energy potential for sustainable urban development.

1.2

Aim and objectives

The present thesis aims to assess the impact of different morphological parameters on urban wind energy potential around high-rise buildings in close proximity. This can support the sustainable urban design of compact cities in terms of maximizing wind energy harvesting. The following sub-objectives are defined: − Analyze and validate CFD models for accurate estimates of wind energy potential of highrise buildings in close proximity; − Investigate the impact of different morphological parameters on the wind energy potential for CFD simulations of high-rise buildings in close proximity; − Investigate the different wind turbine placements on the wind energy potential for CFD simulations of high-rise buildings in close proximity; − Analyze and validate CFD models for urban wind energy potential in a realistic compact high-rise urban area.

1.3

Methodology

This study focuses on the urban wind energy potential and the associated complex airflow phenomena around high-rise buildings for both generic and realistic high-rise urban areas. For this purpose, high-fidelity and high-resolution CFD simulations are employed to investigate the effects of varied morphological parameters on the urban wind energy potential for different possible wind turbine installation regions. These are performed as below: 1-

CFD validation of mean wind speed and turbulence intensity over a generic high-rise building array: High-resolution three-dimensional (3D) CFD simulations with the Reynolds-averaged Navier-Stokes (RANS) approach and various turbulence models are

Introduction

1.4

performed to validate the predictions with the wind tunnel experiments in a generic 2×2 high-rise building array placed in close proximity in Chapters 2 - 4. Parametric analysis of urban wind energy potential: A comprehensive CFD analysis of the generic 2×2 high-rise building array is performed to assess the influences of various parameters on the mean wind speed, turbulence intensity and wind power density in Chapters 2. Parametric analysis of urban wind energy potential: A comprehensive CFD analysis of the generic 2×2 high-rise buildings array is performed to assess the influences of various building corner modifications on the mean wind speed, turbulence intensity and wind power density in Chapters 3. Parametric analysis of urban wind energy potential: A comprehensive CFD analysis of the generic 6×6 high-rise buildings array is performed to assess the influences of various morphological parameters on the mean wind speed, turbulence intensity and wind power density in Chapters 4. CFD simulation on urban wind energy potential for a realistic high-rise urban area as the case study: High-resolution CFD of urban wind energy potential in the Central district, Hong Kong will be performed to evaluate the urban wind resource assessment for this realistic compact high-rise urban area in Chapters 5. The accuracy of CFD simulations is validated by on-site measurements in both winter and summer periods.

Thesis outline

This thesis is composed of four main chapters (Chapter 2-5). The limitations and future research perspectives pertaining to the work presented in these chapters are further discussed in Chapter 6 (Discussion). The conclusions are provided in Chapter 7 (Conclusions). The thesis outline is given below. Chapter 2: Juan YH, Rezaeiha A, Montazeri H, Blocken B, Wen CY, Yang AS, CFD assessment of wind energy potential for generic high-rise buildings in close proximity: Impact of building arrangement and height. Submitted. To estimate the urban wind energy potential over generic high-rise urban areas, full-scale CFD simulations are conducted of the wind field for a 2×2 array of generic high-rise buildings in close proximity. Wind-tunnel experimental data are used for validation of the CFD simulations. This chapter presents a systematic parametric analysis of mean wind speed, turbulence intensity and wind power density as influenced by some main geometrical parameters of the building arrangement, including (i) the passage width between the upstream buildings, (ii) the streamwise distance between the upstream and downstream buildings, (iii) the height difference between the upstream and downstream buildings, and (iv) wind turbine type and orientation on the available wind energy potential along the passage between the buildings and on the roofs. Chapter 3: Juan YH, Rezaeiha A, Montazeri H, Blocken B, Yang AS, CFD assessment of wind energy potential for generic high-rise buildings: Impacts of building corner modifications. Submitted. In Chapter 3, the urban wind energy potential is assessed for the aforementioned 2×2 highrise building array as a function of different building corner modification parameters, including (i) the impacts of the building corner shape (i.e. sharp, chamfered, and rounded shapes), (ii) the impact of the chamfered corner length, (iii) the rounded corner radius, (iv) the impact of the wind

Chapter 1

turbine type and orientation on the available wind power density and reference turbulence intensity along the passage between the buildings, on the roofs and beside the buildings. Chapter 4: Juan YH, Wen CY, Li ZT, Yang AS, Impacts of urban morphology on improving urban wind energy potential for generic high-rise building arrays, Applied Energy, 299, 117304, 2021. In Chapter 4, the urban wind energy potential is assessed for a 6×6 array of generic high-rise buildings. It demonstrates a systematic parametric analysis to explore the impact of different morphological parameters, including (i) urban density altered from compact to sparse urban layouts, (ii) building corner shapes of sharp and rounded corners, (iii) urban layouts of in-line and staggered patterns, and (iv) wind directions on the available wind power density and reference turbulence intensity on the roofs and beside the buildings. Chapter 5: Juan YH, Wen CY, Chen WY, Yang AS, Numerical assessments of wind power potential and installation arrangements in realistic highly urbanized areas, Renewable and Sustainable Energy Reviews, 135, 110165, 2021. In Chapter 5, the urban wind energy potential applied for a realistic compact high-rise urban area in Central, Hong Kong, is evaluated using CFD simulations. On-site measurements of mean wind speed, wind direction, and turbulence intensity have been conducted for the validation of the CFD simulations in both winter and summer periods. The wind resource assessment is focused on different building features of existing high-rise buildings, including (i) building shape, (ii) roof shape, (iii) presence or absence of upstream obstacles, (iv) arrangements of the integrated building complex, and (v) layouts of parallel high-rise buildings to explore the urban wind energy harvesting around the existing high-rise buildings.

Urban wind energy potential: Impact of building arrangement and height This chapter is submitted to a peer-reviewed scientific journal:

CFD assessment of wind energy potential for generic high-rise buildings in close proximity: Impact of building arrangement and height Juan, Y.H., Rezaeiha, A., Montazeri, H., Blocken, B., Wen, C.Y., Yang, A.S. Abstract: High-rise building complexes are of great importance for enabling sustainable urban development in large parts of the world. Earlier studies have indicated that high wind speed regions can be present along the passage between two high-rise buildings as well as above the roofs. At such locations, urban wind energy could be harvested by installing wind turbines between and/or above the roof of the buildings. However, the available wind energy potential around an array of generic high-rise buildings in close proximity has not yet been assessed for different building configurations. This paper conducts a detailed evaluation of the impacts of the building arrangement and height for a 2×2 array with a building height-to-street width ratio of 30 on the mean wind velocity and the wind energy potential along the passages between both upstream and downstream buildings as well as on their roofs. The following parameters are analyzed: (i) the passage width between the two upstream buildings (w), (ii) the streamwise distance between the upstream and downstream buildings (d), and (iii) the height difference between the upstream and downstream buildings (ΔH). The 3D steady Reynolds-averaged Navier-Stokes (RANS) equations are solved using the Reynolds stress model (RSM) turbulence model for closure. The CFD results are validated using wind-tunnel measurements of mean wind speed and turbulence intensity performed for the same building array. The results show elevated wind power density along the upstream passages for small w (= 0.15B), high d (= 0.6B), and equal building height (ΔH = 0). In contrast, comparatively high values of w, small d, and ΔH < 0 yield high wind power densities between the downstream buildings. Among the different wind turbine types considered, horizontally-mounted vertical axis wind turbines seem the most promising option for wind energy harvesting between the buildings. Keywords: Urban wind energy; wind resource assessment; urban planning; urban physics; urban morphology; building arrangement

Chapter 2

12 Nomenclature ɛ κ ρ B Cs Cμ d Fs H H/W Hd Hu ΔH k ks L P r U u*ABL Uref V V3D Vref W w y* z0 ABL AD GCI DFR FR HAWT HPs LPs PD PDref TI TIref VAWT

2.1

Turbulence dissipation rate [m2/s3] von Karman constant Air density [kg/m3] Building breadth [m] Roughness constant Model constant Streamwise distance between upstream and downstream buildings [m] Safety factor Building height [m] Building height-to-street width ratio Downstream building height [m] Upstream building height [m] Height difference between upstream and downstream buildings Turbulent kinetic energy [m2/s2] Sand-grain roughness height [m] Building length [m] Formal order of accuracy Linear grid refinement factor Mean streamwise velocity [m/s] Friction velocity [m/s] Mean streamwise velocity at reference height [m/s] Mean lateral velocity [m/s] Mean velocity magnitude [m/s] Mean velocity magnitude at reference height [m/s] Mean vertical velocity component [m/s] Passage width between the two upstream buildings (street width) [m] Dimensionless wall distance Aerodynamic roughness length [m] Atmospheric boundary layer Average absolute deviations Grid-convergence index Dimensionless flow rate Flow rate [m3/s] Horizontal axis wind turbine Horizontal passages between the upstream and downstream buildings Lateral passages between the upstream and downstream buildings Power density [W/m2] Power density at reference height at the domain inlet [W/m2] Turbulence intensity based on the three components [%] Turbulence intensity at reference height [%] Vertical axis wind turbine

Introduction

Among the various forms of renewable energy, wind energy has become one of the most popular resources [18, 45]. Particularly, the use of wind turbines in urban environments has attracted increasing attention [4, 17, 46, 47]. In practice, wind tends to be more turbulent and less predictable in urban areas, due to the complexity of heterogeneous terrain roughness in the built

Urban wind energy potential: Impact of building arrangement and height

environment. This causes inevitable challenges for wind energy potential assessment [6, 7] and constitutes some unavoidable uncertainty for the deployment and operation of wind turbines and, thus, necessitates special attention [8, 9]. In view of sustainable development and urban expansion, high-rise buildings are turning into the mainstream of city advancements in consequence of the rising urban population density and limited land availability in large parts of the world, especially in East-Asia [38]. High-rise buildings with 30 to over 100 stories have become common in cities around the world in the past few decades, according to the Council on Tall Buildings and Urban Habitat (CTBUH) [48]. For compact urban areas, the urban wind energy potential can be improved through early urban design planning, as urban density, layout, building geometry and arrangement strongly influence the urban wind conditions. The spacing between the buildings, which is usually very small compared to the building height for compact high-rise building areas, can significantly increase the mean wind speed and the wind energy potential because of the well-known concentration effect [15, 49, 50]. Therefore, the impact of building geometry and arrangement are of particular interest to be characterized in view of improving the urban wind energy potential. Computational fluid dynamics (CFD) has been recognized as an effective and affordable tool for the assessment of urban wind energy potential [45, 47, 51]. Table 2.1 presents an overview of recent CFD studies on estimation of wind energy potential around buildings. The table highlights the studied building configuration, the main focus, the modeling approach, whether or not a validation study was performed, the studied parameters, the position where the assessment was made and the type of wind turbine considered for the analysis. Table 2.1. Overview of recent CFD studies on estimation of wind energy potential around buildings publication

urban configuration

focus

Alanis Ruiz et al. (2021) [52] Zabarjad Shiraz et al. (2020) [3] Dilimulati et al. (2018) [53] Wang et al. (2018) [54]

3D/G/FS/a building 3D/R/FS/urba n areas 3D/R/FS/urba n areas 3D/R/FS/a building 3D/G/FS/a cuboid 3D/G/FS/2 cuboids 3D/G/FS/a cuboid 3D/G/FS/a cube 3D/G/FS/a cuboid

Blay, Wdir Wdir, Wturb Wdir

Wang et al. (2017) [55] Zhou et al. (2017) [56] Hassanli et al. (2017) [57] Larin et al. (2016) [58] Toja-Silva et al. (2016) [59] Simões et al. (2016) [60] Kono et al. (2016) [61]

3D/R/FS/urba n areas 3D/G/RS/a cuboid

Wdir Rgeo Blay, Wdir Blay, Wdir Wturb Blay, Bsur, Rgeo Bsur Blay, Wdir

modelin g approach RANS/R SM RANS/R KE RANS/R KE RANS/R KE RANS/S KE RANS/S KE RANS/S ST RANS/R KE RANS/S KE

valida tion data WT

outputs

positi on

V, CP, PD

VAW T -

V, TKE, TI V, PD

V, TKE, PD V, P, CP

V, TI

VAW T -

RANS/S KE LES

V, PD

V, TI

TKE,

turbi ne type -

Chapter 2

14 Yang et al. (2016) [17] Heo et al. (2016) [62] Tominaga et al. (2015) [63] Toja-Silva et al. (2015) [64] Toja-Silva et al. (2015) [35] Toja-Silva et al. (2015) [65] Wang et al. (2015) [66] Romanić et al. (2015) [67] Park et al. (2015) [68] Chaudhry et al. (2014) [69] Lu and Sun (2014) [70] Tabrizi et al. (2014) [71] White and (2014) [72]

Wakes

Abohela et al. (2013) [36] Gagliano et al. (2013) [73] Kono and Kogaki (2012) [74] Balduzzi et al. (2012) [75] Millward-Hopkins et al. (2012) [76] Ledo et al. (2011) [37]

3D/R/FS/a campus 3D/G/FS/2 cuboids 3D/G/RS/a cuboid 3D/G/FS/a cuboid 3D/G/FS/a cuboid 3D/G/FS/a cuboids 3D/G/FS/2 cuboids 3D/R/FS/urba n areas 2D/G/FS/a cuboids 3D/R/FS/2 buildings 3D/R/FS/2 cuboids 3D/R/FS/urba n area 3D/G/FS/a cube 3D/G/FS/a cube

Bsur, Rgeo Wdir, Wsp Rgeo Turb Rgeo Rgeo, Wdir Blay, Wdir Wdir Blay Blay Wsp Wdir, Wsp Blay, Rgeo, Wdir Rgeo, Bsur, Wdir Wdir

RANS/R KE URANS/ SST RANS/R NG RANS/S KE RANS/S KE RANS/ MMK RANS/S KE RANS/S ST RANS/S KW RANS/S KE RANS/R NG RANS/S ST RANS/S KE

V, TI, PD

V, P, CP

V, TKE, CP V, TI

HAW T -

V, TI

V, PD

V, TI, PD

V, TKE, PD V

HAW T HAW T -

RANS/R KE

3D/R/FS/urba LES V a n area 3D/G/RS/a Blay LES WT V, PD, TI a cuboid 2D/G/FS/2 Blay, RANS/S WT V a cuboids Bsur KE 2D/G/RS/4x4 Blay LES WT V a cubic array 3D/G/FS/3x3 Rgeo, RANS/S WT V, TI, PD a cubic array Wdir ST Kalmikov et al. (2010) 3D/R/FS/a Wdir RANS/K FL V, TI, PD a [77] campus L Lu and Ip (2009) [78] 3D/G/FS/1~3 Blay, RANS/S V, PD a,b,c cuboids Rgeo KE Heath et al. (2007) [79] 3D/G/FS/6x4 Wdir RANS/S WT V, CP a cube array KE Watson et al. (2007) 3D/G/FS/a Blay, RANS/S V, CP a HAW [80] cuboids Wturb ST T Column 2: R: real; G: generic; FS: full scale; RS: reduced scale. Column 3: Blay: building layout (building height, street width, building geometry, etc.); Bsur: influence of surrounding buildings; Turb: impact of turbulence models; Rgeo: roof geometry; Wdir: wind direction or flow angle; Wsp: wind speed; Wturb: performance of wind turbines. Column 4: RANS: steady Reynolds-averaged Navier-Stokes; RKE: realizable k-ε; SKE: standard k-ε; SKW: standard k-ω; RNG: renormalization Group k-ε; URANS: unsteady RANS; SST: shear stress transport kω; MMK: Murakami-Mochida-Kondo turbulence model; KL: k-L model; LES: large eddy simulation. Column

Urban wind energy potential: Impact of building arrangement and height

5: WT: wind tunnel measurements; FL: field measurements. Column 6: V: velocity; TKE: turbulent kinetic energy; TI: turbulence intensity; P: pressure; PD: power density; CP: power coefficient. Column 7: a: building rooftop; b: between buildings; c: beside buildings; d: incorporating wind turbines in the integral design of buildings. Column 8: VAWT: vertical axis wind turbine; HAWT: Horizontal axis wind turbine.

Table 2.1 shows that the majority of the studies only considered an isolated [7, 52, 57, 61, 65, 72, 74] or two cuboid buildings [56, 62, 66, 69] without including upstream buildings, or a cubic array with low building height to street width ratios (i.e., H/W < 2) [36, 37, 59]. The impact of upstream high-rise buildings on the wind energy potential has not been considered in most of the studies. In addition, only a very few studies focused on the wind energy potential assessment for high-rise buildings in densely packed urban configurations with H/W > 2 [35, 70]. Therefore, the present study intends to analyze the mean wind speed and the wind power density for a 2×2 array of high-rise buildings placed in close proximity, i.e., H/W = 30. The impact of the following parameters is investigated: (i) (ii) (iii) (iv) (v)

passage width between the upstream buildings (w); streamwise distance between the upstream and downstream buildings (d); height difference between the upstream and downstream buildings (ΔH); wind turbine type and orientation; wind direction.

3D steady Reynolds-averaged Navier-Stokes (RANS) simulations are performed using the Reynolds stress model (RSM) turbulence model. The CFD results are validated using wind-tunnel measurements of mean wind speed and turbulence intensity performed for the same building array. The mean wind velocity and wind power density are analyzed. This paper is organized as follows. The CFD validation study is presented in Section 2.2. Section 2.3 describes the list of the simulation cases. The computational settings are presented in Section 2.4. CFD results are explained in Section 2.5. Discussion and conclusions are presented in Section 2.6 and 2.7, respectively.

2.2

CFD validation study

2.2.1

Wind-tunnel measurements

Wind-tunnel experiments are performed for a 2×2 array of high-rise parallel buildings in the small atmospheric boundary layer wind tunnel at Eindhoven University of Technology, see Fig. 2.1a. The test section of the wind tunnel is 13 m long with a cross section of 0.5 m width and 0.65 m height. The atmospheric boundary layer is generated by a set of roughness elements on the floor upstream of the test section. Floor roughness elements stretch downstream up to 0.65 m from the center of the test section, where the building models will be placed. The building models are four square cuboids, at a scale of 1:643 of the high-rise building employed in the CFD simulations in Section 2.2.2. The building breadth B and length L are 0.031 m (20 m in full scale) and the building height H is 0.14 m (90 m in full scale). The 2×2 array has a passage width of w = 0.028 m (18 m in full scale), see Fig. 2.2a. The blockage ratio in the wind tunnel is 2.95%, which is below the recommended 5% [81]. The three components of the instantaneous velocity are measured using a turbulent flow instrumentation (TFI) Cobra Probe [82] at a frequency of 600 Hz within an angular range of ±45°. Based on the probe specifications, the accuracy is within ±0.5 m/s and ±1° (for pitch and yaw at a turbulence intensity up to 30%). Fig. 2.1b shows the incident vertical profiles of the

Chapter 2

dimensionless mean streamwise velocity (U/Uref) and turbulence intensity (TI/TIref) measured in the wind tunnel, where Uref (= 13.4 m/s) and TIref (= 8%) are the values at z = 0.14 m. Note that the incident flow profiles are defined as those measured in the empty tunnel at the position where the buildings would be located [83]. The building Reynolds number based on H and Uref is 123,724 while the Reynolds number based on the passage width between the buildings (w = 0.028 m) and Uref is 24,745. These values are well above the threshold of 11,000 for Reynolds number independent results [84]. The measurements are made for an approach-flow wind direction of 0°. The mean streamwise velocity (U) and turbulence intensity (TI) are sampled along six vertical lines in the vertical centerplane (y/B = 0) at x/B = -2.9, -1.94, -0.97, 0, 0.97 and 1.94 (Fig. 2.1c).

Figure 2.1. (a) Photograph of reduced-scale 2×2 array of high-rise buildings in wind-tunnel test section; (b) Incident vertical profiles of dimensionless mean streamwise velocity (U/Uref) and turbulence intensity (TI/TIref) measured in wind tunnel; (c) Locations of six vertical sampling lines in the centerplane (y/B = 0).

2.2.2

CFD validation: computational domain and grid

The CFD simulations are performed at wind tunnel scale. The computational domain is based on the WT test section and is shown in Fig. 2.2a. The downstream domain length is 15H, according to best practice guidelines for CFD simulations of wind flow in urban areas [85, 86]. The upstream

Urban wind energy potential: Impact of building arrangement and height

length is 3H, which is smaller than that proposed by the best practice guidelines, to limit unintended streamwise gradients in the vertical inlet profiles [87, 88]. The computational grid consists of 5,464,450 hexahedral cells, see Fig. 2.2b, with 20 cells in the passage between the buildings. The maximum and average y* values are 76 and 40, respectively. This ensures that the center points of wall-adjacent cells are located in the logarithmic layer of the boundary layer, thus the standard wall functions can be employed for the near-wall treatment. The grid resolution is based on a grid-sensitivity analysis presented in Section 2.2.4. 2.2.3

CFD validation: other computational settings

The profiles of the mean wind speed, turbulent kinetic energy and turbulence dissipation rate at the domain inlet are specified using the measured incident vertical profiles of the mean velocity and turbulence intensity, shown in Fig. 2.1b. The turbulent kinetic energy k is calculated from U(z) and TI (z) using Eq. 2.1, where a is 1.5. The turbulence dissipation rate ɛ is given by Eq. 2.2, with κ, u*ABL and z0 representing the von Karman constant (= 0.42), the ABL friction velocity (= 0.55 m/s), and the aerodynamic roughness length (= 9×10-6 m). 2

𝑘(𝑧) = 𝑎(𝑇𝐼(𝑧)𝑢(𝑧)) 𝑢∗

𝐴𝐵𝐿 𝜀(𝑧) = 𝜅(𝑧+𝑧

(2.1) (2.2)

Figure 2.2. Perspective view of (a) computational domain and (b) computational grid at surfaces of the building models and part of the ground surface (5,464,450 cells).

Chapter 2

The standard wall functions are employed together with roughness modification on the ground surface [89]. The values of the roughness parameters, i.e., the sand-grain roughness height ks (m) and the roughness constant Cs, are determined using their consistency relationship with the aerodynamic roughness length z0 [87], (Eq. (2.3)): 𝑘𝑠 =

9.793𝑧0 𝐶𝑠

(2.3)

In this study, ks = 0.0007 m and Cs = 0.13 for the ground surface. The building walls have roughness ks = 0 and Cs = 0.5. Zero static gauge pressure is applied at the outlet plane. Symmetry conditions, i.e., zero normal velocity and zero normal gradients of all variables, are imposed on the top and lateral sides of the domain. The commercial CFD software ANSYS/Fluent v19.0 is used to solve the 3D RANS equations using the Linear Pressure–Strain (LPS) Reynolds Stress Model (RSM) turbulence model for closure. The RSM is selected based on a sensitivity analysis presented in Section 2.2.5. The iterative semi-implicit method for pressure-linked equations (SIMPLE) scheme is used to couple velocity and pressure [90]. Second-order discretization schemes are used for both the convection and viscous diffusion terms of the governing equations. Convergence is assumed to be obtained when the scaled residuals level off and reach a minimumof 10−5 for continuity, 10−8 for x, y, z momentum and k, 10−6 for ɛ, and 10−7 for the six Reynolds stress tensor components.

Figure 2.3. (a-c) Dimensionless mean streamwise velocity and (d-f) turbulence intensity along three lines in the vertical centerplane (y/B = 0) for coarse, medium and fine grid.

Urban wind energy potential: Impact of building arrangement and height 2.2.4

CFD validation: grid-sensitivity analysis

A grid-sensitivity analysis is conducted using three uniformly refined grids with a refinement factor of approximately 1.3. The coarse and fine grids have 2,642,648 cells and 13,113,308 cells, respectively. The total numbers of cells in the passage between the buildings are 15, 20, and 28 for the coarse, medium, and fine grids, respectively. Fig. 2.3 shows the dimensionless mean streamwise velocity (U/Uref) and the turbulence intensity (TI/TIref) along three lines in the vertical centerplane (x/B = -1.94, 0, 1.94), where Uref and TIref are 13.4 m/s and 8%, respectively. The results show a very limited dependence of U/Uref and TI/TIref on the grid resolution along the different lines. This is especially the case for the medium and fine grids. The maximum average absolute deviations between the coarse and medium grids along the lines are 1.0% and 8.3% for U/Uref and TI/TIref, respectively. This is about 0.6% and 1.2% between the medium and fine grids. Therefore, the medium grid is retained for the rest of the study. 2.2.5

CFD validation: turbulence model sensitivity analysis

The sensitivity of the simulation results to the turbulence model is investigated for five commonly utilized RANS turbulence models: the standard k-ε model (SKE) [91], the realizable kε model (RKE) [92], the renormalization group k-ε model (RNG) [93], the shear-stress transport kω model (SST) [94] and the RSM model [95]. It is often assumed that the RSM model can provide more accurate predictions than eddy-viscosity models (EVMs) as it accounts for anisotropy in turbulence, while being computationally cheaper compared to more complex approaches such as large eddy simulations (LES) [96, 97]. Figure 2.4 compares U/Uref by WT and CFD along the six sampling lines in Fig. 2.1c. In general, the agreement between the mean velocity predictions of all the models with the wind-tunnel measurements is acceptable for x/B < -0.97. However, for x/B ≥ -0.97, the predictions of the RSM provide the best agreement with the experimental data, with an average absolute deviation of 4%. Fig. 2.5 compares TI/TIref by WT and CFD. The results of the SKE, RKE, and RNG models obviously show the worst predictions of TI. The results indicate that RSM could successfully reproduce the turbulence intensity profiles with average absolute deviations less than 16%. Based on the analysis of the velocity and TI profiles it can be concluded that the RSM provides the best agreement with the wind-tunnel measurements. Thus, the RSM is selected for the rest of the study.

Chapter 2

Figure 2.4. Comparison of simulated dimensionless mean streamwise velocity with wind-tunnel data at different streamwise positions along the lines in the vertical centerplane (y/B = 0). AD stands for the average absolute deviation along each line.

Urban wind energy potential: Impact of building arrangement and height

Figure 2.5. Comparison of simulated dimensionless turbulence intensity with wind-tunnel data at different streamwise positions along lines in the vertical centerplane (y/B = 0). AD stands for the average absolute deviation.

Chapter 2

2.3

List of cases

The present research investigates the wind energy potential for four identical full-scale highrise buildings that are square cylinders with height-to-width ratio H/B = 4.5 in a 2×2 array. The dimensions of each high-rise building are 20  20  90 m3 (B  L  H). Note that the values are simplified from a real compact urban environment of Central District, Hong Kong [98]. Table 2.2 presents the 14 test cases used to study the influence of the following three parameters: − − −

The passage width between the two upstream buildings (w): w varies from 0.15B to 0.75B. The streamwise distance between the upstream and downstream buildings (d): d varies from 0.3B to 0.6B. The height difference between the upstream and downstream buildings (ΔH = Hu – Hd): ΔH varies from -0.3H to 0.3H, while w and d are the same as the reference case.

For these cases only the arrangement and height of the upstream buildings are varied while the passage width between the two downstream buildings remains unchanged at 0.15B. Table 2.2. Details of the test cases. Case name

2.4 2.4.1

ΔH = Hu – Hd

Case Ref

0.15B

Case W1

0.30B

0.15B

Case W2

0.45B

0.15B

Case W3

0.60B

0.15B

Case W4

0.75B

0.15B

Case D1

0.75B

0.30B

Case D2

0.75B

0.45B

Case D3

0.75B

0.60B

Case H-3 Case H-2 Case H-1 Case H+1 Case H+2 Case H+3

0.15B 0.15B 0.15B 0.15B 0.15B 0.15B

-0.3H -0.2H -0.1H 0.1H 0.2H 0.3H

CFD simulations Computational domain and grid

The size of the computational domain for the full-scale models is 980 m × 1933 m × 540 m, i.e., 10.9H × 21.5H × 6H. The distances from the building surfaces to the top, inlet, lateral, and outlet boundaries of the domain are 5H, 5H, 5H, and 15H, respectively, where H = 90 m. The domain size is made according to the best practice guidelines for CFD simulations of wind flow in urban areas [86, 99]. The computational grid consists of 16,789,350 hexahedral cells. The grid is made based on a grid-sensitivity analysis, which will be presented in Section 2.4.3.

Urban wind energy potential: Impact of building arrangement and height 2.4.2

Other computational settings

At the inlet of the domain, neutral atmospheric boundary layer inflow profiles are imposed for mean wind speed (Eq. 2.4), turbulent kinetic energy (Eq. 2.5), and turbulence dissipation rate (Eq. 2.2). The simulations are performed for a reference wind speed at height h = 10 m of Uh = 5 m/s. The corresponding value of u*ABL is 0.88 m/s. 𝑈(𝑧) = 𝑘(𝑧) =

∗ 𝑢𝐴𝐵𝐿

𝜅 ∗ 𝑢𝐴𝐵𝐿

𝑙𝑛 (

𝑧+𝑧0 𝑧0

(2.4)

)

(2.5)

√𝐶𝜇

where κ is the von Karman constant (= 0.42) and Cμ is a model constant (= 0.09). The aerodynamic roughness length (z0) of the surrounding terrain is 1 m based on the updated Davenport-Wieringa roughness classification [86, 100]. The reference wind speed Uref at the building height (H = 90 m) is 9.4 m/s. The ground roughness is specified by a roughness height ks of 0.15 m and a roughness constant Cs of 8. Note that a value higher than 0.15 m was not possible due to the high near-ground grid resolution, with the center point of the wall-adjacent cell only at 0.15 m from the wall. A noslip boundary condition is used at the building surfaces with roughness ks = 0 and Cs = 0.5. Zero static gauge pressure is applied at the outlet plane. Symmetry conditions are imposed on the top and lateral sides of the domain. The CFD simulations are performed using the 3D steady RANS equations with the RSM turbulence model for the wind direction 0 (Fig. 2.2). The sensitivity of the results to the wind direction is discussed in Section 2.5.5. A converged solution is assumed to be obtained when all the scaled residuals have leveled off at about 10-6 for x, y, z momentum, continuity, 10−5 for k, ɛ and the six Reynolds stress tensor components. 2.4.3

Grid-sensitivity analysis

The analysis is performed for Case Ref using two additional grids, one coarser and one finer grid with a total of 10,402,160 and 46,005,060 cells, respectively. Coarsening and refining is performed in all directions with a linear factor of √2. The total numbers of cells in the passage between the buildings are 6, 10, and 12 for the coarse, medium and fine grids, respectively. Fig. 2.6a-c compares U/Uref along three vertical lines. The average deviation between the coarse and medium grid along the three profiles is 1.7%, whereas it is approximately 0.8% between the medium and fine grids. Fig. 2.6d-f compares TI/TIref along the same three lines. The average deviations between the coarse and the medium grid and between the medium and the fine grid are about 38% and 8.7%, respectively. The grid-convergence index (GCI) proposed by Roache [101] is used to estimate the error of U/Uref on the medium grid, given by Eq. 2.6: 𝑟 𝑃 [(𝑈𝑀𝑒𝑑𝑖𝑢𝑚 −𝑈𝐹𝑖𝑛𝑒 )/𝑈𝑟𝑒𝑓 ]

𝐺𝐶𝐼 = 𝐹𝑠 |

1−𝑟 𝑃

| × 100%

(2.6)

where Fs is the safety factor with the recommended value of 1.25 since three grids are considered, rP is the linear grid refinement factor of √2, P is the formal order of accuracy, which is 2 due to the use of second-order discretization schemes. The average GCI values of U/Uref along the three vertical profiles between the medium and fine grids for x/B = -1.5, x/B = 0, and x/B = 1.5 are 2.34%, 3.76%, and 3.03%, respectively. The average GCI for U/Uref along the three vertical profiles between the coarse and medium grids for x/B = -1.5, x/B = 0, and x/B = 1.5 are 9.07%, 9.46%, and 1.34%, respectively. Therefore, the medium grid is selected for the rest of the study.

Chapter 2

2.5

Results

The wind power density is calculated using Eq. 2.7: 1

3 𝑃𝐷 = 2 𝜌𝑉3𝐷

(2.7)

where  is the air density (= 1.225 kg/m3), and V3D is the 3D air velocity magnitude. For all analyses presented in Sections 2.5.1 – 2.5.3, it is assumed that a horizontal axis wind turbine (HAWT) is implemented between the buildings. Therefore, the mean streamwise velocity component (U) is substituted into Eq. (2.7) in order to compute the wind power density. The sensitivity of the wind power density calculations to the effective velocity component, which is set based on the wind turbine type/orientation, is investigated in Section 2.5.4.

Figure 2.6. Dimensionless mean streamwise velocity and (d-f) turbulence intensity along three lines in vertical centerplane (y/B = 0) for the coarse, medium and fine grid.

2.5.1

Impact of passage width between the two upstream buildings (w)

The impact of the passage width between the two upstream buildings is analyzed for w varying from 3 m (w = 0.15B) to 15 m (0.75B) while d and ΔH remain unchanged. Fig. 2.7 shows the contour plots of the dimensionless velocity magnitude V3D/Uref (with superimposed streamlines) in the vertical centerplane (y/B = 0), see Fig. 2.7a-e, and the horizontal plane at z/H = 0.97, see Fig. 2.7f-j, for different passage widths. Fig 2.8 shows the corresponding contour plots of the dimensionless power density (PD/PDref). Note that Uref and PDref are the incident wind velocity and power density at the reference height of 90 m, i.e., 9.4 m/s and 508.7 W/m2, respectively.

Urban wind energy potential: Impact of building arrangement and height

Figure 2.7. (a-e) Contour plots of dimensionless velocity magnitude (with superimposed streamlines) in vertical centerplane (y/B = 0) and (f-j) horizontal plane at z/H = 0.97 for different passage widths w.

Concerning the upstream passage, the following observations are made: For all the cases, a large stagnation area is formed upstream of the buildings. Because of the presence of this stagnant air, part of the oncoming wind flow is deviated over and around the upstream buildings, while only part of the flow is accelerated into the passage due to the contraction of the flow. For the reference case with the narrowest passage (w = 0.15B), the corner streams in the passage can no longer be distinguished as they are merged into a single passage jet. For the widest passage, however, the two corner streams with lesser interaction can be clearly observed. In this case, the highest velocity is not observed in the center of the jet (passage),

Chapter 2 but near the building corners inside the passage. For relatively low w values (w ≤ 0.30B), the flow is substantially accelerated in the passage (Fig. 2.7), and the regions with PD > 1.2 extend downward to an elevation of z/H = 0.2 (Fig. 2.8). The maximum wind power density occurs at the centerline of the narrowest passage, i.e. w = 0.15B. By increasing w to the moderate value of w = 0.45B, the range of the elevations, along which the flow is highly accelerated, gets limited to near the building roof (z/H > 0.8). In this case, the extent of the region with highest potential for wind energy harvesting reduces and the magnitude of the flow acceleration diminishes. For higher values of w > 0.45B, the maximum V3D/Uref reduces and approaches 1.0. This is accompanied by a significant drop in the extent of the region with highest potential for wind energy harvesting with increasing w for the upstream passage.

Figure 2.8. Contour plots of the dimensionless power density in (a-e) vertical centerplane (y/B = 0) and (f-j) horizontal plane at z/H = 0.97 for different passage widths w.

