Application of Multi-Objective Climate Optimization on Residential in Singapore by lingbanliang

M.Arch 2017

Ling Ban Liang

Application of Multi-Objective Climate Optimization on Residential in Singapore

Acknowledgements I am extremely grateful to my thesis supervisor, Dr John Alstan Jakubiec, for his guidance and immense patience throughout the Masters course. I would also like to thank DP Sustainable Design for taking time off their busy schedule to respond to my queries as well as their readiness in offering resources which helped in the production of this document. Lastly, I would like to thank my parents, brother and also Felicia for their unwavering support and understanding that provided a strong platform for me to pursue this thesis without any worries.

Table of Contents

01 | Importance of Climate Optimization 1.1 Goals of Climate Optimization in Architecture 1.2 Examples of Climate Optimization 1.3 Stakeholders and Design Stage Implementation 1.4 SImulation Software/ Tools 1.5 Current Process in Industry 02 | Characteristics of Singapore Climate 2.1 Singapore Climatic Data 2.2 Contrasting Climatic Optimization Variables 2.3 General Strategy for Tropical Humid Climate 03 | Sustainability in Residential Design 3.1 General Strategies for Climate Adaptation 3.2 Sustainable Strategies for Residential in Singapore 3.3 Trends in Residential Design 3.4 Evaluation Metrics 04 | Multi-Objective Climate Optimization 4.1 Limitations of a Linear Optimization Strategy 4.2 Introduction to Multi-Objective Optimization 4.3 Theory of Pareto Optimal 4.4 Application to Design Process 05 | Inputs of Optimization 5.1 Input Selection Criteria 5.2 Performance of the Building Massing 5.3 Performance of Individual Units 06 | Designing Constraints 6.1 Design Exploration through Constraint Design 6.2 Design Space for Parametrically Generated Massing 6.3 Design Space for Parametrically Generated Unit

07 | Parametric Geometric Explorations 7.1 Geometry as a Climatic Strategy 7.2 Iteration 1: Voronoi Cells 7.3 Iteration 2: Modular Grid Blocks 7.4 Iteration 3: Snake Voronoi Cells 7.5 Iteration 4: Voronoi Extrusions 7.6 Iteration 5: Shifted Voronoi Blocks 08 | Site Selection 8.1 Population Distribution 8.2 Site Analysis: Hougang Central 8.3 Site Analysis: Punggol Waterway 8.4 Site Analysis: Queenstown Mei Chin 09 | Visualization of Results 9.1 Understanding Multi-Objective Optimization Data 9.2 Representing Multi-Objective Optimization Data 9.3 Data Display Framework 9.4 Hougang Central Approximated Pareto Front 9.5 Punggol Waterway Approximated Pareto Front 9.6 Queenstown Mei Chin Approximated Pareto Front 10 | Parametric Floorplan Explorations 10.1 Aim of Search 10.2 Iteration 1: Central Living Room 10.3 Iteration 2: Convex Living Room 10.4 Iteration 3: Spine and Branch 10.5 Iteration 4: Circular Joint 10.6 Iteration 5: Orthogonal Joint 11) Thesis Design 11.1 Selection of Punggol Waterway 11.2 Selection of Massing Iteration 11.3 Design Concept 11.4 Program Distribution 11.5 Typical Floorplan

Necessity

Unnecessary

Qualitative Aspect

Unfulfilled Potential

Uncontrolled Design

Quantitative Aspect

Application of Multi-Objective Climate Optimization on Residential

Abstract Sustainable architecture has been gaining traction in recent years due to its many benefits. With modern day technology, more accurate predictions of a buildingâ&#x20AC;&#x2122;s performance can be determined. Along with increased levels of technology comes along a new wave of data design which is more often than not linked to aspects of sustainable architecture. Multi-objective optimization offers itself as a design tool which can help architects perform informed trade-offs, drawing the link between data design and building performance. Ranging from qualitative feedback to quantitative simulation results, the thesis tries to use multi-objective climate optimization to question the relationship between an architectâ&#x20AC;&#x2122;s design wants and the calculation power of a computer.

Importance of Climate Optimization i. Goals of Climate Optimization in Architecture ii. Examples of Climate Optimization iii. Stakeholders and Design Stage Implementation iv. Simulation Software/Tools v. Current Process in Industry

01 | Importance of Climate Optimization

Introduction Green sustainable architecture has been gaining traction in recent years due to benefits like