The following observations hold for the downstream passage: For all w values, a single jet is present in the downstream passage where the maximum air speed occurs in the center of the jet. The width of the upstream passage has a significant impact on the air flow pattern near the downstream passage opening, and consequently, on the air flow rate being forced through this passage. As w increases from 0.15B to 0.75B, higher velocities are experienced in the downstream passage. For Case W4 (w = 0.75B), the maximum velocity in the downstream passage is about 52% higher than that for the reference case (w = 0.15B). In order to quantify the impact of w on the portion of the flow that is forced through the downstream passage, Table 2.3 shows the dimensionless flow rates (DFR) through the different surfaces of a small control volume between the upstream and downstream buildings near the roof with a height of 0.1H. The control volume and the faces through which the flow rates are calculated are shown in Fig. 2.9. The DFR values are defined as the flow rate through the surface of the control volume divided by the flow rate of the upstream

Urban wind energy potential: Impact of building arrangement and height

surface (FRUS) of the considered control volume in Fig. 2.9, i.e., DFR = FR/ FRUS. For the reference case, which has the narrowest passage with w = 0.15, 86% of the incoming flow goes through the downstream surface, while 13% through the lateral surfaces and another 1% flows escapes vertically. An increase in w from 0.15B to 0.75B reduces the flow rate ratio through the downstream surface to 30%, while it leads to an increase in flow rate ratio through the lateral and horizontal surfaces by 56% and 13%, respectively. Note that by increasing w from 0.15B to 0.75B, the air flow rate through the downstream surface of the control volume increases by 61%, with respect to the case with w = 0.15B. For moderate to high values of w (w ≥ 0.60B), the maximum wind speed in the downstream passage is already higher than that in the upstream passage. The maximum power density in the downstream passage is PD/PDref = 1.2, corresponding to the widest upstream passage, w = 0.75B.

Figure 2.9. Schematic of the study regions: the upstream surface (US) and the downstream surface (DS); the lateral surfaces (LSs) and horizontal surfaces (HSs) between the upstream and downstream buildings for z/H from 0.9 to 1. Table 2.3. Flow rate and DFR through different surfaces of the control volume shown in Fig. 2.9 for different w/B. case name w/B FR(US) [m3/s] DFR(DS) DFR(LSs) DFR(HSs) Case Ref 0.15 203 0.86 0.13 0.01 Case W1 0.30 403 0.53 0.42 0.04 Case W2 0.45 581 0.43 0.49 0.09 Case W3 0.60 751 0.36 0.53 0.11 Case W4 0.75 926 0.30 0.56 0.13

Fig. 2.10 shows the profiles of PD/PDref along the horizontal line (y/B = 0) at z/H = 0.97 for different passage widths between the two upstream buildings. It can be seen that: In the upstream passage, PD/PDref decreases by increasing the passage width. The reference case with the narrowest passage has the highest PD near the passage entrance. The maximum value of PD/PDref for the widest passage (w = 0.75B) is 60% lower than that of the narrowest passage (w = 0.15B).

Chapter 2

In the downstream passage, the reverse trend is observed. Increasing w increases PD/PDref. The highest PD/PDref occurs for the widest upstream passage and near the passage entrance. The maximum PD/PDref in downstream passage shows an increase of 207% from the narrowest passage (w = 0.15B) to the widest one (w = 0.75B). Note that similar trends as presented for z/H = 0.97 in Fig. 2.10 for both the upstream and downstream passages are observed at lower elevations namely z/H = 0.9, 0.8, 0.7, as shown in Table 2.4. -

Figure 2.10. Profiles of the dimensionless power density along horizontal line at z/H = 0.97 for different passage widths w between upstream buildings. Table 2.4. Compariosion of dimensionless maximum power density along the centerline at heights z/H = 0.97, 0.9, 0.8 and 0.7 for different w/B.

w z/H = 0.97 z/H = 0.9 z/H = 0.8 z/H = 0.7

2.5.2

Max. PD/PDref in upstream passage 0.15B 0.3B 0.45B 0.6B 0.75B 2.01 1.62 1.30 1.00 0.81 2.21 1.66 1.24 0.91 0.70 1.89 1.51 1.06 0.73 0.54 1.66 1.33 0.89 0.58 0.41

Max. PD/PDref in downstream passage 0.15B 0.3B 0.45B 0.6B 0.75B 0.38 0.52 0.79 0.98 1.17 0.51 0.57 0.83 1.03 1.24 0.55 0.59 0.83 1.01 1.21 0.57 0.61 0.80 0.97 1.15

Impact of streamwise distance between upstream and downstream buildings (d)

The streamwise distance between upstream and downstream buildings is investigated for d varying from 3 m to 12 m, corresponding to d = 0.15B to 0.6B, respectively. Note that for all cases w = 0.75B and ΔH = 0.

Urban wind energy potential: Impact of building arrangement and height

Figure 2.11. Contour plots of (a-d) dimensionless velocity magnitude (with superimposed streamlines) in vertical centerplane (y/B = 0) and (e-h) horizontal plane at z/H = 0.97 for different streamwise distances d between the upstream and downstream buildings.

Figs. 2.11 and 2.12 show the contour plots of V3D/Uref (with superimposed streamlines) and PD/PDref in the vertical center plane (y/B = 0 in Fig. 2.11a-d and Fig. 2.12a-d) and the horizontal plane (z/H = 0.97 in Fig. 2.11e-h and Fig. 2.12e-h) for different values of d. It can be seen that: -

As d increases, the flow in the upstream passage becomes more accelerated up to V3D/Uref of 1.3. This is attributed to the upstream effect of the downstream buildings, which acts to slow down the approach flow, as demonstrated in earlier studies [50, 83, 102]. In addition, the region of highly accelerated flow in the passage is more extended in the streamwise, lateral

Chapter 2 and vertical directions. For the shortest d, i.e., 0.15B, the maximum V3D/Uref is 1 near the two side corners of the upstream building, where flow separation causes the creation of highspeed corner streams. This corresponds to PD/PDref = 0.9. For the highest value of d = 0.6B, the maximum V3D/Uref increases to 1.2 near the passage entrance of the upstream building, generating PD/PDref of 1.3. By increasing d, the region of low V3D/Uref near the windward facades of the downstream buildings is enlarged, and the flow is deviated over and around the downstream buildings. In order to better understand the air flow pattern near the downstream buildings as a function of d, Table 2.5 lists the dimensionless flow rate through different surfaces of the control volume between the upstream and downstream buildings shown in Fig. 2.9. For the smallest value of d, i.e., 0.15B, 30% of the incoming flow goes through the control volume, where wind energy harvesting is considered, while 13% exits vertically and another 56% flows through the lateral surfaces. By increasing d from 0.15B to 0.60B, the flow rate through the downstream surface reduces to 24%, which shows about 20% reduction with respect to the case with d = 0.15B. The DFR through the control volume reduces to 46% for the lateral surfaces, and increases to 30% for the horizontal surfaces. Note that the air flow rate through the upstream surface of the control volume changes by about 14% as d/B increases from 0.15 (Case W4) to 0.6 (Case D3). As d increases, the area of maximum flow velocity and power density in the downstream passage slightly decreases. For all cases, the maximum V3D/Uref of 1.2 in the passage appears near the passage entrance. For the smallest d of 0.15B in Fig. 2.11a, the regions of high wind energy potential (PD/PDref > 1.2) only appears near the passage entrance, and extends vertically downward until an elevation of z/H = 0.4. Increasing d also reduces the vertical extent of the region with high wind energy potential region the downstream passage.

Figure 2.12. Contour plots of the dimensionless power density for different streamwise distances d between the upstream and downstream buildings in (a-d) vertical centerplane (y/B = 0) and (e-h) horizontal plane at z/H = 0.97.

Urban wind energy potential: Impact of building arrangement and height

Table 2.5. Flow rate and DFR through different surfaces of the control volume shown in Fig. 2.9 for different d/B. case name d/B FR(US) [m3/s] DFR(DS) DFR(LSs) DFR(HSs) Case W4 0.15 926 0.30 0.56 0.13 Case D1 0.30 990 0.27 0.52 0.21 Case D2 0.45 1030 0.25 0.50 0.25 Case D3 0.60 1058 0.24 0.46 0.30

Figure 2.13 shows the profiles of PD/PDref along a horizontal line (centerline) at z/H = 0.97 for different values of d. It can be seen that: In the upstream passage, the maximum PD is highly sensitive to d as increasing d substantially enhances PD. For the largest value of d = 0.6B, the maximum PD/PDref is about 1.3, which is about 65% higher than that for the case with d = 0.15B. In the downstream passage, the maximum PD is weakly sensitive to d where increasing d from 0.15B to 0.6B leads to about 11% reduction in PD. This is in line with the observations of flow rate in the downstream passage, which is also found to be weakly sensitive to d (see Table 2.4).

Figure 2.13. Profiles of the dimensionless power density along centerline at z/H = 0.97 for different streamwise distances between the upstream and downstream buildings.

2.5.3

Impact of height difference between upstream and downstream buildings (ΔH)

To analyze the impact of the height difference between the upstream and downstream buildings (ΔH = Hu – Hd), the upstream building height Hu is varied from 60 m to 120 m, while the downstream building height Hd is retained at 90 m. This corresponds to ΔH between -0.3H and 0.3H. Note that for all cases both d and w are fixed at 0.15B, the same as the reference case. Figs. 2.14 and 2.15 show the contour plots of V3D/Uref (with superimposed streamlines) and PD/PDref in the vertical center plane at y/B = 0 for different values of ΔH. It can be seen that:

32 -

Chapter 2 For ΔH < 0 (corresponding to the cases where the upstream buildings are lower than the downstream ones), a high-velocity region is observed at the windward surfaces of the upstream and downstream building, where the flow is accelerated to V3D/Uref of 1.2, corresponding to PD/PDref of 1.7. As ΔH increases from -0.3 to -0.1, the vertical extent of the high-velocity region gets more limited and becomes negligible at ΔH = -0.1H. In addition, the streamwise extent of the high-velocity region along the roof of the downstream buildings also reduces. For ΔH = 0 (reference case) and ΔH > 0 (cases of upstream buildings higher than downstream buildings), the downstream buildings are located in the wake of the upstream buildings resulting in a significant reduction in the wind velocity in the downstream passage. In this case, V3D/Uref varies between 0.4 (ΔH = 0) and 0.75 (ΔH = 0.3H). This corresponds to PD/PDref values ranging from 0.3 to 0.7, respectively. However, a considerable high-velocity region is present in the upstream passage and above the roof of the upstream buildings, where the flow is accelerated to PD/PDref of 2.0. By increasing ΔH, the vertical extent of the area of highly-accelerated flow in the upstream passage grows.

Figure 2.14. Contour plots of the dimensionless velocity magnitude (with superimposed streamlines) in vertical centerplane (y/B = 0) for different heights of the upstream buildings.

Fig. 2.16 shows the profiles of the dimensionless power density along the centerline at (a) z/Hu = 0.97 and (b) z/Hd = 0.97 for different values of ΔH. Note that because the height of the upstream buildings varies for each case, the presented data are at different z/H. It can be seen that: In the upstream passage: For the cases with ΔH ≥ 0, a peak in PD/PDref can be observed. The case with equal heights of upstream and downstream buildings (ΔH= 0) presents the maximum wind power density, i.e., PD/PDref = 2.0. In the downstream passage: the reference case with ΔH = 0 reveals the lowest maximum in PD/PDref, as compared to the other cases. The cases with ΔH < 0 present high power densities in the downstream passage. The maximum PD/PDref value of 1.8 corresponds to ΔH = -0.2H,

Urban wind energy potential: Impact of building arrangement and height

which is around 364% larger than that of the reference case (ΔH = 0).

Figure 2.15. Contour plots of the dimensionless power density in vertical centerplane (y/B = 0) for different heights of the upstream buildings.

Figure 2.16. Profiles of dimensionless power density along the centerline at (a) z/H u = 0.97 and (b) z/Hd = 0.97 for different height difference between the upstream and downstream buildings.

Chapter 2

34 2.5.4

Impact of wind turbine type/orientation

The calculation of the wind power density is typically performed based on the streamwise component of the mean velocity, which is the component of velocity used to calculate the power density for the typical HAWTs [103]. This approach was used in Section 2.5.1 – 2.5.3. Moreover, many studies emphasized that vertical axis wind turbines (VAWTs) are highly advantageous over HAWTs primarily due to their potential power gain in vertically skewed airflows and omnidirectionality [19-21]. To further evaluate the wind energy potential considering other types of wind turbines, an analysis is performed in this section to compare the calculated wind power density for different types of turbines. Note that the velocity component is different for each turbine type as indicated in Fig. 2.17. a) b)

The typical HAWTs: The corresponding power density, termed as PDH, is calculated using the mean streamwise velocity component (U), as the normal component to the rotor plane. The typical Darrieus (lift-based) VAWTs: the corresponding power density, termed as PDVV, is calculated using the vector sum of the streamwise and lateral velocity components (√𝑈 2 + 𝑉 2 ). The horizontally-mounted Darrieus VAWTs: the corresponding power density, PDVH, is calculated using the vector sum of the streamwise and vertical velocity components (√𝑈 2 + 𝑊 2 ).

Figure 2.17. The velocity vector employed for the calculation of the wind power density for three types of wind turbines.

For the analysis, the cases with the highest power density in the upstream and downstream passages are selected as follows: Case Ref: the reference case, which has the narrowest upstream passage (w = 0.15B, d = 0.15B, ΔH = 0) Case W4: the case with the widest passage between the upstream buildings (w = 0.75B, d = 0.15B, ΔH = 0) Case D3: the case with the longest streamwise distance between the upstream and downstream buildings (w = 0.75B, d = 0.6B, ΔH = 0) Case H-2: the case for which the height of the upstream buildings is lower than the downstream buildings (w = 0.15B, d = 0.15B, ΔH = -0.2H)

Urban wind energy potential: Impact of building arrangement and height

Fig. 2.18 shows the profiles of the dimensionless power density for different turbine types along the centerline at z/H = 0.97 and 1.03, respectively, for the aforementioned cases. The lines of PDVV and PDH evidently overlap due to the absence of lateral velocity component along the centerline in this symmetric flow configuration. The maximum PDVH values at z/H = 0.97 are 20%, 36%, 8%, and 9% higher than PDH along the whole line for the Case Ref, Case W4, Case D3, and Case H-2, respectively. The maximum PDVH values at z/H = 1.03 are 76%, 21%, 37% and 45% higher than that PDH for four selected cases, respectively. This indicates the remarkable contribution of the vertical velocity component, which is clearly advantageous for wind energy harvesting. Based on the presented analysis, the horizontally-mounted Darrieus VAWTs are to be preferred as the best turbine type/orientation for wind energy harvesting in the passage between the high-rise building arrays near the roof as well as along the roof for the normal wind direction of 0°.

Figure 2.18. Profiles of dimensionless power density for different turbine types, i.e. HAWT (PDH), VAWT (PDVV), and horizontally-aligned VAWT (PDVH), along horizontal lines at (a-d) z/H = 0.97 and (e-h) z/H = 1.03 for four selected cases: Case Ref, Case W4, Case D3 and Case H-2.

Chapter 2

36 2.5.5

Impact of wind direction

Previous studies have shown the significant influences of wind direction and building arrangement on the wind flow patterns around buildings [36, 37, 61]. Thus, in this section, the impact of wind direction on the wind energy potential is investigated. The analysis is performed for three wind directions (0°, 22.5°, 45°). The simulations are performed for the four cases with the highest power densities, i.e., Case Ref, Case W4, Case D3, and Case H-2, see Section 2.5.4. Fig. 2.19 shows the contour plots of the dimensionless power density in the horizontal plane at z/H = 0.97 for the different wind directions for the selected cases, see Table 2.2. The wind power density is highly sensitive to the wind direction. For wind direction 22.5°, the wind flow tends to be more tilted to one side and hard to pass through the passage due to the oblique airflow direction, which causes comparatively low PD/PDref values in the upstream and downstream passages, with respect to zero wind direction. For wind direction 45°, a substantial decrease in PD/PDref along the passage between the buildings is observed.

Figure 2.19. Contour plots of dimensionless power density in horizontal plane at z/H = 0.97 for different wind directions (0°, 22.5°, and 45°) for selected cases: (a, e, i) Case Ref, (b, f, j) Case W4, (c, g, k) Case D3 and (d, h, l) Case H-2.

Urban wind energy potential: Impact of building arrangement and height

Fig. 2.20 shows the profiles of the dimensionless power density for different wind directions along the centerline at z/H = 0.97 and 1.03 for the aforementioned cases. Overall, the normal wind direction of 0° has the highest maximum PD/PDref for the four selected cases. For θ = 22.5°, the maximum PD/PDref values are 34%, 38%, 36% and 12% lower than that with θ = 0° for the four selected cases at z/H = 0.97, respectively. For θ = 45°, this decreases by 56%, 30%, 47%, and 33% at z/H = 0.97, respectively. In addition, the maximum PD/PDref results for wind direction 45° are 33%, 17%, and 25% lower than those of 22.5° for Case Ref, Case D3 and Case H-2 at z/H = 1.03, respectively. The wind direction of 45° produces the highest maximum PD/PDref along the rooftop for four selected cases. This is caused by the much lower wind blockage by the building and the reduced flow separation bubble on the roof and corners at an oblique wind direction of 45°, facilitating the flow accelerations on the building rooftops.

Figure 2.20. Profiles of dimensionless power density along horizontal lines at (a-d) z/H = 0.97 and (e-h) z/H = 1.03 for different wind directions (0°, 22.5°, and 45°) for selected cases: Case Ref, Case W4, Case D3 and Case H-2.

2.6

Limitations of the study

The limitations of this study are as follows:

Chapter 2

(1)

The analysis is focused on an isolated array of buildings and the impact of surrounding buildings in urban settings is not considered in the present study. Future investigations should focus on the assessment of wind energy potential in generic and real urban configurations. A full calculation of the wind power density should consider the statistics of the annual speed and wind direction distribution and the fact that the turbines will only operate within a certain range of wind speeds, between the so-called cut-in and cut-off wind speed. The varied wind conditions should be taken into account to facilitate the selection of the most appropriate small wind turbines for the utilization of urban wind power. Furthermore, the impact of the turbulence intensity, which influences the lifetime of the turbines is not considered in the present analysis and is recommended for future study. In this study, the simulations are performed for buildings with smooth and simple facades. Earlier studies have shown that façade geometrical details such as building balconies can significantly influence the near-building airflow patterns [104-107]. Future work will take into account the impact of façade geometrical details on the available wind energy potential around buildings.

(2)

(3)

2.7

Conclusions

High-fidelity CFD simulations are performed to investigate the effect of the building arrangement and height for a 2×2 array of high-rise buildings in close proximity. The following parameters are studied: (i) the passage width between the two upstream buildings (w), (ii) the streamwise distance between the upstream and the downstream buildings (d), (iii) the height difference between the upstream and the downstream buildings (ΔH) and (iv) the type/orientation of the wind turbine for wind energy harvesting. The analysis focuses on the wind speed and the wind power density along the upstream and the downstream building passages as well as on the building roofs. 



The main conclusions are summarized below: A wider passage width of the upstream passage (higher w) leads to a decrease in the wind speed in this passage. A maximum reduction in PD/PDref of 60% is observed in the upstream passage at z/H = 0.97 when w increases from 0.15B to 0.75B. Conversely, increasing w from 0.15B to 0.75B, results in a notable increase of 207% in PD/PDref in the downstream passage at z/H = 0.97. The longest d of 0.6B achieves an increase in the maximum power density up to 65% compared with that for d = 0.15B in the upstream passage at z/H = 0.97. Increasing d only leads to minor reductions in PD with the differences less than 11% in the downstream passage at z/H = 0.97. For ΔH ≥ 0, the flow in the upstream passage at z/Hu = 0.97 and over the upstream rooftop is highly accelerated to PD/PDref of 2.0. The reference case with equal building height, ΔH = 0, presents the highest wind power density of 2.0 in the upstream passage. For ΔH < 0, the flow in the downstream passage at z/Hd = 0.97 and over the downstream rooftop is accelerated to PD/PDref of 1.7. The case with ΔH = -0.2H provides the maximum PD/PDref of 1.8, that is nearly 364% higher than the reference case (ΔH = 0). The power densities calculated for a horizontally-mounted VAWT are higher than those of a HAWT and a typical VAWT by approximately 11% – 37% at z/Hd = 0.97 for the normal wind direction of 0°. This proposes significant contribution of the vertical velocity component, concluding that the horizontally-mounted VAWT is the best option for wind energy harvesting in the passage between the buildings as well as along the rooftop.

Urban wind energy potential: Impacts of building corner modifications This chapter is submitted to a peer-reviewed scientific journal:

CFD assessment of wind energy potential for generic high-rise buildings: Impacts of building corner modifications Juan, Y.H., Rezaeiha A., Montazeri H., Blocken, B., Yang, A.S. Abstract: Urban wind energy can contribute to achieve sustainable development goals. Building corner modifications are considered useful for enhancing the aerodynamic performance of a single tall building; however, such studies targeted at urban wind energy for compact high-rise building complexes remain scarce. Therefore, a comprehensive CFD analysis of the impacts of building corner modifications on the urban wind energy potential for a 2 × 2 array of high-rise buildings placed in close proximity is performed. The following impacts are analyzed: (i) the building corner shape, including sharp, chamfered, and rounded, (ii) the chamfer length (l), (iii) the corner radius (r), (iv) the impact of wind turbine type and orientation, and (v) the wind direction. The analysis is based on 3D steady Reynolds-averaged Naiver-Stokes (RANS) simulations validated with wind-tunnel experiments. The results reveal the notable influence of corner modifications on the mean wind speed, mean wind power density and turbulence intensity along the passage between the buildings, above the building roofs, and beside the buildings. Rounded corners are the most favorable building corner shape yielding a maximum of 365% higher wind power density above the roofs, as compared to sharp corners, with a comparatively noticeable reduction in turbulence intensity under both normal and oblique wind directions. By increasing the chamfer length or corner radius, wind power density can be dramatically enhanced beside the high-rise buildings. Among three common types of wind turbines studied, the horizontally-mounted vertical axis wind turbines (VAWTs) show the best performance of wind energy harvesting along the passages between the buildings with rounded corners as well as above the roofs, while the typical VAWT yields the highest PD beside the round-corner buildings. Keywords: Urban wind energy; urban morphology; renewable energy; wind power density; turbulence intensity; corner modification

Chapter 3

40 Nomenclature ɛ κ ρ B Cs H H/W k ks Iref l L U u*ABL Uref r V V3D Vref W w y* z0 ABL CH FN HAWT PD PDref RC RN RSM SQ TI TIref URAR VAWT

3.1

Turbulence dissipation rate [m2/s3] von Karman constant Air density [kg/m3] Building breadth [m] Roughness constant Building height [m] Building-height-to-street-width ratio Turbulent kinetic energy [m2/s2] Sand-grain roughness height [m] Reference turbulence intensity [%] Chamfer length [m] Building length [m] Mean streamwise velocity [m/s] Friction velocity [m/s] Mean streamwise velocity at reference height [m/s] Corner radius [m] Mean lateral velocity [m/s] Mean velocity magnitude [m/s] Mean velocity magnitude at reference height [m/s] Mean vertical velocity component [m/s] Street passage width [m] Dimensionless wall distance Aerodynamic roughness length [m] Atmospheric boundary layer Chamfered shape Finned shape Horizontal axis wind turbine Power density [W/m2] Power density at reference height [W/m2] Recessed shape Rounded shape Reynolds stress model Square shape Turbulence intensity [%] Turbulence intensity at reference height [%] Unacceptable reference-turbulence-intensity area ratio [%] Vertical axis wind turbine

Introduction

Wind energy has become one of the fastest-growing renewable energy resources, accounting for 22 % of the renewable energy generation worldwide [108]. However, most of this wind energy is harvested outside urban areas. The ability and potential to harness wind power in urban areas are progressing but still remain marginal [4, 45, 53]. The inevitable challenges due to the complexity of large roughness features, especially from high-rise buildings, generally cause lower mean wind speed, higher turbulence intensities and larger changes in wind direction. Previous studies [3, 47, 60] notes that passages between high-rise buildings as well as their roofs can be

Urban wind energy potential: Impacts of building corner modifications

potential locations for wind energy harvesting due to the local high wind speeds [9, 14, 17, 62, 66, 109]. The available wind energy potential in such locations can be significantly improved by varying the morphological parameters, such as urban density [37, 110], building arrangement [40, 56, 69, 111], roof shape [36, 37, 59, 72], building geometry [52] and corner shapes [17, 35, 65]. The building corner modifications are one of the important aspects to enhance the aerodynamic performance of high-rise buildings. To preserve the overall building morphology, it is possible to only change the local shapes in featured geometries of buildings and roofs. Most studies on building corner modifications were conducted to investigate their impact on aerodynamic characteristics such as wind loads on buildings [34, 112, 113], pedestrian wind comfort [33, 42, 114, 115], aeroelastic instabilities of tall buildings [113, 116] and wind energy [17, 35, 110]. Table 3.1 provides an overview of wind-tunnel and computational fluid dynamics (CFD) studies on the impact of building corner shape on the aerodynamic performance of buildings. The table indicates the urban configuration of study (full scale or reduced scale, the numbers and geometries of buildings in the array), the targeted application (pedestrian wind comfort, urban wind energy, wind loads and/or aeroelastic instabilities), the method of the study, the studied parameter(s), the type of corner shape (sharp, chamfered, rounded, recessed, and finned shape as shown in Fig. 3.1), and the performance indicator under study.

Figure 3.1. Schematic of commonly-used building corner modifications. Table 3.1. An overview of studies on building corner modifications. Publication

Urban configuration

App licati on WE

Metho d

Studied parameter

Corner shape

CFD

CS, WD, UD

SQ, RN

Performa nce indicator V, PD, TI

Juan et al. (2021) [110] Thordal et al. (2020) [117] Zhang et al. (2020) [115] Mittal et al. (2019) [114] Li et al. (2018) [112] Xu et al. (2017) [33] Elshaer et al. (2017) [34] Aly and Bresowar (2016) [118] Yang et al. (2016) [17]

FS/ 66 cubic array RS/ a cuboid

CFD, WT CFD

CS, WD

CH, RN

V, Cp

CS, WD

SQ, CH, RN

V, A

CFD

CS, WD

V, A

CS, WD

RS/ a cuboid

PW C WL

CFD

CS, WD

RS/ a cuboid

CFD

BG, CS

SQ, CH, RN, RC SQ, CH, RN, RC SQ, CH, RN, RC SQ, CH, RN, RC SQ, CH, RN, RC

FS/ urban area

CFD

SQ, RN

V, PD

RS/ a cuboid in 55 cube array RS/ a cuboid RS/ a cuboid RS/ a cuboid

PW C PW C WL

C, CM, CD, Cp V, A CD, CL CD, CL

Chapter 3

Toja-Silva et RS/ a cuboid WE CFD BG, CS SQ, RN, FN V, TI al. (2015) [35] Carassale et al. RS/ a cuboid WL WT CS, TURB, SQ, RN CD, CL, Cp, (2014) [119] WD St Tamura et al. RS/ a cuboid WL CFD CS SQ, CH, Hz, CM, (2010) [120] RN, RC Cp, V Tamura and RS/ a cuboid WL WT CS, TURB SQ, CH, RN CD, CL Miyagi (1999) [32] Tamura and RS/ a cuboid WL WT CS, TURB SQ, CH, RN CD, CL, Cp Miyagi (1998) [121] Tamura et al. RS/ a cuboid WL CFD CS SQ, CH, RN CD, CL, Cp, (1998) [121] St Kawai (1998) RS/ a cuboid AI WT CS CH, RN, RC V, η [116] Uematsu et al. RS/ a cuboid PW WT CS, WD SQ, CH, V (1992) [122] C RN, RC Jamieson et al. PW WT CS, WD SQ, CH V RS/ 55 cubic (1992) [123] C array Stathopoulos RS/ a cuboid PW WT BG, CS, WD CH V, Com (1985) [42] C Note: Column 2: FS: full scale; RS: reduced scale. Column 3: PWC: pedestrian wind comfort; WL: wind loads on buildings; WE: wind energy; AI: aeroelastic instabilities of tall buildings. Column 4: WT: wind-tunnel experiment; CFD: computational fluid dynamics. Column 5: SQ: square; CH: chamfered; RN: rounded; RC: recessed; FN: finned. Column 6: BG: building geometry (including building height, roof geometry, etc.); CS: corner shape; TURB: impact of turbulence models; WD: wind direction; UD: urban density. Column 7: A: areaaveraged amplification factor; C: wind force coefficients; CM: moment coefficients; CD: wind drag force coefficient; CL: fluctuating lift coefficients; Cp: pressure coefficient; Com: comfort parameter; Hz: natural frequency of response; PD: power density; St: Strouhal number; TI: turbulence intensity; V: wind speed; η: damping ratio.

Although earlier studies have shown that the use of chamfered, rounded and recessed corner shapes can effectively decrease high wind speed regions at pedestrian level and wake lengths behind the buildings [123] as well as lessen the streamwise, lateral and uplift forces on buildings [122], the impact of building corner modifications on urban wind energy harvesting has been investigated only in a few studies [17, 35, 59]. Toja-Silva et al. [35, 59] analyzed the wind flow around an isolated building with a building height (H) to building length (L) ratio (H/L) = 2 to identify regions of maximum mean wind velocities and minimum turbulence intensities. They proposed adopting curved shapes at the roof edges to improve the wind energy potential. Yang et al. [17] investigated the impact of two corner modifications (i.e., sharp and round shapes) on wind energy potential for a building in a dense urban area. The results showed that rounded roof edges can result in higher wind power density (up to 86.5%) and lower turbulence intensities (down to 494%). Note that the above-mentioned studies were conducted for isolated buildings or low-rise urban areas. In addition, the focus in these studies was on the roof edge shape. To the best knowledge of the authors, a comprehensive analysis of the impact of building corner modifications on the wind energy potential for groups of multiple high-rise buildings has not yet been performed. Therefore, the present study aims to provide a fundamental understanding of the impact of building corner modifications on the urban wind energy potential for a 2×2 array of high-rise buildings placed in close proximity, where the ratio of the building height (H) to the street width (w) ratio, (H/w) = 30. In this perspective, the impact of the following parameters is analyzed:

Urban wind energy potential: Impacts of building corner modifications

(i) building corner shape, including sharp, chamfered, and rounded; (ii) chamfered length (l); (iii) corner radius (r); (iv) wind turbine type and orientation; (v) wind direction. The organization of this paper is as follows: CFD validation study is presented in Section 3.2. Section 3.3 presents a description of the cases studied. Section 3.4 outlines the computational settings and grid-sensitivity analysis. The results are presented in Section 3.5. The limitations of the study are presented in Section 3.6. Section 3.7 summarizes the main conclusions.

3.2 3.2.1

CFD validation study Description of the experiment

Measurements of the 3-component instantaneous velocity are performed using turbulent flow instrumentation (TFI) Cobra probe [82] for a reduced-scale model of four generic high-rise parallel buildings placed in close proximity, see Fig. 3.2a. The measurements were performed in an open-circuit small atmospheric boundary layer wind tunnel. The wind tunnel was 13 m long and had a test section of 0.5 m width and 0.65 m height. Table 3.2 presents some main characteristics of the reduced-scale model and the measurement equipment. In the experiments, the blockage ratio is 2.95%.

Figure 3.2. Validation study: (a) schematic of the building geometry and (b) dimensionless measured incident profiles of mean streamwise velocity and turbulent intensity.

Wind tunnel model

Velocity measurement

Table 3.2. Details of the wind-tunnel experiment. Scale 1:643 Length, L 0.031 (20 m in full scale) Breadth, B 0.031 m (20 m in full scale) Height, H 0.14 (90 m in full scale) Street passage width, w [m] 0.028 (18 m in full scale) Device TFI Cobra probe [82]

Chapter 3

44 Type Frequency Angular range Accuracy

3-component velocity measurement 600 Hz ±45° acceptance cone wind speed: ± 0.5 m/s wind direction: ± 1° (in the pitch-yaw axes)

Fig. 3.2b shows the measured incident vertical profiles of the dimensionless mean streamwise velocity component (U/Uref) and turbulence intensity (TI/TIref). The incident profiles are those measured in the empty wind tunnel at the location where the buildings will be placed [83]. Note that the reference wind velocity, Uref, and the turbulence intensity, TIref, are taken at building height, yielding values of 13.4 m/s and 8%. The building Reynolds number is 24,745 based on the street passage width (0.028 m) and the reference wind speed of 13.4 m/s, which is well above the critical value of 11,000, for which the flow around a building can be considered as Reynolds number independent [84]. 3.2.2

CFD validation: computational settings and results

The upstream and downstream lengths of the computational domain are 3H and 15H, respectively, according to the best practice guidelines for CFD simulations of wind flow in urban areas [86, 124]. Note that the upstream domain length is smaller than the value proposed by the best practice guidelines, i.e., 5H, to limit unintended changes of streamwise gradients in the vertical approach-flow profiles [49, 87, 88]. The lateral length and the height of the computational domain are chosen equal to the cross-section of wind-tunnel resulting in a blockage ratio of 2.95%, which does not exceed the maximum value recommended by the aforementioned CFD guidelines. The computational grid consists of 5,464,450 hexahedral cells with 20 cells along the passage between the buildings. The average and maximum y* values are 40 and 76, respectively. The grid resolution can ensure that the center points of wall-adjacent cells are located in the logarithmic layer of the boundary layer for the near-wall treatment employing the near-wall treatment. The boundary conditions at the domain inlet are based on the measured incident vertical profiles of mean streamwise velocity, as shown in Fig. 3.2b. The turbulent kinetic energy k is calculated from the measured incident vertical profiles of U(z) and TI (z) using Eq. (3.1). The turbulence dissipation rate ɛ is given by Eq. (3.2) as below: 2

𝑘(𝑧) = 1.5(𝑢(𝑧)𝑇𝐼(𝑧)) 𝑢∗

𝐴𝐵𝐿 𝜀(𝑧) = 𝜅(𝑧+𝑧

(3.1) (3.2)

where κ, u*ABL and z0 represent the von Karman constant (= 0.42), the ABL friction velocity (= 0.55 m/s) and the aerodynamic roughness length (9×10-6 m at reduced scale), respectively. The standard wall functions [89] with roughness modification are used on the ground surface. The roughness parameters of the sand-grain roughness height ks (m) and the roughness constant Cs are determined using their consistency relationship with the aerodynamic roughness length z0 [87], (Eq. (3.3)): 𝑘𝑠 =

9.793𝑧0 𝐶𝑠

(3.3)

Urban wind energy potential: Impacts of building corner modifications

In this study, ks = 0.0007 m and Cs = 0.13 are employed for the ground surface. The ground and building walls are modeled as no-slip walls. Zero static gauge pressure is employed at the outlet boundary. Symmetry conditions are imposed on the top and lateral sides of the computational domain. The commercial CFD software ANSYS/Fluent v19.0 is used to perform the simulations. The 3D Reynolds-averaged Navier–Stokes (RANS) simulations are performed using the Linear Pressure–Strain (LPS) Reynolds Stress Model (RSM) turbulence model, which is selected based on a sensitivity analysis for different turbulence models. Detailed information about this sensitivity analysis is presented in Ref. [111]. The SIMPLE algorithm is adopted to couple velocity and pressure [90]. Second-order discretization schemes are employed for both the convection terms and viscous terms of the governing equations. Convergence is obtained when the scaled residuals level off and reach a minimum of 10−5 for continuity, 10−8 for x, y, z momentum, and k, 10−6 for ɛ, and 10−7 for the six Reynolds stress tensor components. Fig. 3.3 compares the simulated and measured U/Uref and TI/TIref values along three lines in the vertical center plane (y/B = 0) at x/B = -0.97, 0 and 0.97. The CFD results are in good agreement with the experimental data where the average absolute deviations for U/Uref and TI/TIref are less than 5% and 16%, respectively.

Figure 3.3. Validation study: comparison of (a-c) dimensionless mean streamwise velocity component and (df) turbulence intensity by CFD and wind-tunnel experiments along three lines in the vertical centerplane (y/B = 0) at x/B = -0.97, 0 and 0.97.

Chapter 3

3.3

Test cases

A 2 × 2 array of high-rise buildings placed in close proximity is studied. The four buildings are identical with length (L) and breadth (B) equal to 20 m and height (H) equal to 90 m. The street passage width (w) between the buildings is 3 m. Details of the building corner modifications for the different test cases are given in Table 3.3. Note that the building corner modifications are applied to all the edges of the facades and roofs of the buildings. Fig. 3.4 shows the different shapes of the building corner modifications: (a) sharp, (b) chamfered, and (c) rounded. Note that the values are normalized with the building length, B. The building with sharp edges is selected as the reference case.

Figure 3.4. Schematic of the buildings with different corner shapes: (a) sharp, (b) chamfered and (c) rounded. Table 3.3. Details of the test cases. Corner modification

Case ID

Building length, B

Chamfer length, l

Corner radius, r

Ref. case

20 m

Sharp corner

Chamfered corner

Case C1 Case C2 Case C3

0.05B 20 m

Case C4 Rounded corner

0.10B 0.15B 0.20B

Case R1 Case R2 Case R3 Case R4

0.05B 20 m

0.10B 0.15B 0.20B

Urban wind energy potential: Impacts of building corner modifications

3.4 3.4.1

CFD simulations Computational domain and grid

The full-scale buildings are placed in a computational domain with dimensions 980 m × 1933 m × 540 m. The distances from the building surfaces to the upstream, lateral, downstream and top domain faces are 5H, 5H, 15H and 6H, respectively, which is in line with the best practice guidelines [86, 99]. The computational grids consist of hexahedral and prismatic cells. A nonconformal grid is used with three subdomains (i.e., Ω1, Ω2, and Ω3) discretized by a 1:2 grid refinement ratio, as suggested by Iousef et al. [125]. The inner subdomain Ω1 consists of cubic cells with a minimum length of H/300, which is extended to H/3 away from the building surfaces for the region where high-velocity gradients expected to occur. Subdomain Ω2 primarily consists of hexahedral cells with a minimum length of H/150, extended up to a distance of 1H away from the windward, leeward, side, and top building surfaces. Subdomain Ω3 is discretized by hexahedral cells with a minimum length of H/75. The total number of cells ranges from 16 to 25 million for different cases. Similar grid topology and resolution are used for all the cases. The minimum number of cells across the passage between the buildings is 10, while the distances from the center points of the wall-adjacent cells to the walls are around 0.3 m. The grid resolution is selected based on a grid-sensitivity analysis, which will be presented in Section 3.4.3. 3.4.2

Computational settings and parameters

Neutral atmospheric boundary layer inflow profiles of mean wind speed, turbulent kinetic energy and turbulence dissipation rate (Eq. (3.2)) [126] are imposed at the inlet of the domain, see Eq. 3.4 - 3.6: U(z) =

u*ABL κ

𝑘(𝑧) =

ln (

∗ 𝑢𝐴𝐵𝐿

√𝐶𝜇

z+z0 z0

)

(3.4) (3.5)

where u*ABL and κ are the friction velocity (= 0.88 m/s) and the von Karman constant (= 0.42), respectively, and Cμ = 0.09. An aerodynamic roughness length (z0) of 1 m is used to implicitly model the surrounding terrain as fully and regularly covered with similar-size high-rise buildings in a densely built-up area based on the updated Davenport-Wieringa roughness classification [100]. The reference wind speed (Uref) at the building height (90 m) is 9.4 m/s. The ground roughness is specified by a roughness height ks of 0.15 m and a roughness constant of Cs = 8. Note that kS higher than 0.15 m is not possible here because of the high grid resolution near the ground surface. The value of kS is limited to the distance of the center point of the wall-adjacent cell to the wall, which is 0.15 m in this case. A no-slip boundary conditions is implemented for the building surfaces with ks = 0 and Cs = 0.5. Zero static gauge pressure is specified at the domain outlet. The top and lateral surfaces of the computational domain are treated as symmetry planes. The solver settings are identical to those used in the CFD validation study (see Section 3.2.2).

Chapter 3

Figure 3.5. (a) Coarse (11.3 million cells), (b) medium (18.0 million cells), and (c) fine grids (47.8 million cells) for Case R3. Enlarged views show the grid near the ground and roof.

3.4.3

Grid-sensitivity analysis

A grid-sensitivity analysis is performed using three uniformly refined grids with a refinement factor of √2. The analysis is performed for case R3 (r = 0.15B). The rounded corner shape is selected. The three grids are shown in Fig. 3.5. The number of cells for the coarse, medium and fine grids is 11,277,645, 18,023,086 and 47,785,776, respectively. Fig. 3.6 compares U/Uref and TI/TIref along the vertical centerline (y/B = 0) at z/H = 0.93 (just below the roof) for the three grids. The results indicate that the average deviation of U/Uref and TI/TIref between the coarse and

Urban wind energy potential: Impacts of building corner modifications

medium grids is 7%, while it is less than 1% between the base and the fine grids. The gridconvergence index (GCI) proposed by Roache [101] is used to estimate the errors of U/Uref on the medium grid, given by Eq. 3.6: 𝑅𝑃 [(𝑈𝐵𝑎𝑠𝑒 −𝑈𝐹𝑖𝑛𝑒 )/𝑈𝑟𝑒𝑓 ]

𝐺𝐶𝐼 = 𝐹𝑠 |

1−𝑅𝑃

| × 100%

(3.6)

here Fs is the safety factor of 1.25 as the recommended value when more than three grids are considered, RP is the linear grid refinement factor of √2, P is the formal order of the accuracy (= 2 based on the use of second-order discretization schemes). The average GCI of U/Uref and TI/TIref along the horizontal centerline at z/H = 0.93 between the medium and fine grids is 3.3% and 8%, respectively. Therefore, the medium grid is considered for the rest of this study.

Figure 3.6. Grid-sensitivity analysis: (a) dimensionless mean streamwise velocity component and (b) turbulence intensity along centerline at z/H = 0.93.

Chapter 3

3.5

Results

To investigate the impact of the building corner modifications presented in Section 3.5.1, the results of the case with the sharp corners are compared with those of the chamfered corner (l = 0.15B) and rounded corner (r = 0.15B). The impact of chamfer length, l, and corner radius, r, are investigated in Section 3.5.2 and 3.5.3, respectively. The wind power density is calculated using the streamwise velocity component, corresponding to the relevant component for horizontal axis wind turbines (HAWTs). An analysis is presented in Section 3.5.4 to investigate the potential of vertical axis wind turbines (VAWTs) mounted in the typical vertical or horizontal orientations. Consequently, for the VAWTs also other velocity components are considered for the calculation of the wind power density. Note that the results presented in subsections 3.5.1 to 3.5.4 correspond to 0° wind direction, i.e., parallel to the passage between the buildings. An analysis is presented in Section 3.5.5 to investigate the sensitivity of the findings to the wind direction. 3.5.1

Impact of building corner shape

Figure 3.7 shows the distribution of the dimensionless 3D velocity magnitude V3D/Uref (with superimposed streamlines) for the different building corner shapes in three planes: (i) the vertical plane at y/B = 0 (midplane), (ii) the horizontal plane at z/H = 1.03 (just above the roof), and (iii) the vertical plane at y/B = 1.1 (besides the buildings). Note that Uref is the incident mean wind velocity at the reference building height of 90 m, i.e., 9.4 m/s. Large differences are revealed between the three cases: -

Along the passage between the buildings at y/B = 0 (Fig. 3.7a, 3.7d, and 3.7g): For the sharp corner case, the airflow is accelerated up to V3D/Uref = 1.2 near the entry of the upstream passage and above the rooftop and reduces to V3D/Uref ≤ 0.6 in the downstream passage. For the chamfered corner (Fig. 3.7d), the mean wind speed increases along the passage between the upstream buildings, where the maximum value reaches V3D/Uref = 1.5. The highest speed region occurs near the chamfered corners of the upstream buildings and extends downstream beyond x/B = -0.5. V3D/Uref along the downstream passage decreases to about 1. For the case with the rounded corner (Fig. 3.7g), the flow velocity in the upstream and downstream passages is the highest among the three corner shapes with a maximum of 1.6 and 1.3, respectively. The flow acceleration (V3D/Uref > 1.2) extends downward along the upstream passage. Afterward, a high-speed flow region also appears in the downwind passage. Above the roofs at z/H=1.03 (Fig. 3.7b, 3.7e, and 3.7h): The wind passes over the sharp corners on the windward side of building roofs, airflow separation is formed with a visible low-velocity recirculation zone (V3D/Uref < 0.4) downstream of the leading edge. For the configurations with chamfered and rounded corners, a very different flow pattern is observed, and the massive low-velocity region is eliminated due to the more aerodynamic roof edge shapes. Beside the buildings in the vertical planes at y/B = 1.1 (Fig. 3.7c, 3.7f, and 3.7i): The shear layer separates from the front sharp corners and does not seem to reattach to the building surfaces, forming a large recirculating zone with low mean wind speed (V3D/Uref < 0.5) in Fig. 3.7c. In contrast, for the chamfered and rounded corners in Fig. 3.7f and 3.7i, high-velocity regions (V3D/Uref > 1.2) can be seen near the front edges of the upstream buildings and the rear edges of the downstream buildings.

Urban wind energy potential: Impacts of building corner modifications

Figure 3.7. Contours of dimensionless velocity magnitude (with streamlines superimposed) for case with sharp corners (Ref. Case) in (a) a vertical plane at y/B = 0 (midplane), (b) a horizontal plane at z/H = 1.03 (just above the roof), (c) and in a vertical plane at y/B = 1.1 (beside the buildings). (d-f) and (g-i) same for chamfered and rounded cases, respectively. The black arrow indicates the flow direction.

In addition to the mean velocity field, turbulence intensity is of great interest with respect to the wind turbine lifetime requirements. High turbulence can result in fluctuating loads on wind turbine blades and could result in fatigue damage, which would eventually limit the turbine lifetime [103]. The International Electrotechnical Commission (IEC) Standard 61400-2 defines diverse classes of wind turbines where each has a limit for the reference turbulence intensity, Iref, calculated as below [127, 128]:

Chapter 3

52 Iref =

TI 0.75+

5.6

(3.7)

where Iref represents hub-height turbulence intensity at a mean wind speed of 15 m/s averaged over a time of 10 min. Based on the IEC standards, wind turbine classifications have the threshold value of Iref = 0.16, 0.14, and 0.12 for classes A, B, and C, respectively. This means that, for example, a class A wind turbine is designed to withstand turbulence up to a level corresponding to Iref = 0.16 during its lifetime typically specified of at least 20 years. Therefore, such a turbine should not be operated in regions with Iref > 0.16. This study uses an Iref value of 0.16 for a Class A wind turbine as the threshold of the acceptable turbulence level for turbine installation [103]. Figure 3.8 shows the Iref distribution in the same three planes shown in Fig. 3.7, for the three corner shapes. -

Along the passages between the buildings at y/B = 0 (Fig. 3.8a, 3.8d, and 3.8g): a noticeable high Iref region is observed around the upstream sharp corners and above the rooftop because of the locally strongly separated and sheared flows. For the configurations with chamfered and rounded corners, Iref tends to be relatively lower with smaller areas of high Iref values except in the center of the four-building array. Above the roofs at z/H = 1.03 (Fig. 3.8b, 3.8e, and 3.8h): For the case of the sharp corners, the flow separation causes large mean velocity gradients and the associated high Iref region clearly emerges above the windward roof edge of the upstream buildings, and extends to the leeward roof edge of the downstream buildings. For the configurations with chamfered and rounded corners, the reattachment of the separated shear layer over the chamfered and rounded roof corners results in less pronounced flow separation, weaker velocity gradients and the associated lower turbulence intensities. These findings are in agreement with the work by Toja-Silva et al. [35], where the impact of the rounded roof shape was studied. This finding highlights the significance of roof corner modifications in reducing Iref, especially on the rooftop. Besides the buildings at y/B = 1.1 (Fig. 3.8c, 3.8f, and 3.8i): For the sharp corners, high turbulence intensities occur over almost the entire area. In Fig. 3.8f and 3.8i, the modified chamfered and rounded corners strongly reduce the size of high-Iref areas. This confirms the significant impact of building corner shapes via chamfered and rounded shapes on the turbulence intensities.

From the viewpoint of wind energy exploitation, what matters most are wind power density and turbulence level. In this section, the power density (PD) is computed using Eq. 3.8: 1

3 𝑃𝐷 = 2 𝜌𝑉3𝐷

(3.8)

where  and V are the air density (= 1.225 kg/m3) and mean wind velocity, respectively. Note that, in Sections 3.5.1 – 3.5.3, it is assumed that a HAWT is installed between the buildings for all cases. Therefore, the mean streamwise velocity (U) is used in Eq. (3.8) in order to compute the power density. The sensitivity of the power density to the wind turbine type/orientation will be presented in Section 3.5.4. In the present study, an attempt is also made to unify the two measures of power density and turbulence intensity in order to provide a more informative representation of potential regions for wind energy harvesting. Towards this end, contour plots of wind power density are generated, and the regions of high turbulence (Iref > 0.16), which are not suitable for wind turbine installation, are indicated with white color in Fig. 3.9. Note that the power density values are non-dimensionalized using the incident reference wind power density PDref = 508.7 W/m2 at a reference building height of 90 m.

Urban wind energy potential: Impacts of building corner modifications

Figure 3.8. Contours of reference turbulence intensity (Iref) for case with sharp corners (Ref. case) in (a) a vertical plane at y/B = 0 (midplane), (b) a horizontal plane at z/H = 1.03 (just above the roof), (c) and in a vertical plane at y/B = 1.1 (besides the buildings). (d-f) and (g-i) same for chamfered and rounded cases, respectively. The black arrow indicates the flow direction.

Chapter 3

Figure 3.9. Contours of dimensionless power density for case with sharp corners (Ref. case) in (a) a vertical plane at y/B = 0 (midplane), (b) a horizontal plane at z/H = 1.03 (just above the roof), (c) and in a vertical plane at y/B = 1.1 (besides the buildings). (d-f) and (g-i) same for chamfered and rounded cases, respectively. Note that the high turbulence region (Iref > 0.16) is masked with white, signaling its unsuitability for turbine installation according to the turbine classifications [45]. The black arrow indicates the flow direction.

To quantify the effect of building corner modifications on the reduction of the unsuitable high turbulence (Iref > 0.16) area for wind turbine installation, a parameter termed as the unacceptable reference-turbulence-intensity area ratio (URAR) is defined using Eq. 3.9 and in Table 3.4-3.5. 𝑈𝑅𝐴𝑅 =

𝑢𝑛𝑎𝑐𝑐𝑒𝑝𝑡𝑎𝑏𝑙𝑒 𝐼𝑟𝑒𝑓 𝑎𝑟𝑒𝑎 𝑡𝑜𝑡𝑎𝑙 𝑠𝑒𝑙𝑒𝑐𝑡𝑒𝑑 𝑎𝑟𝑒𝑎

× 100

(3.9)

Urban wind energy potential: Impacts of building corner modifications

The observations are as follows: -

Along the passages between the buildings at y/B = 0 (Fig. 3.9a, 3.9d, and 3.9g): for the case of sharp corners, URAR = 30%, while the remaining areas are not interesting due to low power density (PD/PDref < 0.7). URAR for the chamfered and rounded corner shapes are also high, i.e., 45% and 27%, respectively. However, in the remaining regions, the PD is significantly higher, especially for the rounded corner shape. Above the roofs at z/H = 1.1 (Fig. 3.9b, 3.9e, and 3.9h): for the case of the sharp corners, almost the whole roof area is unsuitable for wind turbine installation. However, there is a dramatic change for the chamfered and rounded corner shapes where turbines could be installed due to much lower turbulence levels. Note that the power density for the rounded corner shape is comparatively higher than the chamfered corner shape. Beside the buildings at y/B = 1.1 (Fig. 3.9c, 3.9f, and 3.9i): for the case of the sharp corners, almost the whole area beside the buildings is labeled as unsuitable for wind turbine installation. For the chamfered and rounded corners, the URAR drastically decreases to 43% and 28%, respectively. Note that for the chamfered and the rounded corner shapes, the majority of the unsuitable high turbulence region (69% and 52%, respectively) corresponds to the area beside the upstream buildings. For the rounded corner shape, almost the whole area beside the downstream buildings is suitable for turbine installation.

Figure 3.10. Comparison of the area-weighted average dimensionless power densities for six potential locations for wind energy harvesting.

Chapter 3

To observe the overall tendency of PD for the different building corner shapes, Fig. 3.10 shows a histogram comparing the area-weighted average PD/PDref in six potential locations for wind energy harvesting. The highest wind power density is obtained for the rounded corner shape followed by the chamfered corner shape. In addition, it can be seen that: -

Along the passages between the buildings at y/B = 0: The area-weighted average PD/PDref values for the rounded corner configuration are 80% and 283% higher than those of the sharp corner configuration at potential areas I and II, respectively. For the chamfered corner shape, these differences are 60% and 101%, respectively. Above the roofs at z/H = 1.03: the area-weighted average PD/PDref values for the rounded corner configuration are 274% and 365% higher than those of sharp corners at potential areas III and IV, respectively. For the chamfered corner shapes, these are 259% and 315%, respectively. Besides the buildings at y/B =1.1: for the configuration with sharp corners, very low wind power densities (PD/PDref  0) are noted beside the upstream and downstream buildings, while the rounded and chamfered corners present much higher wind power densities (PD/PDref  1). The area-weighted average PD/PDref values for the rounded corners are 1233% (from 0.1 to 1.33) and 1101% (from 0.1 to 1.2) higher than for the sharp corners at potential areas V and VI, respectively. For the chamfered corners, these differences are 997% and 722%, respectively.

It can be concluded that replacing the sharp building corners by the rounded and chamfered corners for both facade and roof can strongly increase the wind power density. 3.5.2

Impact of chamfer length

To study the effect of the chamfer length (l) on wind energy potential, four l values ranging from 1 m to 4 m (i.e., 0.05B, 0.10B, 0.15B, and 0.20B) are investigated. Figure 3.11 shows contour plots of PD/PDref, with high turbulence regions with Iref > 0.16 masked with white color, in the three aforementioned planes for l = 0.05B and 0.20B. The URAR values (Eq. (3.9)) for all l values are presented in Table 3.4. -

Along the passages between the buildings at y/B = 0 (Fig. 3.11a-b & Table 3.4): As l increases from 0.05B to 0.2B, the maximum wind power density enhances by 22% in the upstream passage with a maximum PD/PDref of 2.3, while the power density reduces by 13% in the downstream passage. In this case, we notice an increase in URAR up to 56% in the upstream passage and in the front part of the downstream passage. Above the roofs at z/H = 1.03 and beside the buildings at y/B = 1.1 (Fig. 3.11c-f & Table 3.4): increasing l can augment the power density up to 12% and 110% above the building roofs and beside the buildings, and also reduce the unacceptable Iref areas. In particular, there are no visible undesirable high turbulence regions above the roofs for the chamfer length of 0.15B and 0.20B.

Urban wind energy potential: Impacts of building corner modifications

Figure 3.11. Contours of dimensionless power density for case with chamfer length of 0.05B in (a) a vertical plane at y/B = 0 (midplane), (b) a horizontal plane at z/H = 1.03 (just above the roof), (c) and in a vertical plane at y/B = 1.1 (besides the buildings). (d-f) same for case with chamfer length of 0.2B. Note that the high turbulence region (Iref > 0.16) is masked with white, signaling its unsuitability for turbine installation. The black arrow indicates the flow direction. Table 3.4. Ratio of high-turbulence area (Iref > 0.16, unsuitable for turbine installation) over the total area of the planes shown in Fig. 3.11 for different chamfer lengths.

Case ID

Chamfer length, l

Case C1 Case C2 Case C3 Case C4

0.05B 0.10B 0.15B 0.20B

3.5.3

Along the passages between the buildings (y/B = 0) 25% 28% 45% 56%

Selected area Above the roofs (z/H=1.03) 24% 9% 0% 0%

Beside the buildings (y/B = 1.1) 51% 45% 43% 26%

Impact of corner radius

To assess the impact of the corner radius on the wind power density, four radii are studied: r = 1 m, 2 m, 3m, and 4 m (i.e., 0.05B, 0.10B, 0.15B, and 0.20B). Figure 3.12 shows the contour plots of the predicted PD/PDref, with high turbulence regions with Iref > 0.16 masked with white color, over three possible areas for r = 0.05B, and 0.20B. The URAR values are listed in Table 3.5.

Chapter 3

58 -

Along the passages between the buildings at y/B = 0 (Fig. 3.12a-b & Table 3.5): Enlarging corner radius from 0.05B to 0.20B can enhance PD/PDref by 26%. As the corner radius increases from 0.05B to 0.20B, URAR increases with 62%. Above the roofs at z/H = 1.03 and beside the buildings at y/B = 1.1 (Fig. 3.12c-f & Table 3.5): By enlarging the corner radius, PD/PDref can increase 23% and 350% above the building roofs and beside the buildings, respectively. Increasing the corner radius can reduce URAR and enhance the power density. URAR is zero above the roof for the corner radius of 0.15B and 0.20B.

Figure 3.12. Contours of dimensionless power density for case with rounded radius of 0.05B (a) a vertical plane at y/B = 0 (midplane), (b) a horizontal plane at z/H = 1.03 (just above the roof), (c) and in a vertical plane at y/B = 1.1 (besides the buildings). (d-f) same for case with rounded radius of 0.2B. Note that the high turbulence region (Iref > 0.16) is masked with white, signaling its unsuitability for turbine installation. The black arrow indicates the flow direction. Table 3.5. Ratio of high-turbulence region (Iref > 0.16, unsuitable for turbine installation) over the total area of the planes shown in Fig. 3.12 for different corner radius.

Case ID Case R1 Case R2 Case R3

Rounded Along the passages between corner radius, r the buildings (y/B = 0) 0.05B 24% 0.10B 25% 0.15B 27%

Selected area Above the roofs (z/H=1.03) 28% 13% 0%

Beside the buildings (y/B = 1.1) 76% 37% 28%

Urban wind energy potential: Impacts of building corner modifications Case R4

3.5.4

0.20B

39%

59 22%

Impact of wind turbine type and orientation

The wind power density is typically calculated using only the streamwise velocity component, which is the relevant velocity for the typical HAWTs [103]. Note that the results presented in Secs. 3.5.1-3.5.3 pertain to such a HAWT. However, many studies reported that VAWTs are more suitable for energy harvesting in the urban environment than HAWTs due to their omni-directionality, lower noise production, higher space efficiency and lower installation and maintenance costs [19-21, 129]. Therefore, in this study, the potential for two types of VAWTs is investigated: (i) VAWT; and (ii) H-VAWT, where the latter refers to a VAWT mounted such that its axis is horizontal. The associated wind power densities (PD) are, therefore, calculated based on the velocities for each type of turbine in Fig. 3.13 as follows: a) b) c)

PDH for HAWTs: Only the streamwise velocity component (U) is used to calculate PD. PDVV for VAWTs: The vector sum of the streamwise and lateral velocity (V) components is used to calculate PD. PDVH for H-VAWTs: The vector sum of the streamwise and vertical velocity (W) components is used to calculate PD.

Figure 3.13. Three different wind turbine type/orientation for wind energy harvesting in the building passage.

Figure 3.14 presents the profiles of the dimensionless power density (a) along the passage at y/B = 0 (midplane) and z/H = 0.93, (b) just above the roof at y/B = 0.5 and z/H = 1.03 and (c) beside the buildings at y/B = 1.1 and z/H = 0.93 for the three types of wind turbines. In line with the observations discussed in Section 3.5.1, Fig. 3.14 also shows that the PD for the chamfered and the rounded corner shapes are substantially higher than those of the sharp corner along the three lines. Therefore, the discussion on the different turbine types is focused on the chamfered and the rounded corner shapes due to their comparatively high PD.

Chapter 3

Figure 3.14. Horizontal profiles of the dimensionless power density: (a) along the passage at y/B = 0 (midplane) and z/H = 0.93 (just below the roof); (b) just above the roof (z/H = 1.03) at y/B = 0.5 ; (c) beside the buildings at y/B = 1.1 and z/H = 0.93 for the different wind turbine types.

The findings are presented for the following three regions: -

Along the passages between the buildings (Fig. 3.14a): In the upstream passage, the values of PDVH/PDref along the line are much higher than those of PDVV/PDref and PDH/PDref, which are almost comparable. This shows the potential for benefiting from the vertical component of velocity in this region. In the downstream passage, the three lines are almost overlapping, implying the dominance of the streamwise component of the velocity. The average value of PDVH/PDref for the designs of sharp, chamfered, and rounded corners along the passage is 14%, 20%, and 16% higher than those of the PDVV/PDref and PDH/PDref. Above the building roof (Fig. 3.14b): For the areas in the front of upstream roofs and the back

Urban wind energy potential: Impacts of building corner modifications

of downstream roofs, PDVH/PDref is slightly higher than PDVV/PDref and PDH/PDref. Apart from these areas, the differences between PDVH/PDref, PDVV/PDref, and PDH/PDref remain limited. The average PDVH value is higher than those of PDVV/PDref and PDH/PDref by 24%, 5%, and 6% for sharp, chamfered, and rounded corners, respectively. Besides the buildings (Fig. 3.14c): The values of PDVV/PDref along the line beside the buildings are higher than the values of PDH/PDref and PDVH/PDref. The average PDVV/PDref values can be enhanced by 43%, 11%, and 11%, compared to the PDH/PDref and PDVH/PDref for sharp, chamfered, and rounded corners, respectively.

The analysis shows that, for wind direction 0°, i.e. parallel to the passage between the buildings, the horizontally-mounted VAWTs are the best type of turbine to maximize the wind energy harvesting along the passage between the buildings as well as above the rooftop. On the other hand, the typical (vertically-aligned) VAWTs are the best option for wind energy harvesting besides the buildings.

Figure 3.15. Contours of dimensionless power density in horizontal plane at z/H = 0.93 (just below the roof) for (a-b) sharp, (c-d) chamfered, and (e-f) rounded corner shapes at wind directions of 0° and 45°. Note that the high turbulence region (Iref > 0.16) is masked in white, signaling its unsuitability for turbine installation. The black arrow indicates the flow direction.

Chapter 3

62 3.5.5

Impact of wind direction

The contour plots of PD/PDref, with high turbulence regions with Iref > 0.16 masked with white color, in the horizontal planes at z/H = 0.93 (just below the roof) and 1.03 (just above the roof) are shown in Fig. 3.15 and Fig. 3.16, respectively, for wind directions of 0° and 45°. Here the wind power density is calculated using the streamwise velocity component as for the typical HAWTs. Note that the high turbulence region (Iref > 0.16) is masked with white color, signaling its unsuitability for turbine installation based on the IEC standard [128]. It can be seen that the magnitude and distribution of the wind power density and the extent of the high turbulence regions are very sensitive to the flow angle and very different values and patterns are observed for the two flow angles studied. The URAR for the wind directions of 0° and 45° are listed in Table 3.6.

Figure 3.16. Contours of dimensionless power density in horizontal plane at z/H = 1.03 (just above the roof) for different building corner shapes at wind directions of 0° and 45°. Note that the high turbulence region (Iref > 0.16) is masked with white, signaling its unsuitability for turbine installation. The black arrow indicates the flow direction.

Regardless of the flow angle, the extent of the unsuitable region for turbine installation below/over the roof at different flow angles is significantly less for the chamfered and rounded corner shapes compared to the sharp corner. This is significant as it highlights that with the

Urban wind energy potential: Impacts of building corner modifications

typical sharp corners, many regions near the roof of the high-rise building array are not suitable for wind energy harvesting and the remedy of corner modification is very effective. In addition, using corner modifications can increase the available wind energy potential at different flow angles over the roof. Table 3.6. Ratio of high-turbulence region (Iref > 0.16, unsuitable for turbine installation) over the total area of the planes shown in Figs. 3.16 for different corner shapes at wind directions of 0° and 45°. Case ID

Corner shape

Wind direction

Selected area Above the roofs (z/H=1.03)

0° 45° 0° 45° 0° 45°

96% 42% 0% 0% 9% 0%

Ref. case Sharp corner

3.6

Case C3

Chamfered corner (l = 0.15B)

Case R3

Rounded corner (r = 0.15B)

Limitations of the study Some limitations of the study are mentioned:

(1)

(2)

(3)

3.7

The present analysis is focused on an isolated 2×2 array of generic high-rise buildings, without considering the impact of surrounding buildings in urban settings. Other building arrangements with varied aspect ratios can be taken into account to facilitate the selection of the most appropriate corner modification of buildings. Besides, future investigations can be conducted to compare the wind environments of generic and real urban areas for the assessment of wind energy potential. This study performs the CFD simulations adopting smooth and simple wall facades for high-rise buildings. Earlier studies have shown that building facade geometrical details such as balconies can significantly influence the near-building wind conditions [104-107]. Future work can take into account the impact of building facade geometrical details on the available wind energy potential around high-rise buildings. The analysis in this study and most previous studies employed the mean wind speed or the mean wind power density to characterize the tendency of the wind power potential, without considering the wind intermittency, variability, and the temporal distribution of power generation. Further development can focus on these parameters.

Conclusions

Detailed evaluation of various impacts of building corner modifications on the urban wind energy potential for a 2 × 2 array of high-rise buildings in close proximity has been performed through high-fidelity CFD simulations. The following parameters are studied: (i) the impact of building corner shape, (ii) the impact of chamfer length (l), (iii) the impact of corner radius (r), (iv) the impact of wind turbine type and orientation, and (v) the impact of wind direction. This study primarily focuses on enhancing the wind power density (PD) and reducing the turbulence intensity (Iref) along the passage, above the roof, and beside the buildings. The main conclusions are as follows: -

Among those three types of building corner shapes, the rounded corner shape (r = 0.15B) has the highest PD increased by 365%, and a decrease in the unacceptable Iref region (Iref > 0.16, where turbines cannot be installed) up to 91% above the roofs, as compared to the

Chapter 3 sharp corner case. The larger l (= 0.20B) leads to an increase in the maximum PD up to 110% beside the buildings, and no visible undesirable Iref areas above the roofs and beside the buildings, compared with those for l = 0.05B. The longest r (= 0.20B) achieves an increase in the maximum PD up to 350%, and no visible undesirable Iref areas beside the buildings, compared with those for r = 0.05B. Among the three types of turbines studied, the horizontally-mounted VAWT is the best option for wind energy harvesting along the passages between the buildings with round corners as well as above the roofs. This is while the typical VAWT yields the highest power density beside the round-corner buildings. Under the oblique wind direction of 45°, there are no visible undesirable high turbulence regions above the roofs for the chamfered and rounded corner shapes, as compared to the sharp corner case.

Urban wind energy potential: Impacts of urban density and layout This chapter has been published as a peer-reviewed article in a scientific journal:

Impacts of urban morphology on improving urban wind energy potential for generic high-rise building arrays Juan, Y.H., Wen, C.Y., Li, Z.T., Yang, A.S., Applied Energy 229 (2021) 117303 Abstract: Previous findings have indicated better performance attained by modified urban morphologies for wind energy utilization only in single and pair buildings, or medium-dense low-rise building arrays. Hence, the main purpose of this study is to address the research gaps to complete a fundamental understanding of the influences of urban morphology in compact high-rise urban areas on enhancing urban wind energy harvesting for sustainable urban development. A comprehensive parametric study is conducted using the computational fluid dynamics tool to analyze the impacts of urban morphologies on the wind energy potential for a 6 × 6 array of generic high-rise buildings, including (i) urban density altered from compact to sparse urban layouts, (ii) building corner shapes of sharp and rounded corners, (iii) urban layouts of in-line and staggered patterns, and (iv) wind directions of 0° and 45°. This investigation implements the three-dimensional steady Reynolds-averaged Navier-Stokes equations with the Reynolds stress model to explore the distributions of wind speed, power density, and turbulence intensity over the building array. The results indicate that decreasing urban plan area density reduces the unacceptable turbulence areas with relatively higher wind power density on the roof. Besides, round corners can produce elevated power densities up to 201% greater than sharp corners beside the building. Even under the oblique wind direction of 45°, the rounded corner still shows better wind energy potentials than the sharp corner. The in-line urban layout demonstrates more significant areas with higher power densities and low turbulence intensities than the staggered urban layout. Keywords: Urban wind energy; Urban Morphology; Compact city; High-rise building; Urban density; Aerodynamic modification. Nomenclature ABL B CFD

Atmospheric boundary layer Building length Computational fluid dynamics

Chapter 4

66 Cs ɛ H H/W Iref k ks PD PDref RANS RSM TI TI TIref u u U(z)ABL u*ABL Uref uref v V Vref w W x, y, z y* z0

4.1

Roughness constant Turbulent dissipation rate Building height Building-height-to-street-width ratio Reference turbulence intensity Turbulent kinetic energy Sand-grain roughness height Power density Reference power density Reynolds-averaged Naiver-Stokes Reynolds stress model Turbulence intensity Incident vertical profiles of turbulence intensity Reference turbulence intensity at reference height H Streamwise mean velocity component Incident vertical profiles of streamwise mean velocity component Mean inlet velocity of atmospheric boundary layer ABL friction velocity Reference wind speed Reference streamwise mean velocity component Cross-stream velocity Velocity magnitude Reference velocity magnitude Spanwise velocity Gap width between buildings Coordinates y plus Aerodynamic roughness length

Introduction

Urban wind energy presents lots of interest through turbine installation in the urban environment, which can solve the transportation difficulties of electric transmission lines from the wind farms in remote locations to cities [4, 130]. However, the challenges are also caused by complicated interactions of turbulent wind flows and eddies with urban forms and structures [47, 131]. The urban morphology is usually directly related to the characteristics of urban environments, including the urban density and urban arrangement, and the geometry of buildings. Consequently, to maximize the urban wind energy potential for facilitating sustainable urban areas at the neighborhood and city scales, it is crucial to comprehend the detailed airflow patterns and turbulence characteristics around buildings in the urban configuration. Different flow regimes over street canyons determine the amounts of power density and turbulence intensity around high-rise buildings in urban areas, which are the essential parameters in estimating wind energy potential. Much attention has been devoted to investigating the relationship between the urban form and sustainability, pinpointing the implications of the shape and density of cities for future progression. Strong arguments from many studies suggest the compact city as the most sustainable urban form [132]. Urban density determines the compactness among buildings. It can be correlated with two indexes as (1) the urban plan area density (λp) (i.e., the ratio between the plan area of buildings viewed from above and the total floor area) and (2) frontal area density (λf) (i.e., the ratio of the frontal area of buildings to the total floor area). The building area and frontal

Urban wind energy potential: Impacts of urban density and layout

area densities are closely linked to the size and shape of buildings. High-rise buildings surrounded by narrow street networks can generally provide high wind speeds at higher altitudes but strong resistance to the approaching wind. In addition, the placement and urban layout design are of major significance. The in-line or staggered building arrangements are also crucial in determining whether it is profitable for turbine installation. Escalating urban airflows in high-rise compact urban areas may enhance the urban wind energy potential between the urban canopy layers and their surroundings. A study on the interactions of the turbulent wind flows with buildings in such urban areas can be very useful to formulate the guidelines of better urban wind power implementation strategies in urban planning. As an effective tool for the early-stage urban design and planning, computational fluid dynamics (CFD) simulations were conducted to achieve the parametric studies of outdoor wind environments affected by the urban morphology [45, 59]. It is embedded with errors, uncertainties, and modeling challenges as a wind assessment tool. Hence, it is important that the prediction capabilities need to be verified via the wind tunnel measurements [57, 58, 61] or onsite measurements [17, 18, 38] to ensure reasonable calculation accuracy. Existing investigations of urban morphology primarily focused on the comprehension of urban ventilation [133-136], pedestrian wind comfort [137-140], urban heat island [141, 142], energy consumption [143-145], air pollution [146-148] and wind load distribution [104, 149]. Urban wind energy potential has been a newly developing related topic in the past decade [8, 9, 150]. The parametric studies of generic urban configurations simplify actual complex urban geometries into generic simple morphological models, extensively used in the analyses of urban wind environments for their advantages of linking specific geometric parameters to urban wind energy outcomes. Blocken et al. [151] asserted the importance of validating CFD simulations against other high-quality measurement data to provide strong support to the use of CFD in the outdoor environment. However, most previous studies [36, 37] on urban wind power for the generic urban areas conduct wind tunnel measurements by using a single cube model, without validating the prediction accuracy. There is still a need for further examination of the performance of CFD validations in a building array where the wind field is much more complex than one or two buildings, especially for the case of high-rise buildings. In addition, only the accuracy of wind speed shows a good agreement between predicted CFD results and wind tunnel data [76, 79]. The accuracies of turbulence intensity or turbulence kinetic energy have not been widely discussed. When airflows close to complex urban areas are likely to be highly turbulent, and this also needs to be studied for its effect on wind turbine performance. In the study of urban wind energy potential, the turbulent models without proper validation of turbulence intensity can cause significant errors in power density estimation. Some earlier studies like Lu and Ip [78] and Ledo et al. [37] were focused on the influence of sharp roof shapes (i.e., pitched, pyramidal, and flat roofs) on the distributions of wind speed and turbulence intensity. Wang et al. [66] examined the configurations of two perpendicular buildings with different building dimensions, corner separation distances, and angles. Balduzzi et al. [75] investigated the effects of the installation building height, the height and width of its upwind building, and the distance between the buildings themselves on the flat or sloping roofs for five buildings. Millward-Hopkins et al. [76] proposed the effective access of the highest wind resource available for a uniform array by increasing the mounting height on the roof with further arranging the mounting point close to the leading edge. Heath et al. [79] analyzed the wind field around a simple pitched-roof building on a 6x4 cube array to determine the optimum mounting sites for varied prevailing wind directions. To extend the investigations to other shapes of aerodynamic modifications, Abohela et al. [36] explored the vaulted roofs with a better advantage over an isolated building than a 5x5 cube array. Toja-Silva et al. [59] performed a CFD analysis for various shapes to suggest a spherical roof with a cylindrical wall optimized for wind energy utilization. Zhou et al. [56]

Chapter 4

identified that the composite prism building shape has huge wind energy potential between two low-rise buildings. Although the aforementioned findings have indicated better performance attained by modified urban morphologies for wind energy utilization around the buildings; however, most studies of urban wind energy potential have dealt with one cuboid [54, 55], or two parallel buildings [56, 62]. Only limited papers examined the wind field around cube arrays (i.e., the street aspect ratio of building height to street width, H/W= 1) [37, 59], or low-rise building arrays with H/W less than 2 [76]. Moreover, in view of urban density, most studies only tackled the mediumdense urban layout (λp = λf = 0.25). As exponential growth of high-rise buildings combined with massive urbanization, those high-rise densely populated cities like Hong Kong or the Manhattan district in New York, high-rise buildings are generally tall with relatively narrow streets with H/W commonly more than two or even up to 30 (as shown in Fig. 4.1). In this context, the urban density of compact high-rise cities can range up to λp = 0.76. In addition, only wind energy of the regular in-line urban layout has been examined without considering the scenario of staggered urban layouts, whereas the latter is more related to the diversity of actual complex urban areas. For the building corner modifications, the existing studies have only discussed the roof geometry effect on wind power potential, without modifying the shape of side building corner edges. For the assessment of possible wind turbine installation areas, a comprehensive study on the urban wind power estimation around the building array is still incomplete. Most researches probed the locations of turbine installation over the building roof [36, 75]. Some studies have reported other suitable mounting sites, such as the places along the passage between buildings [56, 62], beside the building [57], or directly integrated into the building [150]. Hence, the present study aims to address the above research gaps to complete a fundamental understanding of the influences of urban morphology in compact high-rise building arrays in a compact city on enhancing urban wind energy harvesting for sustainable urban development. In this perspective, the objectives of this study consist of the following tasks: 1) A regular 6×6 high-rise building array layout is set up as the object for full-scale CFD simulations. The urban density varies from a very compact urban layout (λp= 0.76 and λf= 3.4) to a sparse urban layout (λp= 0.09 and λf= 0.43), to quantitatively evaluate the effects of urban morphology variations on the wind power density and turbulence intensity. 2) The replacement of sharp corners with round corners is implemented for all building and roof edges of the center 2×2 high-rise buildings array to enhance wind energy potential as one of the effective aerodynamic modifications. 3) The CFD simulations are conducted to compare the wind power potentials around the staggered building layouts at varied urban densities with those around the regular in-line building layouts. 4) The wind energy resources for two incident wind directions θ= 0° and 45° are studied. 5) Two areas, including (i) beside the sidewalls of buildings and (ii) above the roofs for possible wind turbine installation, are selected to characterize the wind flow field around the building arrays at varied design parameters.

Urban wind energy potential: Impacts of urban density and layout

Figure 4.1. Photos of high-rise buildings in close proximity in the Kowloon City District, Hong Kong (photographed by Po-Ki Li).

This paper is systematized as follows: The predictions are compared with wind tunnel measurements for the CFD validation study, as illustrated in Section 4.2. Section 4.3 describes the CFD simulation details, consisting of all case scenarios, the computational domain, grid, settings, and grid-sensitivity analysis. In Section 4.4, the CFD simulations present the results of four impacts: (i) the urban density, (ii) the building corner shape, (iii) urban layout, and (iv) the wind direction on the evaluation of wind power potential. The limitations in the current study are discussed in Section 4.5. Section 4.6 summarizes the main conclusions obtained.

4.2

CFD validation study

The wind tunnel experiments are performed using an open-circuit atmospheric boundary layer wind tunnel of Eindhoven University of Technology (TU/e). The cross-section of the wind tunnel is 0.5 m × 0.65 m with 13 m long. A set of floor roughness elements is located 0.65 m ahead of the test section to reproduce the atmospheric boundary layer. The test model consists of four square cuboids placed as a 2×2 building array with a straight crossing street-canyon width of 0.028 m. The dimensions of the square cuboid model are 0.031 m  0.031 m  0.14 m, resulting in a blockage ratio of 3% in the wind tunnel with a scale of 1:643. The turbulent flow instrumentation (TFI) Cobra probe is utilized to measure the 3-component flow velocities and turbulence intensities [82]. Fig. 4.2a shows the locations of monitoring points on the lateral view of 6 profiles along the vertical centerlines (y/B = 0) at 6 positions (x/B = -2.9, -1.94, -0.97, 0, 0.97 and 1.94). The dimensionless incident vertical profiles (Fig. 4.2b) of time-averaged streamwise velocity (u/uref) and total turbulence intensity (TI/TIref) are measured from the empty wind tunnel, which are readily employed in CFD validation. The values of uref and TIref are 13.4 m/s and 8% at the building height.

Chapter 4

Figure 4.2. (a) Locations of monitoring points on lateral view of 6 profiles along the vertical centerlines (y/B = 0) at 6 positions (x/B = -2.9, -1.94, -0.97, 0, 0.97 and 1.94); (b) the dimensionless incident profiles; and (c) the computational domain for CFD validation.

The overall computational domain for the reduced-scale CFD validation study is depicted in Fig. 4.2c, following the CFD best practice guidelines [99, 152]. The high grid resolution computational mesh is generated using hexahedral elements only, with a total number of 5,464,450 cells. No less than 20 cells are disposed along with the passage distance between the crossing street canyons. A peak stretch factor of 1.1 is adopted with the least cell volume of 2.7×109 m3 to ensure y* values within 30 and 350 for proper implementation of the standard wall function treatment. For the boundary conditions, the solid walls are arranged as the no-slip walls. The ground surfaces integrate the sand-grain roughness modification with the roughness height (ks) of 0.0007 m and the roughness constant (Cs) of 0.13. To realize the roughness in the windtunnel tests, the aerodynamic roughness length z0 is set as 9×10-6 m. The outlet boundary is specified to be 1 atm. The lateral and top boundaries are imposed as symmetry with zero normal velocity and zero normal gradients of flow variables. The grid-sensitivity analysis has checked

Urban wind energy potential: Impacts of urban density and layout

the suitability of grid resolution. The CFD validation study utilizes the ANSYS/Fluent v19 to perform the 3D Reynolds-Averaged Navier-Stokes (RANS) simulations. As the most elaborate turbulence model provided by ANSYS/Fluent, the Reynolds stress model (RSM) turbulence model is selected based on a turbulence model sensitivity analysis presented in Section 4.2.1. In effect, RSM solves seven transport equations as turbulence closures to determine the individual components of the Reynolds stress tensor. The second-order discretization scheme is implemented in CFD calculations for the convection and diffusion terms of the governing equations. An iterative semi-implicit method for pressure-linked equations (SIMPLE) algorithm is used for the pressure-velocity coupling [90]. All the normalized residual errors of continuity, momentum, k, and ε equations are converged to 10−6 for attaining a steady solution.

Figure 4.3. Comparison of (a) time-averaged streamwise velocity component and (b) total turbulence intensity with wind-tunnel measured data along vertical lines at x/B= -2.9, -1.94, -0.97, 0, 0.97 and 1.94.

4.2.1

Turbulence model sensitivity analysis

The sensitivity analysis is performed to evaluate the prediction capabilities of the standard k-ε model (SKE) [91], the realizable k-ε model (RKE) [92], the renormalization group k-ε model (RNG) [93], the shear-stress transport k-ω model (SST) [94] and the RSM model [95]. Figure 4.3a presents a comparison of u/uref against the wind-tunnel measured data along the vertical

Chapter 4

centerline (y/B = 0) at x/B = -2.9, -1.94, -0.97, 0, 0.97 and 1.94). Overall, the agreement of u/uref between all five models and wind-tunnel measurements is acceptable in front of the four square cuboids (x/B = -2.91 and -1.94). For the other four positions, RSM has the best agreement with the wind-tunnel data, with the mean absolute differences less than 5%. Figure 4.3b presents a comparison of TI/TIref against the measured data at the aforementioned six positions. It is clear that the SKE, RKE, RNG, and SST models unsatisfactorily predict the turbulence intensities. Nevertheless, only the RSM model can reproduce the turbulence intensity profiles with the absolute mean differences less than 16%. Thus, RSM successfully predicts the velocity and turbulence intensity fields.

Figure 4.4. Description of urban plan area density (λp) and frontal area density (λf).

4.3 4.3.1

CFD simulation Description of urban scenarios

This study examines four different impacts of urban morphologies on the development of urban wind energy. The arranged scenarios of generic urban layouts dispose of the urban geometric parameters from practical applications. Table 4.1 illustrates the details of all involved parameters and investigated values in CFD simulation cases, as depicted in Fig. 4.4. All cases are based on a 6×6 array of identical high-rise buildings in full-scale dimensions. Each high-rise building has the same building height (H) of 90 m and an equal building length (B) of 20 m with different street canyon widths (W). The street canyon width between the buildings from the highest to lowest density cases is varied from 0.15B to 2.25B. To study the variation of urban density, two key parameters, urban plan area density (λp) and frontal area density (λf), are adopted and defined by Oke [153] in Fig. 4.4 (see Section Dispersion and urban geometry in Ref. 56). Table

Urban wind energy potential: Impacts of urban density and layout

4.1 illustrates the details of18 simulation cases to elucidate the influences of urban morphologies on urban wind energy as the following four parameters: (1)

(2)

(3) (4)

Urban density: W varied from 0.15B to 2.25B, corresponding to the transformation from a very compact urban layout (λp= 0.76 and λf= 3.4) to a sparse urban layout (λp= 0.09 and λf= 0.43). Building corner shape: Comparison of wind power potential between the sharp and rounded building corner shapes. The rounded corners are implemented on only the central 2×2 array of four high-rise buildings, as shown in Fig. 4.5 in gray color, while others are maintained as sharp corners. Here the radius of rounded corner modifications of building and roof corners is 0.15B. Urban layout: Both the regular in-line and staggered urban layouts are investigated at varied urban densities. Wind direction: Two wind directions of 0° and 45° are performed.

Figure 4.5. Urban morphologies for different involved parameters.

Chapter 4

Table 4.1. Details of involved urban morphology parameters for CFD simulation cases. Case name

Street

H/W

Urban area

Frontal

Urban

Corner

Wind

area

layout

shape

direction

0°

canyon

plan

width,

density

W/B A1

0.15

0.76

3.40

In-line

0.3

0.59

2.66

In-line

Sharp Sharp

In-line

Sharp

0°

In-line

Sharp

0°

In-line

Sharp

0°

In-line

Sharp

0°

In-line

Rounded

0°

In-line

Rounded

0°

In-line

Rounded

0°

In-line

Rounded

0°

In-line

Rounded

0° 0°

A3 A4 A5 A6 B1 B2 B3 B4 B5

0.45 0.75 1 2.25 0.15 0.3 0.45 0.75 1

10 6 4.5 2 30 15 10 6 4.5

0.48 0.33 0.25 0.09 0.76 0.59 0.48 0.33 0.25

2.14 1.47 1.13 0.43 3.40 2.66 2.14 1.47 1.13

0°

2.25

0.09

0.43

In-line

Rounded

0.5

0.4

Staggered

0°

4.5

0.25

1.13

Staggered

Sharp Sharp

0°

0.5

0.4

Staggered

Rounded

4.5

0.25

1.13

Staggered

Rounded

0°

0.15

0.76

3.40

In-line

0.3

0.59

2.66

In-line

Sharp Sharp

45° 45°

0.45

0.48

2.14

In-line

Sharp

45°

In-line

Sharp

45°

In-line

Sharp

45°

In-line

Sharp

45°

In-line

Rounded

45°

In-line

Rounded

45°

In-line

Rounded

45°

In-line

Rounded

45°

In-line

Rounded

45°

In-line

Rounded

45°

D4 D5 D6 D7 D8 D9 D10 D11 D12

4.3.2

0.75 1 2.25 0.15 0.3 0.45 0.75 1 2.25

6 4.5 2 30 15 10 6 4.5 2

0.33 0.25 0.09 0.76 0.59 0.48 0.33 0.25 0.09

1.47 1.13 0.43 3.40 2.66 2.14 1.47 1.13 0.43

Computational domain and grid

The extent of the computational domain from the building array border to the top, lateral, inlet, and outlet boundaries of the computational domain covers 4H, 4H, 4H, and 15H, respectively, following the requirements of the CFD best-practice guidelines [86, 99]. The details of the high-resolution and high-quality computational mesh for Case A4 are depicted in Fig.4.6, with the magnification of grids near the roof and ground. The minimum cell volume in canyons is 5.5×10-3 m3 in the streamwise, lateral and vertical directions. The grid size is enlarged from the border of high-rise building arrays to the boundaries of the computational domain with an expansion ratio below 1.1. This study arranges the distances from the center points of near-wall cells to the building and ground surfaces below 0.3 m (i.e., the y+ values ranging from 30 and 500) to ensure effective implementation of the standard wall functions in CFD computations. The total

Urban wind energy potential: Impacts of urban density and layout

grid numbers for all cases vary approximately 18-26 million, consisting of the hexahedral and prismatic cells only.

Figure 4.6. Computational mesh of Case A4 for the base grids, with magnification of grids near roof and ground.

4.3.3

Computational settings

The neutral atmospheric boundary layer inflow profiles of mean wind velocity U, turbulent kinetic energy k, and turbulence dissipation rate ε are prescribed at the inlet [126]. 𝑈(𝑧) =

∗ 𝑢𝐴𝐵𝐿

𝜅

𝑙𝑛 (

𝑧+𝑧0 𝑧0

∗ 2 𝑘(𝑧) = 3.3𝑢𝐴𝐵𝐿 ∗ 𝑢𝐴𝐵𝐿 =

𝜅𝑈ℎ 𝑙𝑛(

ℎ+𝑧0 ) 𝑧0

)

(4.1) (4.2) (4.3)

here z is the height coordinate, u*ABL is ABL friction velocity, and κ is the von Karman constant (of 0.42). In Eq (4.1), the aerodynamic roughness length (z0) is 1 m for treating the surroundings of studied high-rise buildings in a densely built-up area by the updated Davenport-Wieringa roughness classification [100] (see Section Classification comparison and an update in Ref. 59). In Eq (4.3), the friction velocity (Uh) is 5m/s at the height of 10 m, while the reference wind speed (Uref) of 9.4 m/s at the reference height of 90 m. The standard wall functions with roughness modifications are employed on the ambient ground with the associated roughness height ks of 0.15 and roughness constant Cs of 8, respectively. All CFD simulations are performed by the 3D steady RANS equations with the RSM model. No-slip boundary conditions and the standard wall functions are implemented to the building surfaces. The zero-gauge static pressure is set at the outlet surface of the computational domain, while the symmetric conditions are imposed on the top and lateral surfaces. All the normalized residual errors of flow variables converge to 10−6 with the mass balance check under 1% to attain the steady wind field environments.

Chapter 4

76 4.3.4

Grid-sensitivity analysis

The grid-sensitivity analysis is checked by the RSM model for Case A2 with rounded corners. The simulations of coarser and finer grids are carried out to improve the mesh with a linear refinement factor of √2 , varying the associated spatial resolutions of grids. The total numbers of mesh system for the coarse, base, and fine grids are 18,644,338, 37,524,060, and 43,130,680 cells, respectively. Figure 4.7 illustrates the predicted dimensionless streamwise velocity component along the centerline of the building array at z/H = 0.93 near the roof for the coarse, base, and fine grids of Case A2. The average deviations of dimensionless streamwise velocity between the coarse and base grids along the centerlines are 4%, whereas the discrepancies are less than 0.5% between the fine and base grids. Therefore, the base grid is retained for the rest of CFD calculations to assess urban wind energy potentials in different cases.

Figure 4.7. Grid-sensitivity analysis: the dimensionless streamwise velocity component along the centerline of building array at z/H = 0.93 for the coarse, base, and fine grids of Case A2.

4.4

Results

In this study, the predictions of wind velocity, wind power density (PD), and turbulence intensity (TI) have been fully investigated to characterize the wind power potential distribution around high-rise building arrays. PD is computed as [154]:

Urban wind energy potential: Impacts of urban density and layout 1

PD = 2 𝜌𝑉 3

77 (4.4)

Here V is the velocity magnitude, whereas  is the air density. PD is normalized by the reference incident power density, specifically, PDref = 508.7 W/m2 at the building height. Another important factor is turbulence intensity, which is of great interest as the threshold to prevent equipment damages during turbine operations. The limit of reference turbulence intensity (Iref) is prescribed as the classes of wind turbines derived from the International Electrotechnical Commission (IEC) Standard 61400-1 [128] (see Section 4.6.2 Wind turbine classes of the IEC). This is also the representative value of hub-height turbulence intensity at a mean wind speed of 15 m/s averaged over a time of 10 min for a function of turbine class, expressed as below: Iref =

TI 0.75+

5.6

(4.5)

The values of Iref of 0.16, 0.14, and 0.12 are categorized into Class A, B, and C of small wind turbine classification based on the IEC standards. This study adopts Iref of 0.16 for Class A wind turbine as the threshold of the acceptable turbulence level for turbine installation [128]. To investigate the effects of the high-rise building array layout on wind energy harvesting, the general possible areas of deploying wind turbines are: (i) mounted on the rooftop, (ii) placed between two adjacent buildings, (iii) located beside the sidewall of buildings. The presence of high-rise building array within the free stream causes two outcomes. One is the high power densities from accelerated airflow speeds and other is the appropriate reference wind turbulence intensity in the vicinity of high-rise buildings. 4.4.1

Impact of urban density

Figure 4.8 illustrates the predicted contour plots of dimensionless velocity magnitude V/Vref (with superimposed streamlines) over the horizontal x-y planes (a) the high-level buildings and (b) above the roofs as well as the vertical x-z planes (c) beside the building sidewalls and (d) along the middle of building column above the roofs for the urban layouts of λp= 0.76, 0.25, 0.09 and the normal wind direction of 0°. Those specific planes are used to characterize the wind flow field around the building array, with special attention to those installable areas limited to (i) beside the sidewall of buildings and (ii) above the roofs for turbine installation. The specified heights of horizontal x-y planes for the high-level buildings and above the roofs are z/H=0.93 and 1.03, respectively. The vertical x-z planes are selected at a distance of 1.5 m beside the sidewalls and along the middle 3-column of buildings above the roofs. For the sake of brevity, only the wind characteristics for the layouts of compact urban (λp = 0.76), medium urban (λp = 0.25), and sparse urban (λp = 0.09) are presented in Figs. 4.8 - 4.10. The following observations can be made: −

−

For the regions along the middle of the street canyon of building array in Fig. 4.8a, the airflows with declining λp from 0.76 to 0.09 smoothly permeate through broader street widths into the building array with elevated wind velocities of V/Vref > 0.9 (marked in redcolor areas) along the passage, caused by the channeling effect between parallel buildings. For the regions above the roof in Fig. 4.8b and 4.8d, essentially, the approaching wind stagnates and diverges upwardly at the rooftops of first row buildings, and then travels toward the downstream with the decelerating airflows. For the compact urban layout at λp = 0.76, it can be visualized that the shear wind over the roof tends to radiate from the windward corner with the largest expansion angle near the roof. The associated recirculation region above the roof is relatively large, with low velocities along the roofs of downstream buildings. In contrast, for the sparse urban layout (λp = 0.09), the recirculation region above the roof is smaller due to the expansion of the high-speed shear wind around

−

Chapter 4 the windward corners. We also observe the reattachment of airflows appearing over the roofs of downstream buildings For a possible alternative to mount wind turbines at a distance of 1.5 m beside the buildings in Fig. 4.8c, the maximum wind speed emerges from the corners of 1-row buildings without causing recirculation vortices over the narrowest gap passage for the compact urban layout (λp= 0.76). For the medium urban layout (λp= 0.25), the highest wind speed appears at the leading windward corner, with the sudden deceleration of wind flow resulting from a small recirculating eddy near the wall of 1-row buildings. Taking the sparse urban layout (λp= 0.09) into account, the swirling vortex becomes larger to allow for the development of a relatively high-speed region appearing over 2-row buildings.

Figure 4.9 presents the predicted contour plots of reference turbulence intensity over the horizontal x-y planes (a) at z/H= 0.93 and (b) 1.03 as well as the vertical x-z planes (c) at a distance of 1.5 m beside the buildings and (d) along the middle of building column for the urban layouts of λp= 0.76, 0.25, 0.09. Here the findings mainly focus on two possible areas for mounting wind turbines as follow: −

−

For the possible area at a distance of 1.5 m beside the building in Fig. 4.9a and 4.9c, the unacceptable Iref regions (Iref > 0.16 in orange-red colored areas) for the compact urban layout (λp = 0.76) appear between the 1-row of building array attributable to strongly sheared airflows over the expansion corners. For decreasing λp with broader street widths, we view notable extension and separation of the unacceptable Iref regions along the leadingedge windward walls and tend to recur over the wake of each downstream building. For an alternative available above the roofs in Fig. 4.9b and 4.9d, the wind flows with the compact urban layout (λp = 0.76) are almost completely covered by unfavorable environments with high turbulence intensities. Alternatively, the incoming separation airflows interacting with the sparse urban layout (λp= 0.09) result in the unacceptable Iref region condensed to a height below 1.1 times the building height over the roof corners, allowing for turbine installation at the height above 1.1H.

Urban wind energy potential: Impacts of urban density and layout

Figure 4.8. Predicted contour plots of dimensionless velocity magnitude V/Vref (with superimposed streamlines) over the horizontal x-y planes (a) at z/H= 0.93 and (b) 1.03 as well as the vertical x-z planes (c) at a distance of 1.5 m beside the buildings and (d) along the middle of building column above the roofs for λp = 0.76, 0.25, 0.09 and the normal wind direction of 0°.

Chapter 4

Figure 4.9. Predicted contour plots of reference turbulence intensity over the horizontal x-y planes (a) at z/H= 0.93 and (b) 1.03 as well as the vertical x-z planes (c) at a distance of 1.5 m beside the buildings and (d) along the middle of building column for the urban layouts of λp = 0.76, 0.25, 0.09 and the normal wind direction of 0°.

To investigate the wind energy potential, not only power density but also turbulence level should be taken into account. Hence, we intend to combine these two criteria in this study. Figure 4.10 shows the contour plots of the normalized power density over the horizontal x-y planes (a) at z/H= 0.93 and (b) 1.03 as well as the vertical x-z planes (c) at a distance of 1.5 m beside the buildings and (d) along the middle of building column for λp = 0.76, 0.33 and 0.09 as three scenarios denoting the compact, medium and sparse urban plan area densities, respectively. Note that the high turbulence region of Iref > 0.16 is masked in white color, representing its

Urban wind energy potential: Impacts of urban density and layout

unacceptability for small wind turbine installation base on the IEC standard [128]. Overall, wind power density is directly proportional to the cube of the wind speed. Therefore, the greater the wind speed, the greater the energy potential will be extracted from the wind for conversion to the useful power density through turbine operations. For the possible area at a distance of 1.5 m beside the building and z/H= 0.93 in Fig. 4.10a and 4.10c, the undesirable high turbulence levels (highlighted in white color) appear over the gap passage between and behind those 1st-row buildings for λp of 0.76 and 0.33. The acceptable turbulence area with high PD only appears over the gap passage between the back half of 1-row and the front half of 2-row buildings for the compact urban layout (λp = 0.76). With the sparse urban layout of λp = 0.09, the unacceptable high turbulence level areas appear over the front half of the windward walls of buildings for every row. − For the areas above the roofs in Fig. 4.10b and 4.10d, the compact urban layout (λp= 0.76) exhibits the most unacceptable high turbulence area extending over the roofs of the nearly whole building array. For the medium urban layout (λp= 0.25), only the roofs of the first 1-, 2-, and 3-row buildings are covered with unsuitably elevated turbulence levels but with lower PD. For the sparse urban layout (λp = 0.09), although the unacceptable Iref regions appear over the roof corners every row, we still view visible high PD areas to install wind turbines. To sum up, increasing λp can reduce both the regions of high wind power density and unacceptable turbulence level for the possible area at a distance of 1.5 m beside the building. Alternately, decreasing λp can reduce the region height of unacceptable turbulence areas with relatively higher wind power density on the roof. −

Figure 4.11 summarizes the predictions of average dimensionless power densities and average reference turbulence intensity of selected areas (i) over the horizontal plane at z/H= 1.03 on the roof as well as (ii) the vertical plane at a distance of 1.5 m beside the building for varied urban plan area densities. Those selected areas primarily focus on the prearranged central highrise buildings as the promising locations for turbine installation, with the area-weighted average wind power densities calculated over the horizontal plane at z/H= 1.03 above the roof of 3-row buildings as well as the vertical plane at a distance of 1.5 m beside the 3-row buildings, respectively. -

For the selected areas at a distance of 1.5 m beside the building with the vertical extent of z/H ranging from 0.5 to 1 (in orange-colored lines), the maximum averaged PD/PDref appears at λp= 0.33. It can be attributed that an optimal layout of urban density is inclined to maximize the power density because of the wind undergoing the channeling effect of the passage between parallel buildings. However, a further decrease in λp can reduce the averaged PD/PDref and increase the averaged Iref. Nevertheless, we still observe the acceptable averaged Iref level with λp below the middle urban layout (at λp= 0.25). For the selected areas on the roof at z/H= 1.03 (in green lines), the averaged PD/PDref increases by reducing λp from the compact to sparse urban layouts, leading to dramatic upsurges of power density at particularly low λp values. We also observe the maximum averaged PD/PDref of 0.32 for the sparse urban layout (λp = 0.09) of 3-row buildings. Besides, a reduction in λp can achieve the lowest possible averaged Iref of around 0.16.

Chapter 4

Figure 4.10. Predicted contour plots of dimensionless power density (with unacceptable turbulence region in white color) over the horizontal x-y planes (a) at z/H= 0.93 and (b) 1.03 as well as the vertical x-z planes (c) at a distance of 1.5 m beside the buildings and (d) along the middle of building column for the urban layouts of λp = 0.76, 0.25, 0.09 and the normal wind direction of 0°.

Urban wind energy potential: Impacts of urban density and layout

Figure 4.11. Predictions of average dimensionless power densities and average reference turbulence intensity of selected areas over the horizontal plane at z/H= 1.03 on the roof as well as the vertical plane at 1.5 m beside the building for varied urban plan area densities.

4.4.2

Impact of building corner shape

Generally, building geometry plays a significant role in enhancing wind power in urban areas. This study implements the rounded corners on only the central 2×2 array of four high-rise buildings to compare the wind power potentials between the sharp and rounded building corner shapes for varied urban plan area densities. Figures 4.12 and 4.13 show the predicted contour plots of dimensionless power density (with unacceptable turbulence region highlighted in white color) over the vertical x-z planes (a) at a distance of 1.5 m beside the buildings as well as the horizontal x-y planes (b) at z/H= 0.5 and (c) 1.03 for the sharp/round corners and urban densities λp of 0.25/0.09, respectively. Overall, the results reveal the significant impact of building corner shape on wind power potential. Detailed observations are provided as below: -

For those selected areas at a distance of 1.5 m beside the building (Fig.4.12a-b and 4.13a-b): The predictions having the sharp corner shape present the relatively high PD beside the leading edge of 3-row buildings, with a quick decrease in PD along the gap passage toward the downstream. In contrast, for the round corners, we notice the appearance of high PD regions extending to the windward and leeward round corners of both 3-row and 4-row buildings for λp= 0.25 depicted in Fig.4.12a-b. For the sparse urban layout (λp= 0.09), the high PD regions even emerge on the leeward round corners of the roof with no visible unacceptable Iref regions in Fig.4.13a. This can be attributed to the continuous expansion of airflows over the round corners to produce high wind energy due to accelerated speeds

Chapter 4 and low-level turbulence, as compared to those of sharp corners. For those selected areas on the roofs at z/H= 1.03 (Fig.4.12c and 4.13c): It can be seen that a more compact urban layout (λp of 0.25) shows a more unacceptable turbulence region than that of λp = 0.09 on the roofs. Considering the scenario of sharp corners for the sparse urban layout (λp = 0.09) in Fig.4.13c, the wind passes over the roof corners of buildings on the windward side of each row, and induces locally unacceptable turbulence regions around the windward roof corners. Alternatively, the round corner shape can reduce the unacceptable turbulence region due to its aerodynamic corner shape. The power densities on the roofs of 4-row buildings (with the windward buildings using round corners) are substantially higher than those on the roofs of 3-row buildings (with the windward buildings having the sharp corner shape).

It is obvious that the round corner shape can result in a higher PD and a lower Iref over the downstream buildings. In addition, when λp increased, the round corner shape produces higher PD outcomes than the sharp corner shape for the installable areas beside the buildings. When λp decreased, higher power densities appear over the round corners for the installable areas on the roofs, as compared to the case of sharp corners. These results are consistent with the findings from the impact study of urban density.

Figure 4.12. Predicted contour plots of dimensionless power density (with unacceptable turbulence region in white color) over the vertical x-z planes (a) at a distance of 1.5 m beside the buildings as well as the horizontal x-y planes (b) at z/H= 0.5 and (c) 1.03 for the sharp/round corners and urban layout of λp = 0.25.

Urban wind energy potential: Impacts of urban density and layout

Figure 4.13. Predicted contour plots of dimensionless power density (with unacceptable turbulence region in white color) over the vertical x-z planes (a) at a distance of 1.5 m beside the buildings as well as the horizontal x-y planes (b) at z/H= 0.5 and (c) 1.03 for the sharp/round corners and urban layout of λp = 0.09.

To explore the effect of varied upstream building corner shapes on the downstream buildings, we focus on the wind environments around the selected 4-row buildings in the center of the building array. Figures 4.14 shows the contour plots of dimensionless power density (with the unacceptable Iref region in white color) (a) beside the 4-row buildings over the horizontal x-y planes at z/H= 0.93, and (b) on the 4-row roofs along the vertical x-z planes in the middle of 4row buildings for the sharp/round corners and the urban layouts of λp = 0.09, 0.25, and 0.59. Detailed observations of close to building corners are provided as below: -

For the selected area beside the buildings at z/H= 0.93 (in Fig.4.14a): By decreasing λp from 0.59 to 0.09 for the sharp corners, we noticeably observe the detachment of separating flows from the sharp windward corner with the regions of higher PD and more unacceptable Iref. This event may be attributed to narrow street canyons for high urban densities, causing the breakdown of flow separation from upstream buildings to reattach to the sidewall surfaces of downstream buildings. For the scenario of round corners, we visualize a considerable reduction in the deviation of the shear layer, as compared to the design of sharp corners for all urban densities. For the round corner, decreasing λp from 0.59 to 0.09 for the round corner layout is more evident to enhance PD/PDref from 0.2 to 0.6 around the windward and leeward round corners. The unacceptable Iref regions increase along with the windward round corner. Hence, the region around the leeward round corner near the sidewall can be

Chapter 4 the suitable mounting locations with high PD and acceptable Iref for wind energy harvesting. For the selected area on the roofs along the middle of 4-row buildings (Fig.4.14b): In the scenario of sharp corners, we observe the appreciable extension and separation of unacceptable Iref regions from the leading sharp roof corners because of localized and intense velocity gradients associated with sharply sheared flows occurred in the strong single-stage expansion process. However, the Iref results become relatively lower levels with diminished unacceptable Iref areas for the rounded corners owing to their reduced velocity gradients from the associated continuous-stage expansion processes. Similar findings were reported by Toja-Silva et al. [59] to highlight the significance of roof corner modifications in diminishing the unacceptable Iref region (see Section Conclusions in Ref. 7), especially on the rooftop. In decreasing λp from 0.59 to 0.09, the sharp corners are more exposed to flow separation from the leading sharp roof corners with higher PD and more unacceptable Iref regions. However, round corners of λp= 0.59 appear to be ineffective in increasing PD and minimizing the unacceptable Iref area. The round corners of λp = 0.25 start to decrease the extent of separated shear layer flows around the leading round corner with slightly increasing PD/PDref up to 0.3. When coming to λp= 0.09, the case of round corners shows a significant increase in PD/PDref up to 0.6 and contracting the unacceptable Iref region around the windward roof.

Urban wind energy potential: Impacts of urban density and layout

Figure 4.14. Predicted contour plots of dimensionless power density (with unacceptable turbulence region marked in white color) (a) beside the buildings over horizontal x-y planes at z/H= 0.93, and (b) on the roofs along vertical x-z planes in the middle of 4-row buildings for sharp/round corners and urban layouts of λp = 0.09, 0.25, and 0.59.

Chapter 4

Figure 4.15. Predicted profiles of wind power density and reference turbulence intensity along building length of two selected areas (a) beside the building and (b) on the roof for sharp/round corners at varied urban densities.

Figure 4.15 shows the predicted profiles of wind power density and reference turbulence intensity along the building length of two selected areas (a) beside the building and (b) on the roof for the sharp/round corners at varied urban densities. By comparing all the simulation results between sharp and round corner shapes at different urban densities, the selected area beside the building has a prescribed distance of 1.5 m beside the sidewall of 4-row buildings at the height of z/H= 0.93. In contrast, the selected area on the roof high This study has revealed the effectiveness of apposite modifications of urban morphology for lights the middle of the roof of 4-row buildings at the height of z/H=1.03. We can carefully examine the PD and Iref profiles against building length at the considering mounting sites for resolving the best installation locations. The observations are summed as below:

Urban wind energy potential: Impacts of urban density and layout

Figure 4.16. Predicted contour plots of (a) dimensionless velocity magnitude and (b) dimensionless power density (with unacceptable turbulence region marked in white color) over the horizontal x-y plane at z/H= 0.93 for sharp/round corners and staggered urban layouts of λp = 0.25, 0.5.

For the selected line beside the building (Fig.4.15a): The case with the round corners for λp = 0.09 shows the highest PD/PDref near the leeward round corners. The maximum values of PD/PDref for the round corner shape are 201%, 55%, 36%, 4% and 12% higher than those of sharp corners for λp = 0.09, 0.25, 0.33, 0,48, and 0.59, respectively. Only for λp = 0.79, the maximum PD/PDref value for the round corner is 11% lower than that of the sharp corner. From the Iref profiles at λp= 0.09, the round corner shape attains the acceptable turbulence level, while the wind environments with the sharp corners exceed the maximum allowable 0.16. For other urban densities of λp≥ 0.25, Iref values of the round corner are slightly higher than those of the sharp corner; nevertheless, the Iref results are in the satisfactory range. In addition, Iref profiles for λp ≥ 0.25 are primarily reduced with higher λp settings. For the selected line on the roofs (Fig.4.15b): The case with the round corner for λp = 0.09 has the highest PD/PDref near the windward and leeward round corners. The maximum PD/PDref values for round corners are 150% and 5% greater than those of sharp corners along the whole line for λp = 0.09 and 0.25, respectively. For these cases, we observe suitable

Chapter 4

Iref values (≦0.16) over the region for the round corner of λp = 0.09 and 0.25. Conversely, the maximum PD/PDref values for the round corner shape are lower than those of sharp corner by 16%, 11%, 44% and 1% for λp = 0.33, 0,48, 0.59, and 0.79, respectively. 4.4.3

Impact of urban layout

In this impact, two urban densities of 0.5 and 0.25 are considered, with high-rise buildings in the staggered pattern. Figure 4.16 shows the predicted contour plots of (a) dimensionless velocity magnitude and (b) dimensionless power density (with unacceptable turbulence region marked in white color) over the horizontal x-y plane at z/H= 0.93 for the sharp/round corners and staggered urban layout. First, we compare the velocity magnitude and power density results of the staggered urban layout with those of the in-line urban layout. For the medium urban layout (λp = 0.25), the predictions reveal that the staggered urban layout tends to decelerate the air velocities with relatively low wind power densities produced because of higher airflow resistance, as compared to the scenario of the in-line urban layout (as compared to Fig. 4.8a and 4.10a). Second, as depicted in Fig.4.16a, the infiltration wind speeds in the 2-row to 4-row for λp = 0.25 are higher than those for λp = 0.5 because of its reduced urban density and lower overlapping windward surface ratio, indicating a poorer ventilation efficiency for the staggered urban layout. Third, round corners show higher velocity magnitudes up to 5% and 15% than those of sharp corners for λp = 0.5 and 0.25 beside the building, respectively. In Fig.4.16b, the obstruction effect in the staggered high-rise buildings array can produce more significant areas of low power densities and high turbulence intensities than those of the in-line urban layout. First-row highrise buildings involve more than half windward surfaces in 2-row of buildings, revealing impinging airflows with high turbulence intensities appeared in the first three rows. To sum up, for the medium urban layout (λp= 0.25), the average flow velocity and power density of the staggered urban layout are 52% and 89% lower than those of the in-line urban layout, respectively. For the staggered urban layout, increasing λp from 0.25 to 0.5 results in the increases of V and PD by 101% and 325%, respectively. 4.4.4

Impact of wind direction

Figure 4.17 shows the predicted contour plots of dimensionless power density (with unacceptable turbulence region marked in white color) over the horizontal planes at z/H= 0.93 and 1.03 with the oblique wind direction θ of 45° for different building corner shapes. First, as compared to the wind direction of 0° for the sharp corner shape in Fig. 4.10ab, we notice more unacceptable turbulence areas and less high PD/PDref regions at the heights of z/H= 0.93 and 1.03 with θ= 45° for all urban densities. Second, for the oblique wind direction θ= 45° at z/H of 0.93 in Fig.4.17a, declining λp can increase the unacceptable turbulence areas for both sharp and round corners, respectively. Third, for the oblique wind direction θ of 45 at z/H= 1.03 in Fig.4.17b, rounded corners show excellent PD/PDref outcomes up to 0.6 with appropriate Iref appearing over the roof of the center 22 building array. The associated power densities are 268% greater than those of the sharp corner layout. For λp= 0.09, round corners also achieve a higher PD/PDref up to 0.38 than the sharp corners. Thus, round corners still have better wind power densities than those of sharp corners with an oblique wind direction of 45°.

Urban wind energy potential: Impacts of urban density and layout

Figure 4.17. Predicted contour plots of dimensionless power density (with unacceptable turbulence region marked in white color) over horizontal x-y planes at (a) z/H= 0.93 and (b) 1.03 with wind direction of 45° for sharp/round corners and urban layouts of λp = 0.09, 0.25, 0.76.

4.5

Discussion

As the world strives to develop the sustainable purposes of combating against climate change and providing renewable energy sources for all, it is crucial to investigate the feasibility and environmental effects of urban wind energy during their developmental stages. By 2050 it's projected that more than two-thirds of the world population will live in cities with denser and more compact urban layouts [155]. Hence, compact city planning has recently attracted much attention as one of the measures for sustainable urban development. As exponential growth of

Chapter 4

high-rise buildings combined with mass urbanization, the high-rise urban areas as the future trend still lack the knowledge-based expert technique for the preliminary estimation of urban wind power potential at promising locations for turbine installation. To attain a better understanding of the innovative development strategies of urban morphology for enhancing wind energy harvesting, this study complements the urban wind researches by filling the gap between research, development, and implementation, especially in compact high-rise urban areas. The obtained results can be a useful reference when contemplating the incorporation of wind energy exploitation into reality to generate the real application not only for urban development but also for practice guide of building designs. It is not just a matter of improving renewable wind energy and mitigating climate change with better urban ventilation. Meanwhile, the COVID-19 pandemic has emerged and forced humanity to pursue the path of energy sustainability in the post-COVID world. According to Elavarasan et al. [156] finding the significance of strategy immediately during this difficult time (see Section Journey towards SDG-7 in the post-COVID world in Ref. 63), they have concluded that increasing the preferences to install the solar and wind energy-based projects is one of priority measures highlighting the energy sustainability vision for progressing towards sustainable development goals, as defined by the United Nations. This investigation primarily applies different design strategies of urban morphologies to improve the urban wind energy potential in generic compact high-rise urban areas to put forward the future essential policy recommendations to the energy sector and the government for starting urban wind renewable development and driving the energy transition. Furthermore, the scope of this study aims to provide a numerical assessment of urban wind power in the vicinity of the possible turbine installation areas to facilitate the related researchers, engineers, policymakers, and investors in the future development of appropriate wind turbine installation strategies. The limitations of this study are illustrated as follows: (1)

This study considers the scenario of an array of uniform-height high-rise buildings as the most generic urban morphology. The impacts of non-uniform building height or unequal building arrangements (i.e., unequal street canyon widths) on wind energy harvesting are not yet taken into account. (2) This study primarily performs the analyses using the regular cuboid of high-rise buildings. The aspect ratios of cuboids will be varied to better select the most suitable building corner modifications in further investigations. (3) Steady-state CFD simulations are conducted at a fixed wind direction. Any possible airflow unsteadiness and variations in the intermittency and variability of wind speed and direction are not considered in detail. (4) Other determinants are strongly bearing on the urban wind environments, such as air quality, air pollutant dispersion, wind energy potential, heat island effect, which can be thoroughly examined for sustainable urban development. Hence, the effects of solar radiation, thermal buoyancy, and diurnal temperature in urban canyons on the wind field around generic high-rise building arrays are ignored in this study. (5) Before delving into the investigations of urban wind turbine installation, the selection of mounting locations and types of wind turbines must be identified first. Therefore, the size, number, layout, placement, capacity, performance, and expected maintenance of wind turbines are not taken into account during this stage of wind energy assessment. To address the above limitations for completing a comprehensive understanding of the outdoor wind environments in compact high-rise urban areas on enhancing urban wind energy

Urban wind energy potential: Impacts of urban density and layout

harvest, the future study can consider more urban morphologies, including various building geometries, non-uniform building heights, unequal aspect ratios of high-rise buildings, unequal building arrangements for sustainable urban development. In addition, after completion of the studies on the urban morphology effects and identification of the locations for turbine installation, the implementation of urban wind turbines will be considered in detail.

4.6

Conclusions

The parametric studies to inclusively evaluate various impacts of urban morphology on the wind power potential and turbulence intensity around the high-rise building array have been examined through the CFD simulations. These impacts are: (i) urban density, (ii) building corner shape, (iii) urban layout, and (iv) wind direction. The sensitivity analysis is accomplished to evaluate the prediction capabilities of five turbulence models compared against the wind tunnel measurement data. A reasonably good agreement between the simulations and experimental data demonstrates the present validated computational model having a dependable accuracy in predicting the airflow velocity and turbulence intensity fields for accurate estimation of urban wind energy potential. This study has revealed the effectiveness of appropriate modifications of urban morphology to significantly enhance wind power densities with reduced turbulence intensities for the high-rise building array. The findings can be summarized as follows: 



For the impact of urban density, reducing urban plan area density (λp) can diminish the unacceptable turbulence level areas with relatively higher wind power densities on the installable areas above the roofs. For other installation areas beside the buildings, it is noted that the maximum average wind power density with the acceptable average reference turbulence intensity appears at λp= 0.33. The most favorable urban density is found to accelerate local wind speeds and wind power densities. For the impact of building corner modification, round corners produce elevated wind power densities and low turbulence intensities over the downstream buildings, as compared to those of sharp corners. For the sparest urban layout (λp = 0.09), round corners develop the highest power densities results up to 201% and 150% than those of sharp corners for those selected areas beside the building and on the roof, respectively. For the impact of urban layout, the staggered pattern leads to poorer wind energy potentials with reduced wind power densities and escalated turbulence intensities than the in-line pattern. Nevertheless, for the staggered pattern, round corners present higher velocity magnitudes and power densities than those of sharp corners by up to 15% and 42% beside the building, respectively. For the impact of wind direction, an oblique wind direction of 45° produces more unacceptable turbulence level areas and less high power density regions for all urban densities, as compared to the wind direction of 0°. However, under the oblique wind direction of 45°, growing urban plan area density to the compact urban layout (λp = 0.76) can increase the power densities up to 268%, especially for the scenarios using the round corner shape."

Appendix See Table 4.A.1, 4.A.2, and 4.A.3. Table 4.A.1.Parameters and values of objective functions for CFD simulation cases A1 - A6.

Chapter 4

Case A1 λp= 0.76 W=0.15B In-line urban layout Sharp corner shape Wind direction of 0°

Case A2 λp= 0.59 W=0.3B In-line urban layout Sharp corner shape Wind direction of 0°

Case A3 λp= 0.48 W=0.45B In-line urban layout Sharp corner shape Wind direction of 0°

Case A4 λp= 0.33 W=0.75B In-line urban layout Sharp corner shape Wind direction of 0°

Case A5 λp= 0.25 W=B In-line urban layout Sharp corner shape Wind direction of 0°

Case A6 λp= 0.09 W=2.25B In-line urban layout Sharp corner shape Wind direction of 0°

Urban wind energy potential: Impacts of urban density and layout Table 4.A.2. Parameters and values of objective functions for CFD simulation cases B1 - B6.

Case B1 λp= 0.76 W=0.15B In-line urban layout Round corner shape Wind direction of 0°

Case B2 λp= 0.59 W=0.3B In-line urban layout Round corner shape Wind direction of 0°

Case B3 λp= 0.48 W=0.45B In-line urban layout Round corner shape Wind direction of 0°

Case B4 λp= 0.33 W=0.75B In-line urban layout Round corner shape Wind direction of 0°

Case B5 λp= 0.25 W=B In-line urban layout Round corner shape Wind direction of 0°

Case B6 λp= 0.09 W=2.25B In-line urban layout Round corner shape Wind direction of 0°

Chapter 4

Table 4.A.3. Parameters and values of objective functions for CFD simulation cases C1-C4.

Case C1 λp= 0.4 W=0.5B Staggered urban layout Sharp corner shape Wind direction of 0°

Case C4 λp= 0.25 W=B Staggered urban layout Round corner shape Wind direction of 0°

Case C2 λp= 0.25 W=B Staggered urban layout Sharp corner shape Wind direction of 0°

Case C3 λp= 0.4 W=0.5B Staggered urban layout Round corner shape Wind direction of 0°

Urban wind energy potential for a realistic high-rise urban area This chapter has been published as a peer-reviewed article in a scientific journal:

Numerical Assessments of Wind Power Potential and Installation Arrangements in Realistic Highly Urbanized Areas Juan, Y.H., Wen, C.Y., Chen, W.Y., Yang, A.S., Renewable and Sustainable Energy Reviews, 135 (2021), 110165 Abstract: Various wind resource assessment (WRA) methods have been applied to explore the feasibility of installing wind turbines for urban wind energy harvest. Nevertheless, there are only limited computational fluid dynamics (CFD) studies available to consider WRA around high-rise buildings in realistic urbanized areas. This paper presents a numerical assessment of urban wind energy potential, specifically pursuing to overcome the limitations of former studies by addressing the following points: i) conducting a large-scale wind power estimation by employing the meticulous topography in realistic compact high-rise urban area; ii) validating CFD simulations with the on-site measurements in two seasons; iii) obtaining the annual mean wind speed, wind power density and turbulence intensity between the existing high-rise building features, including building geometry, roof geometry, presence or absence of upstream obstacles, arrangements of integrated building complex and parallel high-rise buildings; iv) investigating the local installation locations of wind turbines and the distances from rooftop sidewalls or lowest mounting heights above rooftops with high power densities and acceptable turbulence intensities for wind energy harvest. The results of this wind resource assessment suggest an effective strategy of turbine installation for implementing urban wind power potential in a realistic compact high-rise urban area..

Keywords: Wind resource assessment; urban wind power; computational fluid dynamics; building-integrated wind energy harvesting; high-rise urban area Nomenclature E0 u* u*ABL u+ UABL

Empirical constant, 30.0 Friction velocity [m/s] ABL friction velocity [m/s] Dimensionless mean streamwise wind speed Mean inlet velocity of atmospheric boundary layer [m/s]

Chapter 5

98 z0 ε μ μt μeff ρ σk σε Cε1, Cε2 Cμ, gi I y* z0 k

κ L p P ui ABL BCT BIM CIM CKC CTBUH ES ET FSP GT HOMER IFC JH OIFC RANS SMO TI WPD WRA

5.1

Aerodynamic roughness length [m] Turbulent energy dissipation rate Dynamic viscosity Turbulent viscosity Effective viscosity Density Turbulent constant, 1.0 Turbulent constant, 1.3 Turbulent constant, 1.44 Turbulent constant, 1.92 Turbulent constant, 0.09 Gravitational acceleration in the i axis Average turbulence intensity Dimensionless wall distance Aerodynamic roughness length [m] Turbulent kinetic energy von Karman constant Turbulence length scale [m] Pressure Turbulent production term Velocity component in the i axis Atmospheric boundary layer Bank of China Tower Building information modeling City information modeling Cheung Kong Center Council on Tall Buildings and Urban Habitat Exchange Square Edinburgh Tower Four Seasons Place Gloucester Tower Hybrid Optimization Model for Electric Renewables International Finance Centre Jardine House One International Finance Centre Reynolds-averaged Navier–Stokes Survey and Mapping Office Turbulence intensity Wind power density Wind resource assessment

Introduction

Wind power, and associated harnessing technologies, have become an imperative part of the renewable energy industry and the move towards a sustainable economy [4, 157-159]. Power that can be generated by harvesting wind within urban environments (hereafter, ‘urban wind energy’) is a promising energy source. However, it is currently not exploited because the wind speed distributions around buildings are highly complicated with great turbulence intensities (TIs) [8, 9, 160], and no studies have attempted to determine the optimal locations for wind turbines in such environments. Severe turbulence can make it very difficult to capture good-quality wind.

Urban wind energy potential for a realistic high-rise urban area

However, at the same time, in complex, dense urban areas, disturbed flows around high-rise buildings tend to be locally accelerated, and in theory such areas of high airflow speeds could be exploited for power extraction. Indeed, buildings themselves can be purposely designed to augment such high-speed wind flow. Therefore, it is becoming increasingly necessary to be able to pinpoint suitable installation sites for deploying wind turbines in urban areas. Assessments to this end should consider the urban terrain in conjunction with local wind characteristics around buildings. Some general guidelines related to wind turbine deployment on buildings have been published, which include installing each turbine as high as possible [161], as close as possible to the center of the rooftop [161] or the sidewall between two adjacent buildings [78], and a sufficient distance away from other turbines [161], and the implementation of tall building-integrated wind turbines [150]. However, these conventional guidelines were established without considering the interference of surrounding structures and the airflow over an entire site. In our recent paper [17], the effects of adjoining constructions on the wind environment around the subject buildings may be important in resolving prospective mounting sites in convoluted urban terrains. It is also advantageous to determine the optimized hub height of wind turbine installation around the buildings for realizing better wind power capacity. There are a variety of methods for estimating the wind resources at a site, including the use of meteorological data [162-166], field measurements for measure-correlate-predict (MCP) methods [51, 167-169] or Sound Detection and Ranging (SODAR) [170], and modeled resource data [17, 171]. In order to appraise the available wind energy for power generation from micrometeorological data, Gunturu et al. [172] inspected the shape parameters for the Weibull velocity distribution of wind across the United States to identify the locations with high power densities; however, the Weibull model is merely an approximation of the wind speed, whose value is usually small, and thus the results of wind power density (WPD) cannot be accurate. Khan et al. [173] used Weibull analyses and the Hybrid Optimization Model for Electric Renewables (HOMER) to attain applicable hybrid energy systems in Newfoundland. Despite that, HOMER simulates a list of current technologies as available tools; it requires very detailed meteorological data and time to analyze specific energy sources. As mentioned above, a number of overly simplified methods via local micrometeorology data, the Weibull analysis or HOMER were conducted to evaluate wind resources, which cannot be utilized to establish the prospective sites for turbine installation. Normally, the long-term meteorological wind data are few or don’t exist at 50+ meters above the ground level for a particular area referred to as observed data sets. Even the wind data from the nearest weather stations or locale airports can be still far away from the target site, which may produce significant errors in wind energy estimation in complex terrains. Thus, it requires that the field measurement must be recent enough in order to describe better the current wind conditions [174]. Ideally, long time field measurements at the target site are considered the most reliable but can be costly and time consuming, taking up to over a 10year period [174, 175] or a least for one year [176] to collect data. One alternative way to low-cost wind resource assessment is to conduct short-term measurements at a prespecified location with MCP approaches for at least three months [177-179]. The gathered short-term data can be correlated with these measurements with an overlapping time series of another site utilizing the statistical models. Veeramachaneni et al. [177] collected the wind data as minimal data as possible for wind resource assessment. The minimal data of 3 months is still available to attain the good capability of accurate estimations of wind power. Weekes and Tomlin [178] carried out 3 months on-site wind measurements combined with MCP-predicted wind resources and accurately predicted the available wind power density at three viable sites. Bechrakis et al. [179] conducted the study offering a reliable indication of the annual wind energy potential of a target area using only short time period concurrent measurements (1-month from target site and 2-month from

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reference site) to quickly and satisfactorily estimate the energy yield. However, none of the aforementioned results employed any topographical detail in wind energy assessments. Considering the serious deficiencies of those simplified methods, the influences of realistic urban configuration and arrangement on the fields of wind power density and turbulence level in a specific area is therefore an imperative determinant of the most favorable locations and installation heights of wind turbines. In summary, all methods currently available for wind resource assessment (WRA) face major hurdles in practical application. The main challenges are summarized below [180]: a) Since wind is a type of weather phenomena, there is inherent uncertainty in forecasting the long-term wind resource. b) It is lack of high-quality, long-term meteorological measurements at enough height in areas of particular interest. c) For the long-standing average records, such as on a monthly or yearly basis, WRA can underestimate the WPD because higher speed records that contribute the most to calculated wind power densities tend to be smoothed out. d) Wind speeds can be affected by the terrain and building shapes, leading to an approximate deviation of ±10% between the measurements and actual turbine installation sites. e) WRAs require sufficient, critical data on wind velocity distributions, wind direction and turbulence intensity, to resolve or adjust the installation site and height. f) The accuracy, location, and even orientation of measurement instruments can affect wind measurement data and validation results. The task of modeling urban wind motion is difficult in essence. Each urban environment with particular surface roughness describing the height change of building blocks has its local characteristics, and this affects the airflow circulation in and around it and thus the harvestable wind resources. Computer modeling provides large-scale numerical meteorological models produced to extrapolate wind states at a specified site from the chronological data. It validates the detailed wind field at the site via a combination of on-site wind measurements coupled with computer models from long-lasting weather data. Without collecting the microclimate data from the CFD-based analysis for the definite locations, it may cause serious miscalculations to assess the wind power from the weather conditions [181, 182]. CFD simulations can be used to optimize turbine orientation, building designs, and installation locations. In addition, it can be incorporated into models of airflow across urban open spaces, which is known as building information modeling (BIM) or city information modeling (CIM) at a city scale. In earlier researches, CFD simulations have been shown to be useful for obtaining detailed urban characteristics employing fine-scale topography of computational models. Besides, to reflect the actual complexity of the problem, field measurements are needed to validate against the results of wind tunnel experiment and CFD simulation for urban environments [183]. However, with the major attention on evaluating urban wind power, the studies of examining the CFD models by field measurements [71, 77] are rarer than those by wind tunnel experiments [36, 37, 64-66, 75, 79, 109]. The wind features around buildings can be substantially influenced by complicated urban morphologies. Accordingly, prior studies have struggled to produce realistic results [75]. Some studies have assessed wind power using wind power with simple generic architect models, thereby neglecting the complex structures of an urban city, and have analyzed potential wind power based on interactions between airflow and a single building [8, 64, 65, 70, 72, 184, 185] or several parallel buildings [66, 69, 78, 109] without considering the surrounding structures. Other studies have modeled cities with rectangular buildings of the same height [36, 37, 79] or with different types of roof shapes[36, 37, 79] and pitch angles [75, 185]. Some studies have explored the influence of generic buildings with various shapes on the wind power production [69, 70,

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101

186]. Few studies have addressed wind estimations in actual urban areas. Those that have are summarized in Table 5.1. Dilimulati et al. [53] modeled a study site as a 500 m radius open area surrounding a commercial region to probe the performance of various types of various wind turbine designs and quantify the urban wind resources. Wang et al. [187] performed CFD simulations to evaluate the utilization of wind energy with turbines mounted on the roof on a campus. Wang et al. [188] compared and analyzed the wind resource characteristics of 7 varied urban tissues to estimate the wind power potential. Zhou et al. [189] simulated the wind across identified buildings from an urban residential community to explore the available wind energy resource. Yang et al. [17] performed the on-site full-scale measurements coupled with CFD simulations to resolve the power densities and turbulence intensities of the integrated technology complex building (ITCB) with a surrounding area of 500 m radius to evaluate the wind energy. Chaudhry et al. [69] analyzed the wind distribution around the Bahrain Trade Centre, the first wind turbines integrated skyscraper in the world, to determine the power generation of this highrise building. Lu et al. [70] applied the concentration effects to enhance wind energy at different heights of a high-rise building on campus. Taking several representative urban district categories into account, Beller [190] examined the inherent airflow phenomena around obstacles to inspect wind energy potential. Kalmikov et al. [191] carried out the advanced simulation with the complicated urban terrain to appraise the actual wind resources on campus. For the aforementioned CFD studies of wind resource assessment in realistic urban areas, it is common to find the situations lacking to include the multiple effects together. For instance, most of the publications present the wind resource assessment, but: 1) simply discuss the wind speed without considering the effect of the turbulence intensity; 2) only estimate wind power of several simplified buildings and hardly contemplate the associated surrounding area; 3) merely perform simulations without any measurement to validate the accuracy of CFD predictions. Hence, this study considers the surrounding area with a radius of 1 km, including all details of actual highrise buildings; accordingly, the wind flow can be modeled with detailed topography and roughness. We also conduct more comprehensive on-site measurements in an actual urban environment to validate our CFD model, leading to reasonable WRAs for accurate estimates of annual energy production. Table 5.1. Literature review of computational studies carried on wind resource assessment in realistic urban layouts

Publication

Ref.

Configuration

Dilimulati et al. (2018)

[53]

3D / Realistic high-rise urban area 3D/ A commercial area

Wang et al. (2018) Wang et al. (2018) Zhou et al. (2017)

[187 ] [188 ] [189 ]

Soebiyan et al. (2017)

[192 ]

This study

3D/ Single simply building from a campus 3D/ 7 different urban tissues 3D/ Residential areas with the designed building shape in sample building(s) 3D/ A Campus consists of two buildings

Range of surrounding area Radius of 1000m Radius of 500m None Radius of 800m 1000m* 1000m 25,000 m2

Turbulence modeling

Validation

Steady RANS/ RLZ Steady RANS/ SST & RKE Steady RANS/ RKE No

Field

Steady RANS/ SKE

Wind tunnel Field Field

102

Chapter 5 Yang et al. [17] 3D/ A campus with Radius of Steady Field (2016) surrounding area 500m RANS/ RKE Chaudhry [77] 3D/ Two high-rise towers None Steady No et al. (2015) RANS/ SKE Lu et al. [70] 3D/ Two simplified buildings 200m*500m Steady No (2014) from studied campus RANS/ RNG Beller [190 3D/ Building configurations None Steady RANS Field (2011) ] from 4 different studied sites Kalmikov et [191 3D/ A campus with idealized None Steady RANS Field al. (2010) ] smoothed buildings SKE= Standard k-ɛ model; RNG= Renormalization group k-ɛ model; RLZ= Realizable k-ɛ model; SST =Shear Stress Transport k-ω model.

Wind turbines cannot be installed effectively without knowing the details of local wind characteristics [53], and previous studies on generic buildings or over-simplified urban areas may produce unreliable results due to the neglect of realistic situations of surrounding buildings. This paper aims to illustrate an improved method for evaluating wind resources in highly urbanized areas. We conducted the CFD simulations of the wind over high-rise buildings in Central, Hong Kong, to identify the places with higher wind speeds and lower turbulence levels, which are greatly affected by the intricate topography of surrounding high-rise buildings and urban layouts. The turbulent flow characteristics and wind speed distributions can be quite different from the free stream in the proximity of high-rise buildings. Accordingly, there is a need to verify CFD calculations of detailed flow characteristics with the on-site measured data. The wind measurement and CFD simulation results around the subject buildings can provide a much more accurate and detailed wind environment data, including the airflow velocity and turbulence distributions, to facilitate the feasibility analysis for installation of wind turbines on the rooftops of high-rise buildings in urbanized areas. This study conducts CFD validations via the simulations of two cases in the summertime and wintertime. The predicted wind flow velocity, direction, and turbulence intensity (TI) are validated with the on-site wind measurement results using ultrasonic anemometers and a heat ball probe. The WRA is primarily based on the CFD results by the yearly wind data. Considering the prevailing wind past the buildings in the highly urbanized areas to exploit the available wind resources, the distributions of WPD and TI are assessed at the favored apposite locations for turbine installation. This investigation also fully analyzes the impacts of the building geometry, the roof shape, and upstream obstacles of a single high-rise buildings, the gap distance between integrated building complexes and the deep street canyon of parallel high-rise buildings on wind power characteristics. The outcomes of local WRA can be used to suggest the probable mounting sites and orientations for the wind power development and maximum use of urban wind energy in highly urbanized areas. Various market and technical factors, consisting of the government support policy, environmental effect/urban planning, market demand condition, financial mechanism, geographical influence and wind turbine efficiency, can significantly affect the long-term development and competitiveness of urban wind power applications. Without analyzing the aforementioned factors, this investigation mainly applied the detailed topography and boundary conditions of an urban microenvironment to improve the accuracy of estimating wind power potential in realistic highly urbanized areas to put forward for the sustainable development of future essential policy recommendations. The remainder of the paper is divided into six sections. Previous studies of wind energy resources in Hong Kong are described in Section 5.2., as are the details of local meteorological data of the study site. Section 5.3. introduces the on-site wind measurements utilized for CFD validation. Section 5.4. reports the features of a complex urban topography are considered in the

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numerical model with the computational domain and grid setup, governing equations and boundary conditions employed in CFD calculations. Section 5.5. reports the validation results of the CFD simulations, the WRA indices, and wind power estimations. Limitations and recommendations further work are discussed in Section 5.6., and conclusions are given in Section 5.7. CFD validation study

5.2 5.2.1

Studied area - highly urbanized areas of Hong Kong Wind energy resources in Hong Kong

To perform the WRA of a realistic highly urbanized areas, we selected the world-famous, dense skyscrapers in Hong Kong as an illustrative case. As stated by the Council on Tall Buildings and Urban Habitat (CTBUH), Hong Kong has over 9,000 high- rise buildings, with more than 1,500 skyscrapers standing taller than 100 m and 350 buildings over 150 m. Wong & Kwan (2002) [193] reported the annual average WPD of 13 weather stations in Hong Kong from 1994 to 2000. Only six sites were evaluated as appropriate for wind energy development. Except for a station at Central Plaza in the urban area, all of these were located either on outlying islands or hilltops. Of these 13 weather stations, that at Central Plaza is at the third-highest elevation above sea level, suggesting a high wind potential energy, not only there but above Hong Kong in general. However, it is extremely difficult to investigate these interactive and local wind flow effects characterized by densely populated high-rise buildings in urban areas. This study explores the feasibility of wind power harvest above an area in Central, Hong Kong). Figure 5.1. exhibits the study region within a radius of 1 km around the International Finance Centre (IFC) in Central, Hong Kong. The IFC stands at a height of 415 m, making it is the second tallest building in Hong Kong and the ninth tallest building in the world.

Figure 5.1. (a) Studied region (Source: Google Maps) and (b) photo in Central, Hong Kong (Source: Wikipedia)

5.2.2

Local Meteorological Data

To acquire reliable meteorological data for subsequent analyses, it is imperative to select local meteorological stations with sufficient data from the longest period of time to ensure the temporal homogeneity of data variations [194]. To this end, we used data from the Central Pier (22°17'20"N 114°09'21"E) automatic weather station, which has a 14-year history of climate data, including the wind speed and wind direction. Figure 5.2. illustrates the wind roses in 16 sectors for yearly,

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summer and winter periods at Central Pier weather station from 2006 to 2018. The location Central Pier weather station is showed in Fig. 5.3., while the elevation of the anemometer of 30m above the mean sea level. Wind roses summarize the occurrence of winds at the location - Central Pier for each directional sector, listing the absolute frequencies of the viewed wind speeds based on the wind data for 12-year period. The wind rose with yearly meteorological data reveals the wind blowing from east at a speed of 3.46 m/s. In summertime, the east and west wind are both frequent; however, the wind direction is more regularly from the east about 45% of the time, as compared to the wind from the west (about 35%). In addition, the average wind speed in summer is approximately 2.89 m/s. During the winter period, the wind direction is obviously the east with a wind speed of around 3.85 m/s.

Figure 5.2. Wind rose charts for (a) yearly, (b) summer and (c) winter periods in Central Pier weather station from 2006 to 2018

5.3

On-site wind measurements

The wind field measurements have been conducted for the validation of the simulation models in both winter and summer periods. On-site wind field measurements for the validation of simulations were taken twice (July 14-20 and November 12-18, 2017). Then, to obtain threedimensional (3D) wind data, we compared the measured wind speed, wind direction and TI to CFD predictions based on the boundary conditions from the local meteorological data. The validated simulation model was then used to accurately forecast the local wind flow features and turbulence levels to estimate wind power generation. Figure 5.3. shows the locations of 27 and 16 data collection points for the wind velocity/direction and TI, respectively. The strategy of measurement point selection is according to the following reasons: 1) based on the prevailing wind direction from the local meteorological data to determine the measurement points on the windward side over the study area without upstream obstacles; 2) based on the pre-analysis of CFD predictions to arrange the measurement points on the leeward side of those representative high-rise buildings to observe the formation of wake wind zones involving the complex flow phenomena. Since IFC is the highest building in the study region, we primarily select the measurement points within a radius of 1 km surrounding the main high-rise building (IFC), particularly at the 4-floor podium roof of IFC in conjunction with the area near Victoria Harbor

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105

on the windward side and also some on the leeward sides of IFC for surveying the wind field. In addition, considering the safety and convenience of deploying the measurement instruments, other measurement points are located at the pedestrian level around those concerned buildings or near the port, on the pedestrian bridges, on the outdoor covered escalator (i.e. Central–MidLevels escalator and walkway system), and on some open spaces of parks and terraces at high altitudes over the local uphill terrain. To complete the wind characterization in Central, Hong Kong, the locations of measurement points are not only widely selected on both the windward and leeward sides of the studied area but also involving the altitude differences of measurement points. In practice, the values of wind velocity and wind direction were obtained by three sets of WindMaster 3D ultrasonic anemometers (Gill Instruments), while TI was measured using a set of digital multi-parameter system (Testo 480 climate meter and Testo 0635 1050 heat ball probe), as shown in Fig 5.3. We used one ultrasonic anemometer at a fixed position (Point R) for a period of 12 h (7:00 to 19:00 h) as a reference point. Two other mobile ultrasonic anemometers and one set of Testo 0635 1543 were used at the rest of monitoring positions for at least 2 h with a sampling rate of 1 Hz. The WindMaster 3D sonic anemometers can measure the velocity and direction with the accuracies of <1.5% RMS/2o and resolutions of 0.01 m/s/0.1o. The corresponding dynamic range is 0-45 m/s for available wind speed measurements. This study also linked the thermal flow velocity probes with the climate meter to measure the turbulence level, indicating the air velocity fluctuation and intensity of airflow according to EN 13779 [195]. The accuracy of flow velocity is ±0.03 m/s in the measurement range of 0 to +10 m/s.

Figure 5.3. (a) Locations of 27 and 16 data collection points for wind speed and turbulence intensity; Photos of measurement instruments for (b) 3D sonic anemometer and (c) professional climate instrument kit.

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5.4 5.4.1

CFD simulations Computational domain and grid

This study sets up a large-scale accurate 3D numerical model realistically to implement the meticulous topography and architectural structures of a complicated high-density urban area in our study site for CFD simulations, as depicted in Fig. 5.4a. Moreover, Fig. 5.4b. illustrates the domain was divided into two major sectors: the central area around IFC and the surrounding area involving the main core of the urban site. Building shapes and the terrain were specifically generated by reproducing their dimensions and curvature details with 3D contour maps from the Survey and Mapping Office (SMO) of the Lands Department of Hong Kong. In practice, the domain was arranged using the maximum width (W) of the study area and the maximum height (H) of the subject building (IFC). For the extent of the computational domain, the inlet, top, lateral and outlet boundaries are laid at the distances of 1.5W, 5H, 4W and 15H away from the closest part of the studied area at each side, respectively. Moreover, we used the symmetric boundary conditions to prescribe the zero normal gradients of velocity component and all other flow variables at the side and top boundaries. All of these arrangements meet the requirements of the best CFD guidelines [196]. Figure 5.4c. illustrates the computational grids over the urban and ground surface. It is imperative to apply an enhanced grid-generation method for such a complicated model to improve mesh quality and achieve predictions of the wind field that are more accurate. Therefore, a high-resolution mesh system was utilized in the proximity of high-rise buildings and a relatively lower resolution mesh setup was arranged at a far distance away from buildings. Using a total number of 71,109,937 cells, this study has employed tetrahedral elements and hexahedral cells to fit the intricate building geometries and boundaries, enhance numerical accuracy, and avoid convergence problems. The expansion ratio of the grid was limited to 1.05-1.1 over the entire domain, with at least 20 cells along each edge of buildings. In the grid-sensitivity analysis, this study conducts a comparison of the predicted velocity magnitudes at 27 monitored points (as same locations of the field measurement points) to investigate the sensitivity of simulation results (in Fig. 5.5.) for three different grid systems having the total numbers of 59,258,284, 71,109,937, and 90,309,618 cells. The highest differentiation of velocity magnitude is observed up to 32.7% between 59,258,284 and 90,309,618 cells at Point Q, whereas the associated discrepancy of the calculations is lessened to 6.2% for 71,10,937 and 90,309,618 grids. Therefore, the mesh setup of 71,109,937 cells is selected to meet the both needs of proper calculating time and simulating quality in this study.

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Figure 5.4. 3D numerical models of (a) complex high-density architectural forms and (b) computation domain comprising urban characteristic of studied site (c) computational grids on the building surfaces and parts of ground surface.

5.4.2

Governing equations and boundary conditions

The theoretical formulation is based on the steady-state three-dimensional conservation of mass and momentum equations for simulating incompressible isothermal turbulent flows. CFD simulations were thus conducted to resolve the wind flow phenomenon for investigating the interacting physics of the airflow with buildings. A numerical formulation developed in ANSYS/Fluent® software (Release 19.0) was used to emulate the terrestrial wind flow over the Central, Hong Kong [197]. The governing equations are given as follows: ∇∙𝑉 =0

(5.1)

Chapter 5

108 V ∙ ∇V = −

∇𝑝 𝜌

(5.2)

+ ∇ ∙ [(𝑣 + 𝑣𝑡 )∇𝑉] + 𝑔

where V = velocity vector; p= pressure; ρ= density; ν= laminar kinematic viscosity; νt= turbulent kinematic viscosity; g = gravitational acceleration in Eqs. (5.1) and (5.2), respectively.

Figure 5.5. Predicted wind speeds at 27 data collection points from Fig. 5.3. for grid-sensitivity analysis.

Following our earlier study [136], simulations using steady-state Reynolds-averaged Navier– Stokes (RANS) equations were conducted and validated with the wind tunnel measurements. To achieve accurate simulations, three different turbulence models were tested. Summarily, the realizable k-ε model has demonstrated its superior performance of the prediction capabilities (in terms of the magnitude and direction of the wind velocity) over two other conventional highReynolds number k-ε turbulence models, i.e. the standard k-ε and RNG k-ε models, in view of the naturally complex flow phenomenon associated with Central, Hong Kong. The realizable k-ε model for turbulence closure is shown as follows: ∇ ∙ (kV) = ∇ ∙ [(𝑣 + 𝑣

𝑣𝑡 𝜎𝑘

) ∇k] + 𝑣𝑡 𝑆 2 − 𝜀

(5.3)

𝜀2

(5.4)

∇ ∙ (εV) = ∇ ∙ [(𝑣 + 𝜎𝑡 ) ∇ε] + 𝐶1 𝑆𝜀 − 𝐶2 𝑘+√𝑣𝜀 𝜀

where the turbulent kinematic viscosity term, νt, is calculated using Cμ k2/ε, k and ε are the turbulence kinetic energy dissipation rate and turbulence kinetic energy generation rate, 0.5

respectively; and Cμ is a turbulent constant. In addition, the variable S = (2𝑆𝑖𝑗 𝑆𝑖𝑗 ) and 𝑆𝑖𝑗 = 0.5(𝜕𝑢𝑖 /𝜕𝑥𝑗 + 𝜕𝑢𝑗 /𝜕𝑥𝑖 ) . In Eq. (5.4), the factor 𝐶1 = 𝑚𝑎𝑥[0.43, 𝜂/(𝜂 + 5)] and 𝜂 = 𝑆(𝑘/𝜀) . The other associated constants are given as C2 = 1.9, σk= 1.0, and σε= 1.2 [92, 198]. With an annual mean temperature of 22.2oC in Central, Hong Kong, the corresponding air viscosity is 1.188 kg/m3 with air density of 1.841×10-5 N-s/m2. As indicated by the wind rose diagrams in Fig. 5.2., the prevailing annual mean speed is 3.46 m/s at the reference height of 30 m from the east throughout the entire year as the inlet boundary

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condition for estimating the annual wind power potential. For the CFD validations with on-site measurements in both summertime and wintertime, this study essentially considers the highest probability of wind conditions, corresponding to the mean speeds of 2.89 m/s from the east for the summertime and 3.85 m/s from the east for wintertime periods, respectively. All the reference wind data are applied to determine the neutral atmospheric boundary layer (ABL) profiles of the velocity, turbulent kinetic energy and turbulence dissipation rate, as the incoming airflow conditions specified at the inlet of the computational domain [49, 50]. 𝑈𝐴𝐵𝐿 = 𝑘=

∗ 𝑢𝐴𝐵𝐿

𝜅

∗ 𝑢𝐴𝐵𝐿

ln (

𝑧0

)

(5.5) (5.6)

√𝐶𝑢 𝑢∗

𝑧+𝑧0

𝐴𝐵𝐿 𝜀 = 𝜅(𝑧+𝑧

(5.7)

The symbol u*ABL represents the ABL friction velocity expressed by the wind velocity Uh at the reference height of h: 𝑈𝐴𝐵𝐿 =

𝜅𝑈ℎ ln(

ℎ+𝑧0 ) 𝑧0

(5.8)

where z0 is the aerodynamic roughness length and K is a von Karman constant (= 0.42). In essence, the drag from the surface of terrain increases with roughness length. In addition, it is inappropriate to implement the logarithmic law for the velocity profile, which is based on the standard wall function method [196]. CFD simulations can model the influence of standing barriers on the wind field using a roughness wall function to simulate the drag from obstacles imposed on the bottom plane. From an updated Davenport roughness classification for the city center comprising mixed-use residential and commercial buildings with low/medium/ high rise buildings[199], the aerodynamic roughness length of our study site is 1 m. The outlet specifies an ambient pressure of 1 atm. The solid surface carries out zero normal gradients of p, k and ε, with surface grids near the buildings treated by the log-law wall functions [196]. Besides, symmetric boundary conditions are used to prescribe the zero normal derivatives and zero normal velocity component for all flow variables at the top and side boundaries. Therefore, we employed the least square cell-based approach for gradient evaluation in conjunction with the second-order upwind scheme to solve the momentum, pressure, turbulent kinetic energy and turbulent dissipation rate. By using the algorithm of semi-implicit method of iterative and pressure-linked equations consistent (SIMPLEC), the solution process can settle velocity-pressure coupling [200, 201]. Moreover, the convergence of the normalized residual errors and a mass balance check are important to meet the criteria of steady solutions. To resolve the wind environments, the airflow variables (u, v, w, p, k and ε) need to converge below the value of 10-5 and the mass balance check must be lower than 1%.

Chapter 5

110

5.5 5.5.1

Results and discussion Comparison of CFD predictions with field-measurement results

The accuracy of the predicted flow field must be verified before establishing reasonable estimates of the wind power production. To this end, we compared the simulated results from both summer (July) and winter (November) periods with the associated on-site measurement

Figure 5.6. Comparison of CFD predictions with measured (a-b) wind speeds, (c-d) wind directions, and (ef) turbulence intensities in the summer and winter periods.

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data. Figure 5.6a-b. present a comparison of the CFD predictions with the measurement wind speeds (as the averaged values over the recorded period). The error lines of ±10% and ±20% are also included. There are 27 monitoring points on the charts for the validation study in summer and winter. Overall, the predicted wind speeds agreed reasonably well with the measured data at varied monitoring locations with maximum discrepancies within 20%. The calculated wind speeds at the windward positions (i.e., Points A, B, R, S, Q, T and U) were in good agreement with the measured data. Meanwhile, as the wind is notably blocked and disturbed by structures, the wind speeds in the leeward region (i.e. Points C and E) were broadly lower than 1.5 m/s, indicating differences between simulations with measurement results up to around 20% (Point H). Figure 5.6c-d. demonstrate a comparison of the CFD predictions with the measured wind directions in summertime and wintertime. Most of the measured data points denoted the averaged values during the recorded period. The results suggest the moderate agreement of the simulations with experimental data, having the greatest deviations below 20%. Except for Point P, O, and N located in proximity to the rooftop in the leeward region, revealing a maximum variation of 19%. Additionally, Figure 5.6e-f. illustrates a fair agreement between the CFDpredicted and measured TI in summer and winter with the deviations around 20%. However, for the winter period, we over-predict the turbulence intensities on the leeward side at Points W and J by around 22% greater than those of the measured data. Notably, because is only one set of digital multi-parameter system available to measure the turbulence intensity, there are 16 points on the charts for the validation. Overall, the validation results reveal that the simulations were reasonably consistent with the measurements.

5.5.2

Wind resource assessment indices: power density and turbulence intensity

The most common and useful indices in WRA are the WPD and TI. The objective of wind energy optimization is to maximize WPD and minimize TI. WPD is an indicator of the wind power density of the proposed site over a certain period (i.e., a month, or several years) measured in units of W/m2. WPD is expressed as: 1 ̅̅̅̅ 3 𝑊𝑃𝐷 = 2 𝜌𝑈

(9)

Here the overbar denotes the time average. The variable of air density (kg/m3) depends on pressure and temperature, and can vary by 10% to 15% seasonally. The classification of WPD is extracted from Elliott et al [202] corresponding to the respective anemometer heights. In essence, a WPD value less than 100 W/m2 (Class I) is generally unsuitable for wind power development. The WPD ranges for Class II and III are 100-150 W/m2 and 150-200 W/m2, suggesting the grades of moderate and good as the resource ranking, respectively. Class IV and above with WPD ≥ 200 w/m2 are excellent for the wind energy potential.The wind TI is defined as: 2 3

√ 𝑘

𝐼=

𝑈

(5.10)

In Eq.(5.10), the symbols k and U are the turbulent kinetic energy and the mean flow velocity, respectively. In practice, TI has a substantial influence on the operability and lifespan of wind turbines. Based on the International Electrotechnical Commission Standard IEC61400-2 [203], a wind TI greater than 16–18% should be avoided in the operations of small wind turbines.

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112

Figure 5.7. Predicted wind power density contours at z= 50m, 100m, 150m, 200m, 250m and 300m for selected locations in Central, Hong Kong.

5.5.3

Wind power estimation

This study adopts the CFD analysis as a useful tool to propose the associated mounting locations of installed wind turbines and evaluate the available wind power production in an existing highly urbanized area. Considering the effects of wind microclimate and urban roughness conditions, we performed the wind-environment simulations on a yearly basis to predict airflow fields. Figure 5.7. shows the predicted WPD contours at z= 50m, 100m, 150m, 200m, 250m, and 300m for those selected locations in Central, Hong Kong. Using the results of macro-siting analyses, we pinpointed particular high-rise buildings with elevated power densities, as compared to other constructions and use these sites to assess wind energy resource available, as presented in Fig. 5.8. As the wind flows across a building, we can clearly observe the development of a low-speed wake flow region behind the subject building. At the same time, the

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113

prevailing wind flowing over the rooftops of high-rise buildings can achieve elevated WPDs through flow expansion around the edges of buildings. To conduct further analysis, we categorized the building forms into three types: single high-rise buildings, integrated building complexes, and parallel high-rise buildings to explore their wind power characteristics.

Figure 5.8. Selected locations with high-rise building forms having elevated power densities for wind energy assessment at z= 150m in Central, Hong Kong.

5.5.3.1

Impact of building geometry

Figure 5.9. illustrates the predicted contours of WPD and TI in the horizontal cross-sections at different heights for single high-rise buildings S1-S4, as shown in Table 5.2. We clearly observe the best wind resources from the squared-plant building with fillet edges of the selected building S1- IFC. As the tallest building in the studied site, the IFC with a height of 415 m has great potential for wind power generation. The predicted results reveal that the maximum WPD exceeded 400 W/m2, which is classified as the excellent wind energy potential. Places with both high power densities and low turbulence levels appear at the side corners of the IFC because of the effects of expansion corner flows. In contract, regions of stagnant and wake flows with the least power density values less than 100W/m2 and high turbulence levels are located on the windward and leeward sides of the building. From the viewpoint of wind resource exploitation, we choose both side walls of the building, having WPD above 400 W/m2 and acceptable TI range, as the recommended mounting sites of wind turbines. Notably, because these 4 selected horizontal sections are at high altitudes (close to the rooftops), they share similar freestream conditions.

114

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Table 5.2. lists the design features (including the building height, building geometry, roof shape, and with or without upstream obstacles) of five selected buildings in the single high-rise building category in Central, Hong Kong. No. Building name H Building geometry Roof shape Upstream (m) obstacle S1 International Finance 415 Cuboid with fillet edges Doom-like roof No Centre (IFC) S2 The Centre 292 Octagonal star-shaped Pyramid-like roof Yes with shape edges with stepped-shape S3 Bank of China Tower 315 Composed four triangular Prism-like roof Yes (BCT) shapes with varying heights S4 Cheung Kong Center 283 Cuboid with shape edges Flat roof Yes (CKC) S5 One International 210 Cuboid with fillet edges Doom-like roof Yes Finance Centre (OIFC)

At S2-The Centre, the star-shaped octagonal configuration (on the upper right side of Fig. 5.9.) causes low-speed turbulent flows in relatively small wake regions right behind the star-shaped edge of the walls. At a height of 282 m, the WPD value on the windward side of S2 can reach up to 300 W/m2 in front of star-shaped structures. However, the associated TI above 16% is out of acceptable range for wind turbine installation. For the selected building S3- Bank of China Tower (BCT), the distinct configuration involves four triangular towers with varied heights and prismlike facades. The upper part of S3 having the shape of a triangle produces the WPD values of 200400 W/m2 with acceptable turbulence levels resulting from the corner flow effect for the wind flow across its sharp edges (on the lower left side of Fig. 5.9.). As a typical cuboid-like architecture with shape edges, the wind runs separately from the facing tip on the windward corner to develop smooth airflows along the upper and lower side walls for the designated building S4Cheung Kong Center (CKC). We observe that for the rhombus shaped structure with the normal incoming wind direction, no flow separation occurs at two leadingedge sides and a high-speed region formed on the leeward side of the edge. Essentially, the wind resource in the vicinity of the building S4 can generate WPD of 300-400 W/m2 with TI below 16% around both side-corners. Table 5.3. Summary of building geometry impact on wind energy resources. Building Building shape Other design parameter configuration Zhou et al. [189] 3D/ Two buildings Cuboid Various wind direction without Cylinder surrounding area Half-cylinder Half-cylinder and cuboid (Best WPD) Kono et al. [204] 3D/ Single cuboid Cuboid Various wind direction; building without various building surrounding width/length=1, 0.5, 0.25 Chaudhry et al. 3D/ Two buildings Triangular [69] without Square surrounding area Circular (Best WPD) Benchmark Publication

The results are consistent with the findings of earlier studies performed for the generic buildings by Zhou et al. [189] and Chaudhry et al. [69] in Table 5.3. Zhou et al. [189] studied the

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115

wind velocity distributions of the cuboid, cylinder, and half-cylinder as well as half-cylinder and cuboid diffusers between two low-rise buildings with the low-speed inflows, and reported that half-cylinder and cuboid diffusers had the strongest wind amplification outcomes. Chaudhry et al. [69] tested the wind energy performance of different cross-sectional configurations of highrise buildings for the shapes of triangular, square, circular, and benchmark. The optimum crosssectional design to install wind turbines was the circular orientation. Taken together, these results confirmed that the building geometries with fillet edges or smooth corners achieve high power densities and low TIs.

Figure 5.9. Predicted contours of WPD and TI in horizontal cross sections at different heights for selected buildings of single high-rise S1-S4.

116

Chapter 5

Figure 5.10. Predicted profiles of wind power density and turbulence intensity against level distance from near-rooftop sidewalls at potential mounting sites for selected buildings of single high-rise S1-S4.

After selecting several promising locations with high WPD and low TI, we carefully examined the profiles of power density and TI against building height at the considering mounting sites for resolving the least installation heights from the near-rooftop sidewalls and/or above the roofs of buildings. Figure 5.10. presents the predicted profiles for these against the level distance (in Y’ axis) from near-rooftop sidewalls at the potential mounting sites for S1-S4. At S1, we notice satisfactory TIs (≦16%) over the region within Y’=10 m along S1-Y1. Similarly, the TI along S1– Y2 is acceptable at a distance (Y’) exceeding 1.5 m. The maximum power densities at each point are up to 700 and 500 W/m2, respectively, with a corresponding least installation distance of 3 m from near-rooftop sidewalls. For the case of S2, the nearest distances of appropriate mounting locations along S2-Y1 and S2-Y2 are 2.5 and 5.5 m from the near-roof sidewalls, where TIs are

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117

within 16%. Beyond the shortest distances for installing the wind turbines, along S2-Y1 and S2Y2 can generate the highest WPD values of almost 400 and 300 W/m2. The wind resources of S3 shows a similar trend as those of S2, with an almost constant WPD of more than 400 W/m2 along both S3–Y1 and S3–Y2 with accepted TIs at a least level distance of 3 and 5 m from the sidewalls. For the case of S4, TIs along S4-Y1 and S4-Y2 are both under 16% for a level distance within 10 m from the sidewalls of the near-rooftop. The highest WPD values along S4-Y1 and S4-Y2 can are 450 and nearly 500 W/m2, respectively, at a distance Y’ of 3 m.

Figure 5.11. Predicted contours of wind power density and turbulence intensity in vertical cross sections for selected buildings of single high-rise S1-S4.

Chapter 5

118 5.5.3.2

Impact of roof geometry

High-rises S1-S4 have different roof shapes, including the doom-like roof, pyramid-like roof, prism roof and flat roof. Figure 5.11. illustrates the predicted contours of WPD and TI in vertical cross sections of S1-S4. Overall, the airflow of the incoming wind splits at the leading edge of the roof, with a low-WPD region involving recirculating flows near the roof surface. The vertical component of wind velocity is nearly zero at the roof level. The remaining tangential velocity component develops a strong thin shear layer, and thereby causes high shear production and dissipation rates at the roof level. TIs are then higher near the leeward side of buildings than near the windward side of buildings due to stronger wind shears. For S1 (the IFC), the wind resource potential is of an excellent grade with the greatest power density exceeding 400 W/m2 around its domed roof. In the building design viewpoint, the curvilinear edge of the roof tends to continuously accelerate the airflow with a decrease in turbulence level. For wind resource exploitation, the installing sites of wind turbines are above the roof near the edge area on the windward corner to evade low wind velocities and intense TIs near the separation bubble (on the upper left side of Fig. 5.11.). The size of the separation region for the pyramid roof with stepped shape on the building S2 is appreciably smaller than that for the domed roof of S1. As reported by Oke [153], the wind flow appearing along stepped structures exhibits the skimming flow behavior (on the upper right side of Fig. 5.11.). The highest power density (>400W/m2) occurs at the tip and side rims of the roof. Moreover, the pyramid roof has an advantage of availability to install wind turbines for each transversally stepped plane. For the prism-like roof of S3-BCT, the recirculating flows resides over an extensive area of its oblique surface above the roof. It is noticed that the TI increases over 16% between the eave and edge, s making this area inappropriate for turbine installation. Besides, the shape of pyramid roof leads to the difficulties of mounting wind turbines on the windward rim of the rooftop, even though S3 is taller than other buildings surrounded. For S4, which is the CKC, almost no airflow separation appears over the windward plane of the rhombus-patterned flat roof, producing a WPD more than 400 W/m2 (on the lower left side of Fig. 5.11.). Nevertheless, the WPD value can be lower than 100 W/m2 in the wind wake, where the TI is above 16%, indicating its worthlessness for wind energy deployment. The airflow transition from the walls to the roofs of high-rise buildings has a strong effect on the wind power potential. It is clearly observed that a curved edge of the roof shows the best results of high WPDs and low TIs. Table 5.4 summarizes the effects of roof geometry on wind energy resource. Specifically, many researchers reported the best wind energy resources attained using the curved roof shape. In a realistic high-rise studied site, the simulation predicts the enhanced WPDs around the curved building-roof shape as S1. Besides, in accordance with the description by Ledo et al. (2011) [37], we note that TI above the pyramidal roof is generally well below the limit of 16%. Table 5.4. Summary of roof geometry impact on wind energy resource. Building Roof shape Other design configuration parameters Toja-Silva et al. 3D/ a cuboid Half-sphere roof Different variations of [59] Spherical roof geometrically spherical roof, roofintegrated with squaredwidth, exploration of plant the building aspect Spherical roof with a ratio, different heights cylindrical wall (Best WPD) for the surroundings Toja-Silva et al. 3D/ a cuboid Flat roof Roof edge [205] Shed roof Simple edge; Pitched roof Curved edge; Publication

Urban wind energy potential for a realistic high-rise urban area

Toja-Silva et al. [64]

3D/ a cuboid

Abohela et al. [36]

3D/ a cube

Ledo et al. [37]

3D/ 3x3 array of cubic

Flat

Spherical roof (Best WPD) Vaulted roof (Best WPD)

Flat roof Domed roof (Best WPD) Gabled roof Pyramidal roof Barrel (Best WPD) Vaulted roof Wedged roof Flat (Best WPD) Pitched and Pyramidal roofs

119 Railing; Cantilever. Various types of RANS turbulence models

Figure 5.12. demonstrates the predicted profiles of WPD and TI against height (in Z’ axis) above the rooftop at the potential mounting sites for selected single high-rise buildings S1-S4. For the case of S1, the results along S1-Z4 and S1-Z5 reveal the acceptable ranges of TI≦16%, with the highest WPD of both sites reaching 750 W/m2 within 2 m above the rooftop. The TI profiles along S1-Z1, S1-Z2 and S1-Z3 display similar acceptable TI, whereas the lowest heights of satisfactory installation locations at S1-Z1, S1-Z2, and S1-Z3 are Z’= 4, 2.5 and 2 m above the rooftop with the associated peak power densities of 500, 720, 300 W/m2, respectively. For the chosen building S2, the lowest heights of appropriate mounting locations along S2-Z1 and S2-Z2 are around 5 and 7 m above the rooftop where turbulence intensities meet the condition of maximum allowable 16%, with the WPD values up to 400 W/m2. Based on the results of S2-Z3 and S2-Z4, we observe the TI values above the rooftop are all in the appropriate range (≦16%) and it can yield almost 550 W/m2 as the highest WPD at a height Z’ of 3 and 5 m. For the profiles of S3, the lowest heights of mounting locations along S3-Z1 and S3-Z2 are 7 and 3 m above the rooftop with the peak WPD of around 400 and 500 W/m2. For the case of S4, besides S4-Z1 with the wind energy of low WPD and high TI, turbulence levels of others locations are under 16% for the lowest heights Z’ of 5.5 m above the rooftop, with the WPD values above 600 W/m2. 5.5.3.3

Impact of upstream obstacles

Upstream obstacles can substantially affect wind power production at a subject building. Figure 5.13. demonstrates the predicted contours of WPD and TI in the horizontal cross-sections at a fifth building, S5 (One International Finance Centre, OIFC; Fig. 5.8.), which is directly behind IFC. We obviously visualize the influence of the wake flow at the back of IFC on the wind environment around OIFC, showing breakdown of the expansion corner flow over both side corners of the OIFC due to the interference from the extended wake. As a result, the simulation reveals a significant decrease in wind speed downstream of the roof, causing the WPDs of the OIFC to fall below 200 W/m2 with TIs over 16%. In effect, the wind path toward S5 from the incident wind direction is blocked by the IFC. In the previous study, Yi and Li (2015) [206] also reported interference effects from surrounding buildings dependent on incident wind direction, which mainly led to a reduction in the mean wind loads.

120

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Figure 5.12. Predicted profiles of wind power density and turbulence intensity against height above rooftop at potential mounting sites for selected buildings of single high-rise S1-S4..

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121

Figure 5.13. Predicted contours of wind power density and turbulence intensity in vertical and horizontal cross sections for selected building of single high-rise S5. Table 5.5. Design features of selected integrated building complexes in Central, Hong Kong. No.

Building name

H (m)

G (m)

H/G

Exchange Square I&II

188/188

3.3

56.4

15°

160/160

25.83

172/186

7.5

205/165

2.9

I2 I3 I4

5.5.3.4

Gloucester Tower (GT) & Edinburgh Tower (ET) Lippo Centre Tower (LCT) I&II Four Season Place & Four Season Hotel

Building geometry Two rectangular parallelepipeds

Upstream obstacles

-5°

Two cuboids

Yes

45°

Two octagons

Yes

57.59

45°

Two arc structures

Yes

Impact of integrated building complex design

The design of integrated building complex generally requires to integrate two or multiple buildings together as a unified approach during the planning and design phase. It is common to see the arrangements of integrated building complexes in highly urbanized cities. This section assesses the effects of gap distance (G) on the urban wind energy potential of the such complexes.

122

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We deﬁne α as the angle between the vector of the wind (streamwise) direction and the vector along which the airflow passes through the gap. A narrow gap formed can lead to a high aspect ratio of the maximum height of the subject building to gap distance (H/G), inducing relatively high wind velocities through the gap. This study considers four integrated building complex models to estimate the wind energy resources, with the associated design features described in Table 5.5. Figure 5.14. shows the predicted contours of WPD and TI in the horizontal cross-sections of different heights at integrated building complexes (I1-I4). For the chosen integrated building complex I1- Exchange Square (ES) I&II, G is 3.3 m, and the buildings ES I and II form a divergent channel with an intersected angle of around 90°, which induces the “Venturi effect” for achieving better airflow acceleration through the gap at α= 15°. For the whole complex, the areas with high WPDs of 300-400 W/m2 appear both side corners and within the gap passage. By contrast, the areas of light/calm winds occur on both of its windward and leeward sides. Similarly, the selected integrated building complex I2- Gloucester Tower (GT) & Edinburgh Tower (ET) is also erected with two square-shaped cross sections at a diverging angle of 90° and an incoming flow angle α of -5° (almost streamwise wind direction). However, with a G of 6 m between two towers, the airflow is blocked by upstream structures, resulting in a very low WPD, as shown in Fig. 5.14. The complexes I3 and I4 also have similar building layouts with relatively narrow gap distances between buildings; however, the CFD predictions of α= 45° reveal the difficulties for flows to permeate into the passage between building complexes under highly oblique wind directions, resulting in WPDs (<100 W/m2) in the area. In addition, this outcome can be another solid illustration of deteriorating wind resources due to the obstruction from existing upstream obstacles to depreciate the effectiveness of wind direction in realistic urban areas. As mentioned previously in Table 5.4 of Section 5.5.3.1, Zhou et al. [189] and Chaudhry et al. [69] examined the effects of building geometry on the energy extraction of prevailing inlet wind between two symmetry buildings in close proximity, and reported that curved edges and small gap distances promote high WPDs. However, the simulation results for I1-I4 reveal that the impacts of upstream obstacles and incoming flow angle are very critical to the feasibility of implementing wind turbines over integrated building complexes. Furthermore, the optimum gap of integrated building complex can be used to avoid flow separation with a higher and more uniform WPD harvested in the gap passage.

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Figure 5.14. Predicted contours of WPD and TI in horizontal cross sections of different heights for selected integrated building complexes I1-I4.

Figure 5.15. presents the predicted profiles of WPD and TI against (a) level distance (in Y’ axis) from the rooftop sidewalls and (b) height (in Z’ axis) above the rooftops of potential mounting sites for I1-I4. This study considers the promising locations in the middle of the gap between those integrated building complexes. For the level distance of -10–10 m in front of the edge of rooftop

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sidewalls, the case of I1 shows the highest WPD value of 400 W/m2 as compared to those of I2, I3 and I4. Meanwhile, the maximum acceptable value (≦16%) appears in the level distance of at least 2 m (Y’= -2 m) ahead of rooftop sidewalls. On the other hand, for the vertical distance from -10 to 10 m along the center line (Z’= 0 corresponding to the roof height) between integrated buildings, the peak WPD (of 340 W/m2) of I1 is greater than those of I2, I3, and I4 at Z’≦-4m below the rooftop. For the area above the rooftop (Z’≧0), the associated wind resources of I3 have the highest WPD of 315 W/m2 as compared to others. Nevertheless, TI values of I2 and I3 are lower than the maximum acceptable value (≦16%) when Z’≧0.

Figure 5.15. Predicted profiles of wind power density and turbulence intensity against (a) the level distance from rooftop sidewalls and (b) the height above rooftops at potential mounting sites for selected integrated building complexes I1-I4.

5.5.3.5

Impact of parallel high-rise buildings

In the realistic highly urbanized areas, high-rise buildings and skyscrapers tend to be parallel to each other for creating human-built street canyons. To analyze the interference outcomes of parallel buildings on the wind energy potential, different building geometries and incoming wind incidence angles may affect the power generation capacity of wind turbines. Hence, the current study selects four parallel high-rise buildings to resolve the proper sites for mounting wind turbines in existing highly urbanized areas. The most important geometrical factor is the aspect ratio of the building height (H) to canyon width (W), H/W. A value of aspect ratio higher than 2 is normally classified as deep canyon [207], showing the same category for the selected parallel high-rise buildings in this study, to which a city with skyscrapers can be considered analogous.

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Table 5.6. Design features of selected parallel buildings in Central, Hong Kong.

No.

Building name

H (m)

W (m)

H/W

Upstream obstacles

P1 P2 P3 P4

OIFC & FSP BCT & CKC IFC & ES ES & Jardine House

210/205 315/283 415/188 188/178.5

70 52 143 42.35

2.93 5.44 1.31 4.21

-5° 85° -30° 60°

Yes Yes No No

Figure 5.16. presents the predicted contours of WPD and TI in the horizontal cross-sections for selected parallel buildings P1-P4 in Table 5.6. The predicted WPD field inside a deep canyon greatly depends on the incoming flow direction α with respect to the street orientation. When the incoming flow direction is parallel to the street (i.e., α = 0° for P1), channelization can be observed and the wind tends to be channeled and accelerated through the street canyon, further enhancing wind power potential. At P3, prevailing winds flow across the space between IFC and ES, and at P1 the same is true between Four Seasons Place (FSP) and OIFC. Because of the accelerated airflows by the merging of two streams of corner-expansion flows past facing sides of selected buildings (IFC and ES or FSP and OIFC), it then permeates into the street canyon to produce roughly symmetrical distributions of power density with high WPD ( ≧ 400 W/m2) and satisfactory TI values over the core regions on the leeward side between these high-rise buildings. In contrast, as the incoming flow direction is significantly slanting to the street canyon, as presented in the cases of designated parallel high-rise buildings CKC/BCT and ES/Jardine House (JH) (P2 and P4), the predicted WPD distributions can be appreciably different from the cases of P1 and P3. For instance, along the incoming wind direction, the airflow traverses the space between the buildings ES and JH obliquely for the case of P4, with the expansion flows appeared on both the north corner of JH and the south corner of ES. Accordingly, the resultant wind filed leads to the asymmetrical power density contours with the maximum value greater than 400W/m2 and the associated TI under 16%. At P2, the wind comes across the gap between two buildings at a large α (85°). Those high WPD areas are marked by light and dark blue areas located at the sharp convex corners on the windward side (z=273 m) of parallel buildings CKC/BCT. The airflow field shows a quite distinct pattern attributable to the unique shape of BCT. The TIs are below 16% over those high WPD areas on the windward side of parallel buildings.

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Figure 5. 16. Predicted contours of WPD and TI in horizontal cross sections for selected parallel buildings P1P4.

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We have discussed the predicted wind resources around IFC and CKC, as depicted in Fig. 5.9. and Fig. 5.11. Additionally, the CFD simulations indicate low wind power potential of OIFC due to the blockage by IFC. This section addresses the results of WPD and TI against level distance from the rooftop sidewalls for the building ES (P4), as illustrated in Fig. 5.17. A high WPD value of 800 W/m2 is found within a distance of 2 m from the edge of rooftop sidewalls along ES-Y2, with a suitable range of TI (≦16%). Besides, the power density reaches 600 W/m2 at Y’= 2.8 m along ES-Y1, with an agreeable turbulence level for Y’≦9 m.

Figure 5.17. Predicted profiles of WPD and TI against level distance from rooftop sidewalls at potential mounting sites for building ES.

For the high-rise buildings in realistic urbanized areas, we have explored relevant factors consisting of the building geometry, street canyon dimensions, incoming wind direction, and vector along the street canyon passage in conjunction with the interaction between upstream obstacles and downstream buildings on the WRA. This study then uses the CFD predictions to estimate the WPD distributions and wind TIs in the high-rise and high-density urban areas of Central, Hong Kong. The prevailing wind flowing over the rooftops of high-rise buildings can attain elevated WPDs through the flow expansion around the corners of buildings. In view of the buildings with notable design features, we investigate the impacts of building forms on the available wind resources. The IEC Standard of wind TI above 16% is also checked to exclude inappropriate mounting sites. Afterwards, we can then determine the promising locations to install wind turbines. The correct placement of small wind turbines in the urban settings has a key influence on the operating performance in the energy harvesting process. WPD increases with the cube of wind speed; therefore, it is critical to identify the proper mounting locations. To meet the essentials of mounting wind turbines, we integrate the aforementioned procedures to probe the suitable locations over the selected buildings with high power densities and low TIs. After properly screening those selected sites by the criteria of the lowest mounting height and maximum power density, the final locations in conjunction with the level distances from the rooftop sidewalls and heights above the rooftops can be resolved for turbine installation.

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In effect, from the current results, the information of wind resource assessment about the site selection for turbine installation can be important for both the private and public decision-makers to estimate the urban wind power potential. The obtained results also provide vital information for wind equipment manufacturing companies to select new locations and for local governments to develop strategic industrial development policies. Overall, this study can be a useful reference when contemplating the incorporation of wind power generation into different building designs, including the building geometry, roof geometry, presence/ absence of upstream obstacles, arrangements of integrated building complex and parallel high-rise buildings. Although it has not actually determined or selected any wind turbine at the studied site at this stage. It needs to be taken in specifying turbine types that will cope with the existing local wind situations, turbulence levels and possible future changes of urban development.

5.6 (1)

(2)

(3)

(4)

Discussion

This study addresses the limitations as follows. As a numerical assessment of wind energy potential, we primarily adopted the annual meteorological conditions of most frequent mean wind conditions with a fixed wind direction as the inflow boundary condition for steady CFD simulations. It can offer the practical and quick estimation of wind energy resource, covering most periods with average wind conditions, but not all situations, specifically the worst weather conditions and extreme weather variations. In the studied areas of Hong Kong, the worst weather condition is typhoon, which is severe when the winds can exceed a maximum sustained speed of 33 m/s. In these extreme scenarios, the wind turbines shall be locked up for the safety measures of operations. Besides, the unsteadiness and variations of wind speed and direction in the intermittency and variability are not considered in detail. Moreover, we ignore the influences of solar radiation and thermal buoyancy on the wind velocities, direction and diurnal temperature in urban canyons. The present assessment can be extended to detail local wind conditions pertaining to the effects of building geometry and surrounding arrangement of existing single high-rise buildings, integrated buildings complex and parallel high-rise buildings. However, the scope of this study aims to provide a numerical assessment of urban wind power for facilitating the relative researchers, engineers, policymakers, and investors in the development of appropriate wind turbine installation strategies. The impact of the future development in the vicinity of selected site on the performance of wind turbine is not yet considered in this wind resource assessment. Before delving into the investigations of turbine installation in realistic urban areas, the selection of installed locations and types of wind turbines must be pinpointed first. To choose the appropriate wind turbines in realistic urban areas, the factors of the sizes of rotor and generator, capacity, delivered performance of wind turbines, number of turbines, layout placement of wind farms, power loss due to the influence of the upstream turbines, required space for turbine installation, expected generation of power and estimated maintenance cost are not considered in this numerical assessment. All the relative impacts become entirely dependent on the geographical location and long-term local wind conditions. In order to get valuable scrutiny, the observed duration must be large enough to smooth out temporal variability to one year or decade. When computing the wind power potential over such long periods, the implementation of wind turbines becomes fully independent of the intermittency phenomenon which is therefore not considered in this article.” One of the challenges for utilization and installation of wind turbines in realistic urban areas is the safety considerations and its associated environmental impacts because of those turbines in the vicinity to property and people. The hazard potential of wind turbines on the

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environment can include the fire dangers, leaked lubricating fluids, sound noises of blades, causes of bird deaths and obstacles affecting aircraft, is not considered in this article. The results of this numerical assessment to better understand the inflow wind conditions are crucial for input into the design process of a small wind turbine as useful references to the above safety considerations. (5) This study evaluated WRA via the effective WPD and TI ≦ 16% as the indicators to characterize the urban wind power potential. Future works should implement and compare various resource assessment indices to estimate urban wind energy. (6) This study is lack of the wind data from nearby weather stations or field measurements at the reference point above the rooftop of the highest building – IFC, since the application for the permission of on-site measurements at the roof of IFC was declined. It can be very useful to acquire the wind data measured on the top of other high-rise buildings in the future. Despite these limitations, the concerned issue is on the feasibility of installing and applying wind turbines on the high-rise buildings in realistic highly urbanized areas. This paper well analyzed the effect of realistic and complex building configurations on the wind resources above an urban area of Central, Hong Kong. The CFD simulation outcomes confirm the effectiveness of the present WRA for improving wind harvest performance by evidence-based urban design.

5.7

Conclusions

This study reports the wind power potential distribution, influenced by its wind resource and existing urban configuration, in a realistic highly urbanized area, such as Central, Hong Kong. To discover the highest available wind resources with avoidance of elevated turbulence intensities, we conducted CFD simulations and on-site measurements with considering the details of boundary conditions and urban topography of the microenvironment to estimate the WPD on the suggested locations for turbine installation. To verify the computational model for WRA through the CFD-based assessment process, we compared the calculated wind speed, wind direction and turbulence intensity with the measurement results. The main conclusions are summarized below. (1) The present CFD simulations have employed the most frequent and annual mean wind condition in the study site, with the wind from the east direction as the inflow boundary conditions for the annual wind energy assessment. (2) This study has fully considered the effects of local climate, urban landscape, complex building shapes, and existent obstacles in the complicated urban areas on the local velocity and TI of the wind to pinpoint the appropriate sites for turbine installation. (3) This study compared the CFD predictions with the field measurement data to verify the computational model. The results of wind speed, wind direction and TI were in reasonable agreement with the on-site measurement data in summer (July) and winter (November) with differences of less than 20%. (4) Several prospective sites are suggested via the wind power estimation results, addressing the high WPD and TI under 16% to determine the proper locations for the deployment of wind turbines. Performing design trades by the factors including the final locations, least level distance from the rooftop sidewalls, lowest mounting height of wind turbines above the roofs, highest WPD and acceptable TI, we can then put forward the suggestions of the potential mounting sites and wind resource assessment in realistic highly urbanized areas. (5) As the prevailing wind across four selected single high-rise buildings with the variations of building shapes (e.g., cuboid shape with sharp edge or fillet edge, octagonal star-shaped, triangular shapes), we can observe relatively low WPD values on the leeward side. The rooftop sidewalls around the corners of buildings have high WPD with a suitable TI range, as the suggested mounting locations of wind turbines. In addition, the building geometries

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with fillet edges or smooth corner are of great importance to achieve high WPDs and low TIs. (6) Considering the variations of roof shapes (doom-like, pyramid-like, prismatic, and flat roof) of selected single high rise buildings, the wind over the leading edge of the roof tends to continuously accelerate to yield high WPD (over 400 W/m2) with low TI. Accompanying of airflow splitting developed strong thin shear layers at windward roof surface, suggesting appropriate locations for turbine installation. As many researchers drew similar conclusions, curved edge of roof (as doom-like roof in this case) shows the best results of high WPDs and low TIs. (7) In a realistic highly urbanized area, nearby upstream obstacles can substantially diminish wind power production and cause elevated turbulence levels over 16% from the subject building located downstream. (8) For integrated building complexes with a narrow gap (i.e., high H/G), the building arrangement of divergent channel with an intersected angle can form the “Venturi effect”, inducing relatively high WPD and low TI through the gap passageway. The oblique incoming flows are relatively hard to permeate into the gap passage between building complexes, and thus result in lower wind power densities, as compared to those of upright incoming flows. (9) In view of deep street canyons between parallel high-rise buildings (i.e., H/W>2), the predicted WPD greatly depends on the incoming flow direction α in respect to the street orientation. Straight incoming flows (α= 0°) can be fully channeled and accelerated via the merging of two streams of corner-expansion flows through a deep street canyon to attain elevated WPD (≧400 W/m2) and satisfactory TI values over the core regions on the leeward side. In contrast, slanting incoming flows pass through the space between parallel high-rise buildings, the wind resources with high WPD and the associated TI under 16% appear around asymmetrical corners with the expansion flows. (10) Similar high-rise and high-density morphological characteristics of the area under study can be found in future urban design development schemes. Hence the conclusions and any possible recommendations for improving the wind energy potential of the area under investigation are considered to be useful for urban planners and decision-makers of these regions.

Discussion This thesis focuses on using urban wind energy as a renewable energy source to progress towards the United Nations’ sustainable development goals (SDGs). Various morphological parameters are evaluated towards improving the wind energy potential for high-rise buildings in close proximity. The obtained results are presented in four chapters. The methodological limitations and directions for future work were already discussed for each chapter. For brevity, the majority of the associated information will not be repeated here. In this part of the thesis, the main findings of this thesis are described in a more practical application perspective, and the directions for further research will be presented. In Chapter 2, a literature review of 33 journal publications related to the analyses of recent CFD studies on estimation of wind energy potential in urban areas was presented. Contrary to the suggestions based on CFD best practice guidelines [86, 99, 152] , 39% of CFD urban wind energy potential studies merely reported their findings without validation results. However, in view of accuracy and reliability, validation studies are essential before CFD can be used as a potential urban wind energy design tool. The studies illustrated in Chapters 2, 3, and 4 evaluated different building morphological parameters of the generic high-rise building array. The study aimed to improve the urban wind energy potential by adjusting these morphological features. Future studies can further maximize the wind energy potential by evaluating various additional aspects such as non-uniform building height, unequal street widths, façade geometrical details, etc. The urban wind energy assessment of a realistic case study was conducted in Chapter 5, while the wind conditions are based on the 14-year history of average meteorological data. However, the implementation of wind turbines is also related to the intermittent phenomena in turbulent winds, which was not considered in this study. The impact of the future development in the vicinity of the selected site on the performance of wind turbines is not considered in this thesis and neither in most other recent studies. Regarding wind turbines, low wind speeds and high turbulence can reduce wind power yield as well as cause severe stresses on turbine blades, and thereby deteriorate operating efficiency [19]. The complex turbulent wind in the urban environment generally leads to a lower efficiency of wind turbines and could render them less economically viable [47]. Regardless, to choose the appropriate wind turbines in urban areas, the detailed information of wind turbines must be gathered first, including the factors of the sizes of rotor and generator, capacity, delivered performance of wind turbines, number of turbines, power loss due to the influence of the upstream turbines, required space for turbine installation, expected generation of power and estimated maintenance cost, etc.

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Summary and Conclusions In Chapter 2, the urban wind energy potential is assessed for an array of generic high-rise buildings in close proximity. A comprehensive CFD analysis focuses on the building arrangement and height, including (i) the passage width between the upstream buildings (w) varying from 0.15B to 0.75B, (ii) the streamwise distance between the upstream and the downstream buildings (d) varying from 0.15B to 0.6B, and (iii) the height difference between the upstream and the downstream buildings (ΔH) varying from -0.3H to 0.3H, and (iv) wind turbine type and orientation for the typical horizontal axis wind turbines (HAWTs), the typical (verticallymounted) vertical axis wind turbines (VAWTs), and the horizontally-mounted VAWTs. The study investigates the wind field around four identical full-scale high-rise buildings, with a building height-to-building side length ratio of H/B = 4.5 and H/w = 30, in a 2×2 array. CFD validation study examines the prediction capabilities of five commonly-used Reynolds-Averaged Navier-Stokes (RANS) turbulence models, i.e., standard k-ε model (SKE), realizable k-ε model (RKE), and renormalization group k-ε model (RNG), shear-stress transport k-ω model (SST) and reynolds stress model (RSM), as compared to the wind tunnel experimental data. Wind tunnel measurements of the 3-component flow velocity and turbulence intensity are performed using a Cobra probe for the validation of the reduced-scale CFD simulations. The results show reasonable agreement for the mean streamwise velocity and turbulence intensity between the predictions of the RSM turbulence model and the experimental data. The results show that (i) the wind power density along the upstream passage increases by decreasing w, increasing d and for an equal building height (ΔH = 0); (ii) high w values (> 0.15B), low d values (< 0.6B), and low upstream building heights (ΔH < 0) can realize high wind power densities between the downstream buildings; (iii) the horizontally-mounted VAWT is the most promising option for wind energy harvesting with a maximum of 37% higher wind power density, as compared to the HAWT and the VAWT in the passage between the buildings and on the rooftop. In Chapter 3, the analysis is focused on the impact of different building corner modification parameters on the wind power density and the reference turbulence intensity (Iref), including (i) the impacts of the building corner shape (i.e. sharp, chamfered, and rounded shapes), (ii) the impact of the chamfered corner length (l) varying from 0.05B to 0.2B, (iii) the rounded corner radius (r) varying from 0.05B to 0.2B, (iv) the impact of the wind turbine type and orientation (i.e., HAWT, VAWTs, and horizontally-mounted VAWTs) for the aforementioned 2×2 building array. The findings reveal that (i) there are noticeable influences of the corner modification on the realization of higher wind power density up to 1167%, and lower total turbulence intensity down to 50%, on three prospective regions along the passage between the buildings, on the building roofs, and beside the buildings; (ii) the rounded corner is the most favorable building corner shape yielding a maximum of 337% higher wind power densities, as compared to the sharp corner, with a comparatively significant reduction of 52% in total turbulence intensity on the roof under both normal and oblique wind directions; (iii) by increasing the chamfer length to 0.20B or rounded corner radius to 0.20B, wind power density can be dramatically enhanced up to 110%

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and 350%, respectively, besides the high-rise buildings; (iv) for all three aforementioned building corner shapes, the horizontally-mounted VAWTs yield a maximum of 24% higher wind power densities than others in the passage between the buildings and on the rooftop, while typical (vertically-aligned) VAWTs enhance a maximum of 43% higher wind power densities than others besides the buildings. In Chapter 4, the urban wind energy potential is assessed for a 6×6 array of generic high-rise buildings. A comprehensive parametric study is conducted to analyze the impacts of urban morphologies on the power density and the Iref, including (i) urban densities altered from compact to sparse urban layouts, (ii) building corner shapes of sharp and rounded corners, (iii) urban layouts of in-line and staggered patterns, and (iv) wind directions of 0° and 45° for a 6×6 array of generic high-rise buildings. The findings indicate that (i) decreasing urban plan area density (λp) reduces the unacceptable turbulence areas with relatively higher wind power density on the roof; (ii) round corners can produce elevated power densities up to 201% and 150% than those of sharp corners for those selected areas beside the building and on the roof, respectively; (iii) the urban layout of staggered pattern leads to poorer wind energy potentials with reduced wind power densities and escalated turbulence intensities than the in-line pattern; (iv) even under the oblique wind direction of 45°, increasing urban plan area density to the compact urban layout (λp = 0.76) can increase the power densities up to 268%, especially for the round corners. Chapter 5 performs wind resource assessment around a realistic compact high-rise urban area in Central, Hong Kong using full-scale CFD simulations. This study has fully considered the effects of local climate, urban landscape, complex building shapes, and existent obstacles in the complicated urban areas on the local wind velocity, power density, and total turbulence intensity to pinpoint the appropriate sites for turbine installation. The wind resource assessment is focused on different building features of existing high-rise buildings, including (i) building shape (i.e., cuboid with sharp or round edges, rounded, octagonal star-shaped, and triangular shapes), (ii) roof shape (i.e., flat, doom-like, pyramid-like, prism-like roofs), (iii) presence or absence of upstream obstacles, (iv) arrangements of the integrated building complex, and (v) layouts of parallel high-rise buildings. On-site measurements of the wind velocity, the wind direction, and the total turbulence intensity are conducted at 16 monitored points using the 3D ultrasonic anemometers and the thermal flow velocity probe during the daytime over 7 days for the validation of the CFD simulations in both winter and summer periods. Based on defined criteria, i.e. the highest wind power density with an acceptable total turbulence intensity (≦ 16%), this study suggests several prospective sites for installing wind turbines on the existing high-rise buildings in realistic high-rise urban areas. The results show that (i) the CFD validation of wind speed, wind direction, and total turbulence intensity are in reasonable agreement with the on-site measurement data in summer and winter with differences of less than 20%; (ii) for the various building shapes, the building geometry with round edges achieves higher power density and lower turbulence around the two lateral sidewalls than its with sharp edges; (iii) for the various roof shapes, the curved leading edge of the doom-like roof shows up to 80% higher wind power density than flat shape roof; (iv) for the impact of upstream obstacles, one upstream high-rise building reduces 80% wind power density and rises the turbulence intensities over 16% of the downstream building; (v) for integrated building complexes with a narrow passage width , the building arrangement of the orthogonal divergent channel with aligned incoming flows induces relatively the highest power density and lowest turbulence intensity in between the passage than others with oblique incoming flows; (vi) for deep street canyons between parallel high-rise buildings (i.e., H/w > 2), oblique incoming flows cause the promising installation location to appear around lateral outer corners. As a general conclusion, it is found that the arrangement of the high-rise buildings in an array and their geometrical characteristics are very influential factors on the available wind power density and the level of turbulence intensity in the potential

Summary and conclusions

135

locations for wind energy harvesting on the building roofs, in the passage between the buildings and besides them. As a result, early considerations in the initial architectural design of high-rise buildings play a significant role to realize the viability of urban wind energy harvesting for highrise buildings.

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[190] Beller C. Development of a Simulation Tool to Predict Urban Wind Potential. 2011. p. 111-20. [191] Kalmikov A, Dupont G, Dykes K, Chan C. Wind power resource assessment in complex urban environments: MIT campus case-study using CFD Analysis2010. [192] Soebiyan V, Saragih JFB, Tedja M. Study on High-rise Building Using Wind Energy at Humid Tropical Climate. Chemical engineering transactions. 2017;56:241-6. [193] Wong MS, Kwan WK. Wind Statistics in Hong Kong in Relation to Wind Power: Hong Kong Observatory; 2002. [194] Siu LW, Hart MA. Quantifying urban heat island intensity in Hong Kong SAR, China. Environ Monit Assess. 2013;185:4383-98. [195] Technology DSCHaV. Energy performance of buildings - Part 3: Ventilation for nonresidential buildings - Performance requirements for ventilation and room-conditioning systems. 2014. [196] Wieringa J. Updating the Davenport roughness classification. Journal of Wind Engineering and Industrial Aerodynamics. 1992;41:357-68. [197] ANSYS I. ANSYS FLUENT, Version 18.0: User Manual. ANSYS,2017. [198] Karava P, Jubayer CM, Savory E. Numerical modelling of forced convective heat transfer from the inclined windward roof of an isolated low-rise building with application to photovoltaic/thermal systems. Applied Thermal Engineering. 2011;31:1950-63. [199] Hargreaves DM, Wright NG. On the use of the k– model in commercial CFD software to model the neutral atmospheric boundary layer. Journal of Wind Engineering and Industrial Aerodynamics. 2007;95:355-69. [200] Doormaal JPV, Raithby GD. Enhancements of the simple method for predicting incompressible fluid flows. Numerical Heat Transfer. 1984;7:147-63. [201] Jang DS, Jetli R, Acharya S. Comparison of the PISO, SIMPLER, and SIMPLEC algorithms for the treatment of the pressure-velocity coupling in steady flow problems. Numerical Heat Transfer. 1986 10:209-28. [202] Elliott DL, Holladay CG, Barchet WR, Foote HP, Sandusky WF. Wind energy resource atlas of the United States. United States1991. [203] Commission IE. IEC 61400-1 Wind Turbines - Part 1: Design requirements 2005. [204] Kono T, Kogaki T, Kiwata T. Numerical Investigation of Wind Conditions for RoofMounted Wind Turbines: Effects of Wind Direction and Horizontal Aspect Ratio of a High-Rise Cuboid Building. Energies. 2016;9:907. [205] Toja F, Peralta C, Lopez-Garcia O, Navarro J, Cruz I. On Roof Geometry for Urban Wind Energy Exploitation in High-Rise Buildings. Computation. 2015;3:299-325. [206] Yi J, Li QS. Wind tunnel and full-scale study of wind effects on a super-tall building. Journal of Fluids and Structures. 2015;58:236-53. [207] Krüger E, Suga M. Recommendations of Height Restrictions for Urban Canyons in Curitiba, Brazil. Journal of Asian Architecture and Building Engineering. 2009;8:447-52. [208] Su YM, Wu YC, Yang AS, Juan YH. Wind simulations for studying ecological influences of existing Guggenheim museum Bilbao on the urban surroundings. Advanced Science Letters. 2013;19:2884-90. [209] Yang A-S, Wen C-Y, Juan Y-H, Su Y-M, Wu J-H. Using the central ventilation shaft design within public buildings for natural aeration enhancement. Applied Thermal Engineering. 2014;70:219-30. [210] Yang AS, Juan YH, Wen CY, Su YM, Wu YC. Investigation on Wind Environments of Surrounding Open Spaces Around a Public Building. Journal of Mechanics. 2017;33:10113.

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References [211] Wen C-Y, Juan Y-H, Yang A-S. Enhancement of city breathability with half open spaces in ideal urban street canyons. Building and Environment. 2017;112:322-36. [212] Li Z, Shi T, Wu Y, Zhang H, Juan Y-H, Ming T, et al. Effect of traffic tidal flow on pollutant dispersion in various street canyons and corresponding mitigation strategies. Energy and Built Environment. 2020. [213] Li Z, Zhang H, Wen C-Y, Yang A-S, Juan Y-H. The effects of lateral entrainment on pollutant dispersion inside a street canyon and the corresponding optimal urban design strategies. Building and Environment. 2021;195:107740. [214] Li Z, Zhang H, Wen C-Y, Yang A-S, Juan Y-H. Effects of height-asymmetric street canyon configurations on outdoor air temperature and air quality. Building and Environment. 2020;183:107195. [215] Juan Y-H, Wen C-Y, Li Z, Yang A-S. A combined framework of integrating optimized half-open spaces into buildings and an application to a realistic case study on urban ventilation and air pollutant dispersion. Journal of Building Engineering. 2021;44:102975. [216] Juan YH, Rezaeiha A, Montazeri H, Blocken B, Yang AS. CFD assessment of wind energy potential for generic high-rise buildings: Impacts of building corner modifications.In preparation. [217] Juan YH, Montazeri H, Blocken B, Yang AS. Numerical analysis of urban wind power potential between high-rise buildings: impact of building orientations. 7th International Symposium on Computational Wind Engineering (CWE2018). Republic of Korea2018. [218] Juan YH, Chen YP, Yang AS, Cheng CH. CFD Simulations of Pollutant Dispersion for Optimized Half Open Spaces in Ideal Urban Street Canyons. 4th International Conference On Building Energy, Environment. Melbourne, Australia2018. p. 33-8. [219] Juan YH, Wen CY, Su YM, Lee YT, Yang AS. A Preliminary Assessment of Potential Wind Power Utilization in the Leeward Side of High-Rise Buildings. 4th International Conference On Building Energy, Environment. Melbourne, Australia2018. p. 39-44. [220] Chen YP, Juan YH, Yang AS. CFD Simulations of Outdoor Pollutant Dispersion for Generic Urban Designs with Partly-open Spaces. Engineering & Technology, Computer, Basic & Applied Sciences (ECBA2017). Seoul, South Korea2017. [221] Juan YH, Lee YT, Zhuang JR, Yang AS, Cheng CH, Wang PC. Wind Flow Predictions Around Buildings Having Semi-open Spaces in Urban Canyons. Proceedings of the 2nd Thermal and Fluid Engineering Conference (TFEC2017) and 4th International Workshop on Heat Transfer (IWHT2017). Las Vegas, NV, USA2017. [222] Juan YH, Yang AS. A Study of Estimating Wind Power Generation in Dense Urban Area Considering Wind Speed Uncertainty. 2016 The 11th National Conference on Hydrogen Energy and Fuel Cell Full Paper Format. Taipei, Taiwan2016. [223] Liu CY, Yang AS, Juan YH, Cheng CH, Lee YT. CFD Simulations to Study the Cooling Effect of Urban Vegetation on Outdoor Thermal Environment. Proceedings of the 8th Asian Conference on Refrigeration and Air Conditioning (ACRA2016). Taipei, Taiwan2016. [224] Wang WS, Yang AS, Juan YH, Jheng JY. CFD Assessments of Installation Sites of Wind Power on an Urban Building. Proceedings of the 7th Asian Conference on Refrigeration and Air Conditioning (ACRA2014). Korea2014. [225] Yang AS, Wen CY, Wu YC, Juan YH, Su YM. Wind Field Analysis for a High-rise Residential Building Layout in Danhai, Taiwan. The 2013 International Conference of Computer Science and Engineering (ICCSE). London, U.K.2013.

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[226] Yang AS, Chang CJ, Juan YH, Wu JH. Using CFD Simulations to Study the Outdoor Wind Comfort of Taipei Flora Exposition. The 20rd National Computational Fluid Dynamics Conference. Nantou, Taiwan2013. [227] Juan YH, Yang AS, Su YM, Wu YC. Applications of CFD Simulations for Studying Ecological Influences of Existing Guggenheim Museum Bilbao on Urban Surroundings. 2012 Energy and Refrigerating Air-Conditioning Conference Conference (ERAC2012). Taipei, Taiwan2012.

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References

Biography Yu-Hsuan Juan, born in 1989 in Taipei, Taiwan, obtained her BSc with honors in ‘Aerospace and Systems Engineering’ from ‘Feng Chia University, Taiwan’ in 2011. Then, she received her MSc in ‘Energy and Refrigerating Air-Conditioning Engineering’ at ‘National Taipei University of Technology (TaipeiTECH), Taiwan’ in 2013. Her research interest focuses on sustainability in the urban environment since her MSc studies. Following her MSc degree, she continued as a researcher, where based on her MSc thesis and the continued research between 2013 and 2016, she published three peer-reviewed ISI journal papers [208-210]. During her studies in 2014 to 2016, she conducted two years of research work at the ‘Department of Mechanical Engineering, The Hong Kong Polytechnic University (PolyU), Hong Kong’, where this collaboration later led to publishing four peer-reviewed ISI journal papers [17, 38, 43, 211]. In 2017-2019, she continued at PolyU on a two-year research project - ‘development of a wind-field simulation platform to assess installation sites of wind turbines in highly urbanized areas of Hong Kong’, where her findings have been submitted to a peer-reviewed scientific journal [18]. In 2016, she cstarted her PhD studies at ‘National Taipei University of Technology, Taiwan' and soon after she joined the Double PhD degree program with 'Eindhoven University of Technology (TU/e), The Netherlands’. In 2017, she secured the Government Scholarship to Study Abroad sponsored by 'Ministry of Education, Taiwan', the Graduate Students Study Abroad Program sponsored by 'Ministry of Science and Technology, Taiwan', and the Graduate Students Study Abroad Scholarship sponsored by TaipeiTECH. In 2018, she joined the Marie-Curie fellowship as part of the Horizon2020 ITN Project AEOLUS4FUTURE to continue her PhD studies at TU/e, where her research was focused on ‘wind energy harvesting in the built environment’. She has published a total of 13 peer-reviewed ISI journal papers [17, 38, 43, 110, 182, 208-215], submitted an additional two peer-reviewed ISI journal papers [111, 216] and has published several papers in international conference proceedings [98, 217-227]. She has already cosupervised nine master students. She is a reviewer for 11 international ISI journals including Building and Environment, Energy, and the Journal of Building Engineering.

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List of publications 1. ISI journal papers 1.1 Published 1)

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Juan, Y. H., Wen, C. Y., Li, Z. and Yang, A. S.*, A combined framework of integrating optimized half-open space s into buildings to improve urban ventilation and air pollutant dispersion, Building Engineering, 102975, 2021. Juan, Y. H., Wen, C. Y., Li, Z. and Yang, A. S.*, Impacts of urban morphology on improving urban wind energy potential for generic high-rise building arrays, Applied Energy, Vol. 299, 117304, pp 1-23, 2021. Li, Z., Zhang, H., Wen, C. Y., Yang, A. S.* and Juan, Y. H., The effects of lateral entrainment on pollutant dispersion inside a street canyon and the corresponding optimal urban design strategies, Building and Environment, Vol. 195, 107740, 2021. Juan, Y. H., Wen, C. Y., Chen, W.Y. and Yang, A. S.*, Numerical Assessments of Wind Power Potential and Installation Arrangements in Realistic Highly Urbanized Areas, Renewable and Sustainable Energy Reviews, Vol. 135, 110165, 2021. Li, Z., Zhang, H., Wen, C. Y., Yang, A. S.* and Juan, Y. H., ‘‘Effects of height-asymmetric street canyon configurations on outdoor air temperature and air quality,‘‘ Building and Environment, Vol. 183, 107195, 2020. Li, Z., Zhang, H., Wen, C. Y., Yang, A. S.* and Juan, Y. H., Effects of frontal area density on outdoor thermal comfort and air quality, Building and Environment, Vol. 180, 107028, 2020. Li, Z., Shi, T., Wu, Y., Zhang, H., Juan, Y. H., Ming, T.* and Zhou, N., Effect of traffic tidal flow on pollutant dispersion in various street canyons and corresponding mitigation strategies, Energy and Built Environment, Vol. 1, No.3, pp. 242-253, 2020. Juan, Y. H., Yang, A. S.*, Wen, C. Y., Lee, Y.T. and Wang, P.C., Optimization Procedures for Enhancement of City Breathability Using Arcade Design in a Realistic High-Rise Urban Area, Building and Environment, Vol. 121, pp. 247-261, 2017. Lee, Y. T., Yang, A. S., Juan, Y. H., Liu, C. S. and Chang, Y. H., A New MicroHydrodynamic Herringbone Bearing Using Slant Groove Depth Arrangements for Performance Enhancement, Journal of Mechanics, Vol. 33, No.5, pp. 725-737, 2017. Yang, A. S., Juan, Y. H., Wen, C. Y.* and Chang, C. J., Numerical simulation of cooling effect of vegetation enhancement in a subtropical urban park, Applied Energy, Vol. 192, pp. 178–200, 2017. Wen, C. Y., Juan, Y. H. and Yang, A. S.*, Enhancement of city breathability with half open spaces in ideal urban street canyons, Building and Environment, Vol. 112, pp. 322336, 2017. Yang, A. S., Su, Y. M., Wen, C. Y.*, Juan, Y. H., Wang, W. S. and Cheng, C. H., Estimation of Wind Power Generation in a Dense Urban Area, Applied Energy, Vol. 171, pp. 213-230, 2016. Yang, A. S., Juan, Y. H., Wen, C. Y.*, Su, Y. M. and Wu, Y. C., Investigation on Wind Environments of Surrounding Open Spaces around a Public Building, Journal of

List of publications

154

Mechanics, Vol. 33, pp. 101-113, 2016. 14) Yang, A. S.*, Wen, C. Y., Cheng, C. H. and Juan, Y. H., ‘‘CFD Simulations to Study the Cooling Effects of Different Greening Modifications,’’ World Academy of Science, Engineering and Technology, International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, Vol. 9, No. 7, pp. 825-831, 2015. 15) Yang, A. S., Wen, C. Y.*, Juan, Y. H., Su, Y. M., and Wu, J. H., Using the Central Ventilation Shaft Design within Public Buildings for Natural Aeration Enhancement, Applied Thermal Engineering, Vol. 70, pp. 219-230, 2014. 16) Yang, A. S.*, Cheng, C. H., Wu, J. H. and Juan, Y. H., CFD Simulations to Examine Natural Ventilation of a Work Area in a Public Building, World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, Vol. 8, No. 7, pp. 1186-1191, 2014. 17) Yang, A. S.*, Chang, C. J., Juan, Y. H. and Su, Y. M., CFD Simulations to Predict Comfort Level of Outdoor Wind Environment for Taipei Flora Exposition, Applied Mechanics and Materials, Vol. 421, pp. 844-849, 2013. 18) Yang, A. S.*, Wu, J. H., Juan, Y. H. and Su, Y. M., CFD Simulations of Natural Ventilation Effect: A Case Study of New Administrative Building of the Guanyin Township, Applied Mechanics and Materials, Vols. 368-370, pp. 611-614, 2013. 19) Su, Y. M., Yang, A. S.*, Wu, Y. C. and Juan, Y. H.,Wind Simulations for Studying Ecological Influences of Existing Guggenheim Museum Bilbao on the Urban Surroundings, Advanced Science Letters, Vol 19(10), pp 2884-2890, 2013.

1.2 Submitted/in preparation 1)

Juan, Y.H., Rezaeiha, A., Montazeri, H., Blocken, B., Wen, C.Y., Yang, A.S., CFD assessment of wind energy potential for generic high-rise buildings in close proximity: Impact of building arrangement and height, Submitted. Juan, Y.H., Rezaeiha A., Montazeri H., Blocken, B., Yang, A.S., CFD assessment of wind energy potential for generic high-rise buildings: Impacts of building corner modifications, Submitted.

2. International conference proceedings 1)

Juan, Y. H., Wen, C-Y., Yang, A-S., Montazeri, H. and Blocken, B. Potential wind power utilization in diverging passages between two high-rise buildings: using “Venturi effect” on the windward side. 34th edition of the International Conference on Passive Low Energy Architecture (PLEA2018), Hong Kong, 2018. Juan, Y. H., Montazeri, H., Blocken, B. and Yang, A-S. Numerical analysis of urban wind power potential between high-rise buildings: impact of building orientations. 7th International Symposium on Computational Wind Engineering (CWE2018), Republic of Korea, 2018. Juan, Y. H., Chen, Y.P., Yang, A. S., and Cheng, C. H., CFD Simulations of Pollutant Dispersion for Optimized Half Open Spaces in Ideal Urban Street Canyons, 4th International Conference on Building Energy, Environment, ISBN: 978-0-646-98213-7, pp. 33-38, Melbourne, Australia, 2018. Juan, Y. H., Wen, C. Y., Su, Y. M., Lee, Y. T., and Yang, A. S., A Preliminary Assessment of Potential Wind Power Utilization in the Leeward Side of High-Rise

List of publications

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Buildings, 4th International Conference on Building Energy, Environment, ISBN: 978-0646-98213-7, pp. 39-44, Melbourne, Australia, 2018. Lee, Y. T., Yang, A. S., Chang, L. W., Xiao, Y. X., and Juan, Y. H., Investigation of Heat and Mass Transfer of Evaporating Liquid Film on an Elliptic Tube, 2017 JSRAE Annual Conference, Japan Society of Refrigerating and Air Conditioning Engineers, Tokyo, Japan, 2017. Chen, Y.P., Juan, Y.H., and Yang, A. S.*, CFD Simulations of Outdoor Pollutant Dispersion for Generic Urban Designs with Partly-open Spaces, Engineering & Technology, Computer, Basic & Applied Sciences (ECBA2017), Paper No. SUE-177-104, Seoul, South Korea, 2017. Juan, Y. H., Lee, Y. T., Zhuang, J. R., Yang, A. S.*, Cheng, C. H. and Wang, P. C., Wind Flow Predictions Around Buildings Having Semi-open Spaces in Urban Canyons, Proceedings of the 2nd Thermal and Fluid Engineering Conference (TFEC2017) and 4th International Workshop on Heat Transfer (IWHT2017), Paper No. TFEC-IWHT201718206, Las Vegas, NV, USA, 2017. Lee, Y. T., Zhuang, J. R., Juan, Y. H., Yang, A. S.*, Liu, C. S., and Chang, Y. H., Predictions of Load Capacity in Thermo-hydrodynamic Lubrications Process for Herringbone Groove Bearings, Proceedings of the 2nd Thermal and Fluid Engineering Conference (TFEC2017) and 4th International Workshop on Heat Transfer (IWHT2017), Paper No. TFEC-IWHT2017-18219, Las Vegas, NV, USA, 2017. Juan, Y. H. and Yang, A. S., A Study of Estimating Wind Power Generation in Dense Urban Area Considering Wind Speed Uncertainty, 2016 The 11th National Conference on Hydrogen Energy and Fuel Cell Full Paper Format, Taipei, Taiwan, 2016. Lee, Y. T., Yang, A. S.*, Liu, C. S., Chang, Y. H. and Juan, Y. H., CFD Simulations to Determine the Loading Characteristics of a Micro-hydrodynamic Bearing at Different Designs of Herringbone Grooves, 2016 JSRAE Annual Conference, Japan Society of Refrigerating and Air Conditioning Engineers, Paper No. C133, Kobe, Japan, 2016. Liu, C. Y., Yang, A. S.*, Juan, Y. H., Cheng, C. H., and Lee, Y. T., CFD Simulations to Study the Cooling Effect of Urban Vegetation on Outdoor Thermal Environment, Proceedings of the 8th Asian Conference on Refrigeration and Air Conditioning (ACRA2016), Paper No. ACRA2016 -130, Taipei, Taiwan, 2016. Wang, W. S., Yang, A. S.*, Juan, Y. H., and Jheng, J. Y., CFD Assessments of Installation Sites of Wind Power on an Urban Building, Proceedings of the 7th Asian Conference on Refrigeration and Air Conditioning (ACRA2014), Paper No. ACRA2014432, Korea, 2014. Yang, A. S.*, Wen, C. Y., Wu, Y. C., Juan, Y. H. and Su, Y. M., Wind Field Analysis for a High-rise Residential Building Layout in Danhai, Taiwan, The 2013 International Conference of Computer Science and Engineering (ICCSE), Paper No. ICCSE_29, London, U.K., 2013. Yang, A. S.*, Chang, C. J., Juan, Y. H., and Wu, J. H., Using CFD Simulations to Study the Outdoor Wind Comfort of Taipei Flora Exposition, The 20rd National Computational Fluid Dynamics Conference, Paper No. CFD22-5, Nantou, Taiwan, 2013. Juan, Y. H., Yang, A. S*, Su, Y. M. and Wu, Y. C., Applications of CFD Simulations for Studying Ecological Influences of Existing Guggenheim Museum Bilbao on Urban Surroundings, 2012 ERAC Conference, Paper No. 19, Taipei, Taiwan, 2012.

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Bouwstenen is een publicatiereeks van de Faculteit Bouwkunde, Technische Universiteit Eindhoven. Zij presenteert resultaten van onderzoek en andere activiteiten op het vakgebied der Bouwkunde, uitgevoerd in het kader van deze Faculteit. Bouwstenen en andere proefschriften van de TU/e zijn online beschikbaar via: https://research.tue.nl/

Reeds verschenen in de serie Bouwstenen nr 1 Elan: A Computer Model for Building Energy Design: Theory and Validation Martin H. de Wit H.H. Driessen R.M.M. van der Velden

nr 9 Strukturering en Verwerking van Tijdgegevens voor de Uitvoering van Bouwwerken ir. W.F. Schaefer P.A. Erkelens

nr 2 Kwaliteit, Keuzevrijheid en Kosten: Evaluatie van Experiment Klarendal, Arnhem J. Smeets C. le Nobel M. Broos J. Frenken A. v.d. Sanden

nr 10 Stedebouw en de Vorming van een Speciale Wetenschap K. Doevendans

nr 3 Crooswijk: Van ‘Bijzonder’ naar ‘Gewoon’ Vincent Smit Kees Noort

nr 12 Staal in de Woningbouw, Korrosie-Bescherming van de Begane Grondvloer Edwin J.F. Delsing

nr 4 Staal in de Woningbouw Edwin J.F. Delsing

nr 13 Een Thermisch Model voor de Berekening van Staalplaatbetonvloeren onder Brandomstandigheden A.F. Hamerlinck

nr 5 Mathematical Theory of Stressed Skin Action in Profiled Sheeting with Various Edge Conditions Andre W.A.M.J. van den Bogaard nr 6 Hoe Berekenbaar en Betrouwbaar is de Coëfficiënt k in x-ksigma en x-ks? K.B. Lub A.J. Bosch nr 7 Het Typologisch Gereedschap: Een Verkennende Studie Omtrent Typologie en Omtrent de Aanpak van Typologisch Onderzoek J.H. Luiten nr 8 Informatievoorziening en Beheerprocessen A. Nauta Jos Smeets (red.) Helga Fassbinder (projectleider) Adrie Proveniers J. v.d. Moosdijk

nr 11 Informatica en Ondersteuning van Ruimtelijke Besluitvorming G.G. van der Meulen

nr 14 De Wijkgedachte in Nederland: Gemeenschapsstreven in een Stedebouwkundige Context K. Doevendans R. Stolzenburg nr 15 Diaphragm Effect of Trapezoidally Profiled Steel Sheets: Experimental Research into the Influence of Force Application Andre W.A.M.J. van den Bogaard nr 16 Versterken met Spuit-Ferrocement: Het Mechanische Gedrag van met Spuit-Ferrocement Versterkte Gewapend Betonbalken K.B. Lubir M.C.G. van Wanroy

nr 17 De Tractaten van Jean Nicolas Louis Durand G. van Zeyl nr 18 Wonen onder een Plat Dak: Drie Opstellen over Enkele Vooronderstellingen van de Stedebouw K. Doevendans nr 19 Supporting Decision Making Processes: A Graphical and Interactive Analysis of Multivariate Data W. Adams nr 20 Self-Help Building Productivity: A Method for Improving House Building by Low-Income Groups Applied to Kenya 1990-2000 P. A. Erkelens nr 21 De Verdeling van Woningen: Een Kwestie van Onderhandelen Vincent Smit nr 22 Flexibiliteit en Kosten in het Ontwerpproces: Een Besluitvormingondersteunend Model M. Prins nr 23 Spontane Nederzettingen Begeleid: Voorwaarden en Criteria in Sri Lanka Po Hin Thung nr 24 Fundamentals of the Design of Bamboo Structures Oscar Arce-Villalobos nr 25 Concepten van de Bouwkunde M.F.Th. Bax (red.) H.M.G.J. Trum (red.) nr 26 Meaning of the Site Xiaodong Li

nr 27 Het Woonmilieu op Begrip Gebracht: Een Speurtocht naar de Betekenis van het Begrip 'Woonmilieu' Jaap Ketelaar nr 28 Urban Environment in Developing Countries editors: Peter A. Erkelens George G. van der Meulen (red.) nr 29 Stategische Plannen voor de Stad: Onderzoek en Planning in Drie Steden prof.dr. H. Fassbinder (red.) H. Rikhof (red.) nr 30 Stedebouwkunde en Stadsbestuur Piet Beekman nr 31 De Architectuur van Djenné: Een Onderzoek naar de Historische Stad P.C.M. Maas nr 32 Conjoint Experiments and Retail Planning Harmen Oppewal nr 33 Strukturformen Indonesischer Bautechnik: Entwicklung Methodischer Grundlagen für eine ‘Konstruktive Pattern Language’ in Indonesien Heinz Frick arch. SIA nr 34 Styles of Architectural Designing: Empirical Research on Working Styles and Personality Dispositions Anton P.M. van Bakel nr 35 Conjoint Choice Models for Urban Tourism Planning and Marketing Benedict Dellaert nr 36 Stedelijke Planvorming als Co-Produktie Helga Fassbinder (red.)

nr 37 Design Research in the Netherlands editors: R.M. Oxman M.F.Th. Bax H.H. Achten nr 38 Communication in the Building Industry Bauke de Vries nr 39 Optimaal Dimensioneren van Gelaste Plaatliggers J.B.W. Stark F. van Pelt L.F.M. van Gorp B.W.E.M. van Hove nr 40 Huisvesting en Overwinning van Armoede P.H. Thung P. Beekman (red.) nr 41 Urban Habitat: The Environment of Tomorrow George G. van der Meulen Peter A. Erkelens nr 42 A Typology of Joints John C.M. Olie nr 43 Modeling Constraints-Based Choices for Leisure Mobility Planning Marcus P. Stemerding nr 44 Activity-Based Travel Demand Modeling Dick Ettema nr 45 Wind-Induced Pressure Fluctuations on Building Facades Chris Geurts nr 46 Generic Representations Henri Achten nr 47 Johann Santini Aichel: Architectuur en Ambiguiteit Dirk De Meyer

nr 48 Concrete Behaviour in Multiaxial Compression Erik van Geel nr 49 Modelling Site Selection Frank Witlox nr 50 Ecolemma Model Ferdinand Beetstra nr 51 Conjoint Approaches to Developing Activity-Based Models Donggen Wang nr 52 On the Effectiveness of Ventilation Ad Roos nr 53 Conjoint Modeling Approaches for Residential Group preferences Eric Molin nr 54 Modelling Architectural Design Information by Features Jos van Leeuwen nr 55 A Spatial Decision Support System for the Planning of Retail and Service Facilities Theo Arentze nr 56 Integrated Lighting System Assistant Ellie de Groot nr 57 Ontwerpend Leren, Leren Ontwerpen J.T. Boekholt nr 58 Temporal Aspects of Theme Park Choice Behavior Astrid Kemperman nr 59 Ontwerp van een Geïndustrialiseerde Funderingswijze Faas Moonen

nr 60 Merlin: A Decision Support System for Outdoor Leisure Planning Manon van Middelkoop

nr 72 Moisture Transfer Properties of Coated Gypsum Emile Goossens

nr 61 The Aura of Modernity Jos Bosman

nr 73 Plybamboo Wall-Panels for Housing Guillermo E. González-Beltrán

nr 62 Urban Form and Activity-Travel Patterns Daniëlle Snellen

nr 74 The Future Site-Proceedings Ger Maas Frans van Gassel

nr 63 Design Research in the Netherlands 2000 Henri Achten nr 64 Computer Aided Dimensional Control in Building Construction Rui Wu nr 65 Beyond Sustainable Building editors: Peter A. Erkelens Sander de Jonge August A.M. van Vliet co-editor: Ruth J.G. Verhagen nr 66 Das Globalrecyclingfähige Haus Hans Löfflad nr 67 Cool Schools for Hot Suburbs René J. Dierkx nr 68 A Bamboo Building Design Decision Support Tool Fitri Mardjono nr 69 Driving Rain on Building Envelopes Fabien van Mook nr 70 Heating Monumental Churches Henk Schellen nr 71 Van Woningverhuurder naar Aanbieder van Woongenot Patrick Dogge

nr 75 Radon transport in Autoclaved Aerated Concrete Michel van der Pal nr 76 The Reliability and Validity of Interactive Virtual Reality Computer Experiments Amy Tan nr 77 Measuring Housing Preferences Using Virtual Reality and Belief Networks Maciej A. Orzechowski nr 78 Computational Representations of Words and Associations in Architectural Design Nicole Segers nr 79 Measuring and Predicting Adaptation in Multidimensional Activity-Travel Patterns Chang-Hyeon Joh nr 80 Strategic Briefing Fayez Al Hassan nr 81 Well Being in Hospitals Simona Di Cicco nr 82 Solares Bauen: Implementierungs- und UmsetzungsAspekte in der Hochschulausbildung in Österreich Gerhard Schuster

nr 83 Supporting Strategic Design of Workplace Environments with Case-Based Reasoning Shauna Mallory-Hill nr 84 ACCEL: A Tool for Supporting Concept Generation in the Early Design Phase Maxim Ivashkov nr 85 Brick-Mortar Interaction in Masonry under Compression Ad Vermeltfoort nr 86 Zelfredzaam Wonen Guus van Vliet nr 87 Een Ensemble met Grootstedelijke Allure Jos Bosman Hans Schippers nr 88 On the Computation of Well-Structured Graphic Representations in Architectural Design Henri Achten nr 89 De Evolutie van een West-Afrikaanse Vernaculaire Architectuur Wolf Schijns nr 90 ROMBO Tactiek Christoph Maria Ravesloot nr 91 External Coupling between Building Energy Simulation and Computational Fluid Dynamics Ery Djunaedy nr 92 Design Research in the Netherlands 2005 editors: Henri Achten Kees Dorst Pieter Jan Stappers Bauke de Vries nr 93 Ein Modell zur Baulichen Transformation Jalil H. Saber Zaimian

nr 94 Human Lighting Demands: Healthy Lighting in an Office Environment Myriam Aries nr 95 A Spatial Decision Support System for the Provision and Monitoring of Urban Greenspace Claudia Pelizaro nr 96 Leren Creëren Adri Proveniers nr 97 Simlandscape Rob de Waard nr 98 Design Team Communication Ad den Otter nr 99 Humaan-Ecologisch Georiënteerde Woningbouw Juri Czabanowski nr 100 Hambase Martin de Wit nr 101 Sound Transmission through Pipe Systems and into Building Structures Susanne Bron-van der Jagt nr 102 Het Bouwkundig Contrapunt Jan Francis Boelen nr 103 A Framework for a Multi-Agent Planning Support System Dick Saarloos nr 104 Bracing Steel Frames with Calcium Silicate Element Walls Bright Mweene Ng’andu nr 105 Naar een Nieuwe Houtskeletbouw F.N.G. De Medts

nr 106 and 107 Niet gepubliceerd nr 108 Geborgenheid T.E.L. van Pinxteren nr 109 Modelling Strategic Behaviour in Anticipation of Congestion Qi Han nr 110 Reflecties op het Woondomein Fred Sanders nr 111 On Assessment of Wind Comfort by Sand Erosion Gábor Dezsö nr 112 Bench Heating in Monumental Churches Dionne Limpens-Neilen nr 113 RE. Architecture Ana Pereira Roders nr 114 Toward Applicable Green Architecture Usama El Fiky nr 115 Knowledge Representation under Inherent Uncertainty in a Multi-Agent System for Land Use Planning Liying Ma nr 116 Integrated Heat Air and Moisture Modeling and Simulation Jos van Schijndel nr 117 Concrete Behaviour in Multiaxial Compression J.P.W. Bongers nr 118 The Image of the Urban Landscape Ana Moya Pellitero nr 119 The Self-Organizing City in Vietnam Stephanie Geertman

nr 120 A Multi-Agent Planning Support System for Assessing Externalities of Urban Form Scenarios Rachel Katoshevski-Cavari nr 121 Den Schulbau Neu Denken, Fühlen und Wollen Urs Christian Maurer-Dietrich nr 122 Peter Eisenman Theories and Practices Bernhard Kormoss nr 123 User Simulation of Space Utilisation Vincent Tabak nr 125 In Search of a Complex System Model Oswald Devisch nr 126 Lighting at Work: Environmental Study of Direct Effects of Lighting Level and Spectrum on Psycho-Physiological Variables Grazyna Górnicka nr 127 Flanking Sound Transmission through Lightweight Framed Double Leaf Walls Stefan Schoenwald nr 128 ˙ Bounded Rationality and Spatio-Temporal Pedestrian Shopping Behavior Wei Zhu nr 129 Travel Information: Impact on Activity Travel Pattern Zhongwei Sun nr 130 Co-Simulation for Performance Prediction of Innovative Integrated Mechanical Energy Systems in Buildings � Marija Trcka nr 131 Niet gepubliceerd

nr 132 Architectural Cue Model in Evacuation Simulation for Underground Space Design Chengyu Sun

nr 143 Modelling Life Trajectories and Transport Mode Choice Using Bayesian Belief Networks Marloes Verhoeven

nr 133 Uncertainty and Sensitivity Analysis in Building Performance Simulation for Decision Support and Design Optimization Christina Hopfe

nr 144 Assessing Construction Project Performance in Ghana William Gyadu-Asiedu

nr 134 Facilitating Distributed Collaboration in the AEC/FM Sector Using Semantic Web Technologies Jacob Beetz nr 135 Circumferentially Adhesive Bonded Glass Panes for Bracing Steel Frame in Façades Edwin Huveners nr 136 Influence of Temperature on Concrete Beams Strengthened in Flexure with CFRP Ernst-Lucas Klamer nr 137 Sturen op Klantwaarde Jos Smeets nr 139 Lateral Behavior of Steel Frames with Discretely Connected Precast Concrete Infill Panels Paul Teewen nr 140 Integral Design Method in the Context of Sustainable Building Design Perica Savanovic´ nr 141 Household Activity-Travel Behavior: Implementation of Within-Household Interactions Renni Anggraini nr 142 Design Research in the Netherlands 2010 Henri Achten

nr 145 Empowering Seniors through Domotic Homes Masi Mohammadi nr 146 An Integral Design Concept for Ecological Self-Compacting Concrete Martin Hunger nr 147 Governing Multi-Actor Decision Processes in Dutch Industrial Area Redevelopment Erik Blokhuis nr 148 A Multifunctional Design Approach for Sustainable Concrete Götz Hüsken nr 149 Quality Monitoring in Infrastructural Design-Build Projects Ruben Favié nr 150 Assessment Matrix for Conservation of Valuable Timber Structures Michael Abels nr 151 Co-simulation of Building Energy Simulation and Computational Fluid Dynamics for Whole-Building Heat, Air and Moisture Engineering Mohammad Mirsadeghi nr 152 External Coupling of Building Energy Simulation and Building Element Heat, Air and Moisture Simulation Daniel Cóstola

nr 153 Adaptive Decision Making In Multi-Stakeholder Retail Planning Ingrid Janssen nr 154 Landscape Generator Kymo Slager nr 155 Constraint Specification in Architecture Remco Niemeijer nr 156 A Need-Based Approach to Dynamic Activity Generation Linda Nijland nr 157 Modeling Office Firm Dynamics in an Agent-Based Micro Simulation Framework Gustavo Garcia Manzato nr 158 Lightweight Floor System for Vibration Comfort Sander Zegers nr 159 Aanpasbaarheid van de Draagstructuur Roel Gijsbers nr 160 'Village in the City' in Guangzhou, China Yanliu Lin nr 161 Climate Risk Assessment in Museums Marco Martens nr 162 Social Activity-Travel Patterns Pauline van den Berg nr 163 Sound Concentration Caused by Curved Surfaces Martijn Vercammen nr 164 Design of Environmentally Friendly Calcium Sulfate-Based Building Materials: Towards an Improved Indoor Air Quality Qingliang Yu

nr 165 Beyond Uniform Thermal Comfort on the Effects of Non-Uniformity and Individual Physiology Lisje Schellen nr 166 Sustainable Residential Districts Gaby Abdalla nr 167 Towards a Performance Assessment Methodology using Computational Simulation for Air Distribution System Designs in Operating Rooms Mônica do Amaral Melhado nr 168 Strategic Decision Modeling in Brownfield Redevelopment Brano Glumac nr 169 Pamela: A Parking Analysis Model for Predicting Effects in Local Areas Peter van der Waerden nr 170 A Vision Driven Wayfinding Simulation-System Based on the Architectural Features Perceived in the Office Environment Qunli Chen nr 171 Measuring Mental Representations Underlying Activity-Travel Choices Oliver Horeni nr 172 Modelling the Effects of Social Networks on Activity and Travel Behaviour Nicole Ronald nr 173 Uncertainty Propagation and Sensitivity Analysis Techniques in Building Performance Simulation to Support Conceptual Building and System Design Christian Struck nr 174 Numerical Modeling of Micro-Scale Wind-Induced Pollutant Dispersion in the Built Environment Pierre Gousseau

nr 175 Modeling Recreation Choices over the Family Lifecycle Anna Beatriz Grigolon

nr 185 A Distributed Dynamic Simulation Mechanism for Buildings Automation and Control Systems Azzedine Yahiaoui

nr 176 Experimental and Numerical Analysis of Mixing Ventilation at Laminar, Transitional and Turbulent Slot Reynolds Numbers Twan van Hooff

nr 186 Modeling Cognitive Learning of Urban Networks in Daily Activity-Travel Behavior ¸ Sehnaz Cenani Durmazoglu �

nr 177 Collaborative Design Support: Workshops to Stimulate Interaction and Knowledge Exchange Between Practitioners Emile M.C.J. Quanjel

nr 187 Functionality and Adaptability of Design Solutions for Public Apartment Buildings in Ghana Stephen Agyefi-Mensah

nr 178 Future-Proof Platforms for Aging-in-Place Michiel Brink

nr 188 A Construction Waste Generation Model for Developing Countries Lilliana Abarca-Guerrero

nr 179 Motivate: A Context-Aware Mobile Application for Physical Activity Promotion Yuzhong Lin

nr 189 Synchronizing Networks: The Modeling of Supernetworks for Activity-Travel Behavior Feixiong Liao

nr 180 Experience the City: Analysis of Space-Time Behaviour and Spatial Learning Anastasia Moiseeva

nr 190 Time and Money Allocation Decisions in Out-of-Home Leisure Activity Choices Gamze Zeynep Dane

nr 181 Unbonded Post-Tensioned Shear Walls of Calcium Silicate Element Masonry Lex van der Meer

nr 191 How to Measure Added Value of CRE and Building Design Rianne Appel-Meulenbroek

nr 182 Construction and Demolition Waste Recycling into Innovative Building Materials for Sustainable Construction in Tanzania Mwita M. Sabai

nr 192 Secondary Materials in Cement-Based Products: Treatment, Modeling and Environmental Interaction Miruna Florea

nr 183 Durability of Concrete with Emphasis on Chloride Migration Przemys�aw Spiesz nr 184 Computational Modeling of Urban Wind Flow and Natural Ventilation Potential of Buildings Rubina Ramponi

nr 193 Concepts for the Robustness Improvement of Self-Compacting Concrete: Effects of Admixtures and Mixture Components on the Rheology and Early Hydration at Varying Temperatures Wolfram Schmidt

nr 194 Modelling and Simulation of Virtual Natural Lighting Solutions in Buildings Rizki A. Mangkuto nr 195 Nano-Silica Production at Low Temperatures from the Dissolution of Olivine - Synthesis, Tailoring and Modelling Alberto Lazaro Garcia nr 196 Building Energy Simulation Based Assessment of Industrial Halls for Design Support Bruno Lee nr 197 Computational Performance Prediction of the Potential of Hybrid Adaptable Thermal Storage Concepts for Lightweight Low-Energy Houses Pieter-Jan Hoes nr 198 Application of Nano-Silica in Concrete George Quercia Bianchi nr 199 Dynamics of Social Networks and Activity Travel Behaviour Fariya Sharmeen

nr 204 Geometry and Ventilation: Evaluation of the Leeward Sawtooth Roof Potential in the Natural Ventilation of Buildings Jorge Isaac Perén Montero nr 205 Computational Modelling of Evaporative Cooling as a Climate Change Adaptation Measure at the Spatial Scale of Buildings and Streets Hamid Montazeri nr 206 Local Buckling of Aluminium Beams in Fire Conditions Ronald van der Meulen nr 207 Historic Urban Landscapes: Framing the Integration of Urban and Heritage Planning in Multilevel Governance Loes Veldpaus nr 208 Sustainable Transformation of the Cities: Urban Design Pragmatics to Achieve a Sustainable City Ernesto Antonio Zumelzu Scheel

nr 200 Building Structural Design Generation and Optimisation including Spatial Modification Juan Manuel Davila Delgado

nr 209 Development of Sustainable Protective Ultra-High Performance Fibre Reinforced Concrete (UHPFRC): Design, Assessment and Modeling Rui Yu

nr 201 Hydration and Thermal Decomposition of Cement/Calcium-Sulphate Based Materials Ariën de Korte

nr 210 Uncertainty in Modeling Activity-Travel Demand in Complex Uban Systems Soora Rasouli

nr 202 Republiek van Beelden: De Politieke Werkingen van het Ontwerp in Regionale Planvorming Bart de Zwart

nr 211 Simulation-based Performance Assessment of Climate Adaptive Greenhouse Shells Chul-sung Lee

nr 203 Effects of Energy Price Increases on Individual Activity-Travel Repertoires and Energy Consumption Dujuan Yang

nr 212 Green Cities: Modelling the Spatial Transformation of the Urban Environment using Renewable Energy Technologies Saleh Mohammadi

nr 213 A Bounded Rationality Model of Short and Long-Term Dynamics of Activity-Travel Behavior Ifigeneia Psarra nr 214 Effects of Pricing Strategies on Dynamic Repertoires of Activity-Travel Behaviour Elaheh Khademi nr 215 Handstorm Principles for Creative and Collaborative Working Frans van Gassel nr 216 Light Conditions in Nursing Homes: Visual Comfort and Visual Functioning of Residents Marianne M. Sinoo

nr 223 Personalized Route Finding in Multimodal Transportation Networks Jianwe Zhang nr 224 The Design of an Adaptive Healing Room for Stroke Patients Elke Daemen nr 225 Experimental and Numerical Analysis of Climate Change Induced Risks to Historic Buildings and Collections Zara Huijbregts nr 226 Wind Flow Modeling in Urban Areas Through Experimental and Numerical Techniques Alessio Ricci

nr 217 Woonsporen: De Sociale en Ruimtelijke Biografie van een Stedelijk Bouwblok in de Amsterdamse Transvaalbuurt Hüseyin Hüsnü Yegenoglu

nr 227 Clever Climate Control for Culture: Energy Efficient Indoor Climate Control Strategies for Museums Respecting Collection Preservation and Thermal Comfort of Visitors Rick Kramer

nr 218 Studies on User Control in Ambient Intelligent Systems Berent Willem Meerbeek

nr 228 Fatigue Life Estimation of Metal Structures Based on Damage Modeling Sarmediran Silitonga

nr 219 Daily Livings in a Smart Home: Users’ Living Preference Modeling of Smart Homes Erfaneh Allameh

nr 229 A multi-agents and occupancy based strategy for energy management and process control on the room-level Timilehin Moses Labeodan

nr 220 Smart Home Design: Spatial Preference Modeling of Smart Homes Mohammadali Heidari Jozam

nr 230 Environmental assessment of Building Integrated Photovoltaics: Numerical and Experimental Carrying Capacity Based Approach Michiel Ritzen

nr 221 Wonen: Discoursen, Praktijken, Perspectieven Jos Smeets nr 222 Personal Control over Indoor Climate in Offices: Impact on Comfort, Health and Productivity Atze Christiaan Boerstra

nr 231 Performance of Admixture and Secondary Minerals in Alkali Activated Concrete: Sustaining a Concrete Future Arno Keulen

nr 232 World Heritage Cities and Sustainable Urban Development: Bridging Global and Local Levels in Monitoring the Sustainable Urban Development of World Heritage Cities Paloma C. Guzman Molina nr 233 Stage Acoustics and Sound Exposure in Performance and Rehearsal Spaces for Orchestras: Methods for Physical Measurements Remy Wenmaekers

nr 241 Gap-Theoretical Analyses of Residential Satisfaction and Intention to Move Wen Jiang nr 242 Travel Satisfaction and Subjective Well-Being: A Behavioral Modeling Perspective Yanan Gao nr 243 Building Energy Modelling to Support the Commissioning of Holistic Data Centre Operation Vojtech Zavrel

nr 234 Municipal Solid Waste Incineration (MSWI) Bottom Ash: From Waste to Value Characterization, Treatments and Application Pei Tang

nr 244 Regret-Based Travel Behavior Modeling: An Extended Framework Sunghoon Jang

nr 235 Large Eddy Simulations Applied to Wind Loading and Pollutant Dispersion Mattia Ricci

nr 245 Towards Robust Low-Energy Houses: A Computational Approach for Performance Robustness Assessment using Scenario Analysis Rajesh Reddy Kotireddy

nr 236 Alkali Activated Slag-Fly Ash Binders: Design, Modeling and Application Xu Gao nr 237 Sodium Carbonate Activated Slag: Reaction Analysis, Microstructural Modification & Engineering Application Bo Yuan nr 238 Shopping Behavior in Malls Widiyani nr 239 Smart Grid-Building Energy Interactions: Demand Side Power Flexibility in Office Buildings Kennedy Otieno Aduda nr 240 Modeling Taxis Dynamic Behavior in Uncertain Urban Environments Zheng Zhong

nr 246 Development of sustainable and functionalized inorganic binder-biofiber composites Guillaume Doudart de la Grée nr 247 A Multiscale Analysis of the Urban Heat Island Effect: From City Averaged Temperatures to the Energy Demand of Individual Buildings Yasin Toparlar nr 248 Design Method for Adaptive Daylight Systems for buildings covered by large (span) roofs Florian Heinzelmann nr 249 Hardening, high-temperature resistance and acid resistance of one-part geopolymers Patrick Sturm

nr 250 Effects of the built environment on dynamic repertoires of activity-travel behaviour Aida Pontes de Aquino

nr 259 A Sustainable Industrial Site Redevelopment Planning Support System Tong Wang

nr 251 Modeling for auralization of urban environments: Incorporation of directivity in sound propagation and analysis of a framework for auralizing a car pass-by Fotis Georgiou

nr 260 Efficient storage and retrieval of detailed building models: Multi-disciplinary and long-term use of geometric and semantic construction information Thomas Ferdinand Krijnen

nr 252 Wind Loads on Heliostats and Photovoltaic Trackers Andreas Pfahl

nr 261 The users’ value of business center concepts for knowledge sharing and networking behavior within and between organizations Minou Weijs-Perrée

nr 253 Approaches for computational performance optimization of innovative adaptive façade concepts Roel Loonen nr 254 Multi-scale FEM-DEM Model for Granular Materials: Micro-scale boundary conditions, Statics, and Dynamics Jiadun Liu nr 255 Bending Moment - Shear Force Interaction of Rolled I-Shaped Steel Sections Rianne Willie Adriana Dekker nr 256 Paralympic tandem cycling and handcycling: Computational and wind tunnel analysis of aerodynamic performance Paul Fionn Mannion nr 257 Experimental characterization and numerical modelling of 3D printed concrete: Controlling structural behaviour in the fresh and hardened state Robert Johannes Maria Wolfs nr 258 Requirement checking in the building industry: Enabling modularized and extensible requirement checking systems based on semantic web technologies Chi Zhang

nr 262 Characterization and improvement of aerodynamic performance of vertical axis wind turbines using computational fluid dynamics (CFD) Abdolrahim Rezaeiha nr 263 In-situ characterization of the acoustic impedance of vegetated roofs Chang Liu nr 264 Occupancy-based lighting control: Developing an energy saving strategy that ensures office workers’ comfort Christel de Bakker nr 265 Stakeholders-Oriented Spatial Decision Support System Cahyono Susetyo nr 266 Climate-induced damage in oak museum objects Rianne Aleida Luimes nr 267 Towards individual thermal comfort: Model predictive personalized control of heating systems Katarina Katic

nr 268 Modelling and Measuring Quality of Urban Life: Housing, Neighborhood, Transport and Job Lida Aminian nr 269 Optimization of an aquifer thermal energy storage system through integrated modelling of aquifer, HVAC systems and building Basar Bozkaya nr 270 Numerical modeling for urban sound propagation: developments in wave-based and energy-based methods Raúl Pagán Muñoz nr 271 Lighting in multi-user office environments: improving employee wellbeing through personal control Sanae van der Vleuten-Chraibi nr 272 A strategy for fit-for-purpose occupant behavior modelling in building energy and comfort performance simulation Isabella I. Gaetani dell’Aquila d’Aragona nr 273 Een architectuurhistorische waardestelling van naoorlogse woonwijken in Nederland: Het voorbeeld van de Westelijke Tuinsteden in Amsterdam Eleonore Henriette Marie Mens nr 274 Job-Housing Co-Dependent Mobility Decisions in Life Trajectories Jia Guo nr 275 A user-oriented focus to create healthcare facilities: decision making on strategic values Emilia Rosalia Catharina Maria Huisman nr 276 Dynamics of plane impinging jets at moderate Reynolds numbers – with applications to air curtains Adelya Khayrullina

nr 277 Valorization of Municipal Solid Waste Incineration Bottom Ash - Chemical Nature, Leachability and Treatments of Hazardous Elements Qadeer Alam nr 278 Treatments and valorization of MSWI bottom ash - application in cement-based materials Veronica Caprai nr 279 Personal lighting conditions of office workers - input for intelligent systems to optimize subjective alertness Juliëtte van Duijnhoven nr 280 Social influence effects in tourism travel: air trip itinerary and destination choices Xiaofeng Pan nr 281 Advancing Post-War Housing: Integrating Heritage Impact, Environmental Impact, Hygrothermal Risk and Costs in Renovation Design Decisions Lisanne Claartje Havinga nr 282 Impact resistant ultra-high performance fibre reinforced concrete: materials, components and properties Peipeng Li nr 283 Demand-driven Science Parks: The Perceived Benefits and Trade-offs of Tenant Firms with regard to Science Park Attributes Wei Keat Benny Ng nr 284 Raise the lantern; how light can help to maintain a healthy and safe hospital environment focusing on nurses Maria Petronella Johanna Aarts nr 285 Modelling Learning and Dynamic Route and Parking Choice Behaviour under Uncertainty Elaine Cristina Schneider de Carvalho

nr 286 Identifying indoor local microclimates for safekeeping of cultural heritage Karin Kompatscher

nr 295 Auditory Distraction in Open-Plan Study Environments in Higher Education Pieternella Elizabeth Braat-Eggen

nr 287 Probabilistic modeling of fatigue resistance for welded and riveted bridge details. Resistance models and estimation of uncertainty. Davide Leonetti

nr 296 Exploring the effect of the sound environment on nurses' task performance: an applied approach focusing on prospective memory Jikke Reinten

nr 288 Performance of Layered UHPFRC under Static and Dynamic Loads: Effects of steel fibers, coarse aggregates and layered structures Yangyueye Cao

nr 297 Design and performance of water resistant cementitious materials– Mechanisms, evaluation and applications Zhengyao Qu

nr 289 Photocatalytic abatement of the nitrogen oxide pollution: synthesis, application and long-term evaluation of titania-silica composites Yuri Hendrix nr 290 Assessing knowledge adoption in postdisaster reconstruction: Understanding the impact of hazard-resistant construction knowledge on reconstruction processes of self-recovering communities in Nepal and the Philippines Eefje Hendriks nr 291 Locating electric vehicle charging stations: A multi-agent based dynamic simulation Seheon Kim nr 292 De invloed van Lean Management op de beheersing van het bouwproces Wim van den Bouwhuijsen nr 293 Neighborhood Environment and Physical Activity of Older Adults Zhengying Liu nr 294 Practical and continuous luminance distribution measurements for lighting quality Thijs Willem Kruisselbrink

nr 298 Design Optimization of Seasonal Thermal Energy Storage Integrated District Heating and Cooling System: A Modeling and Simulation Approach Luyi Xu nr 299 Land use and transport: Integrated approaches for planning and management Zhongqi Wang nr 300 Multi-disciplinary optimization of building spatial designs: co-evolutionary design process simulations, evolutionary algorithms, hybrid approaches Sjonnie Boonstra nr 301 Modeling the spatial and temporal relation between urban land use, temperature, and energy demand Hung-Chu Chen nr 302 Seismic retrofitting of masonry walls with flexible deep mounted CFRP strips Ömer Serhat Türkmen nr 303 Coupled Aerostructural Shape and Topology Optimization of Horizontal-Axis Wind Turbine Rotor Blades Zhijun Wang

nr 304 Valorization of Recycled Waste Glass and Converter Steel Slag as Ingredients for Building Materials: Hydration and Carbonation Studies Gang Liu nr 305 Low-Carbon City Development based on Land Use Planning Gengzhe Wang nr 306 Sustainable energy transition scenario analysis for buildings and neighborhoods Data driven optimization Shalika Saubhagya Wickramarachchi Walker nr 307 In-between living and manufactured: an exploratory study on biobuilding components for building design Berrak Kirbas Akyurek nr 308 Development of alternative cementitious binders and functionalized materials: design, performance and durability Anna Monika Kaja nr 309 Development a morphological approach for interactive kinetic façade design: Improving multiple occupants’ visual comfort Seyed Morteza Hosseini nr 310 PV in urban context: modeling and simulation strategies for analyzing the performance of shaded PV systems Ádám Bognár nr 311 Life Trajectory, Household Car Ownership Dynamics and Home Renewable Energy Equipment Adoption Gaofeng Gu nr 312 Impact of Street-Scale Built Environment on Walking/Cycling around Metro Stations Yanan Liu

nr 313 Advances in Urban Traffic Network Equilibrium Models and Algorithms Dong Wang nr 314 Development of an uncertainty analysis framework for model-based consequential life cycle assessment: application to activity-based modelling and life cycle assessment of multimodal mobility Paul Martin Baustert nr 315 Variable stiffness and damping structural joints for semi-active vibration control Qinyu Wang nr 316 Understanding Carsharing-Facilitating Neighborhood Preferences Juan Wang nr 317 Dynamic alignment of Corporate Real Estate to business strategies: An empirical analysis using historical data and in-depth modelling of decision making Howard Cooke nr 318 Local People Matter: Towards participatory governance of cultural heritage in China Ji Li nr 319 Walkability and Walkable Healthy Neighborhoods Bojing Liao nr 320 Light directionality in design of healthy offices: exploration of two methods Parisa Khademagha nr 321 Room acoustic modeling with the timedomain discontinuous Galerkin method Huiqing Wang nr 322 Sustainable insulating lightweight materials for enhancing indoor building performance: miscanthus, aerogel and nano-silica Yuxuan Chen

nr 323 Computational analysis of the impact of façade geometrical details on wind flow and pollutant dispersion Xing Zheng

Implementation of renewable energy strategies in cities is imperative for sustainable development. Wind energy has turned into one of the fastest-growing renewable energy resources. Particularly, the use of wind turbines in urban environments has attracted increasing attention. Urban airflow can be locally accelerated to boost wind speeds and energy yields when it passes over compact high-rise buildings. However, the wind tends to be more turbulent and less predictable in urban areas, due to the complexity and the heterogeneous terrain roughness in the built environment. Therefore, high-resolution and accurate urban wind resource assessments are crucial for providing detailed information regarding the potential installation sites. As an effective tool for early-stage urban design, computational fluid dynamics (CFD) simulation has been recognized as a suitable tool for systematic parametric analysis of urban wind energy potential. The present thesis aims to achieve further insights into the impact of different morphological parameters on urban wind energy potential around high-rise buildings in close proximity. This can support the sustainable urban design of a compact city to maximize wind energy harvesting and satisfy sustainable development requirements.

DEPARTMENT OF THE BUILT ENVIRONMENT

PhD Thesis by Yu-Hsuan Juan

Articles inside

References

Biography

4.5 Discussion

4.4.2 Impact of building corner shape

5.1 Introduction

5 Urban wind energy potential for a realistic high-rise urban area

4.4.1 Impact of urban density

4.3.3 Computational settings

4.3.2 Computational domain and grid

4.2.1 Turbulence model sensitivity analysis

4.2 CFD validation study

4 Urban wind energy potential: Impacts of urban density and layout

3.5.5 Impact of wind direction

4.1 Introduction

3.5.4 Impact of wind turbine type and orientation

3.5.3 Impact of corner radius

3 Urban wind energy potential: Impacts of building corner modifications

3.5.2 Impact of chamfer length

3.4.3 Grid-sensitivity analysis

2.7 Conclusions

3.2.2 CFD validation: computational settings and results

3.3 Test cases

2.6 Limitations of the study

Discussion ...................................................................................................................................... 131

buildings (d

Summary and Conclusions.......................................................................................................... 133

Summary

1.4 Thesis outline

2.2.2 CFD validation: computational domain and grid

2.2.3 CFD validation: other computational settings

2 Urban wind energy potential: Impact of building arrangement and height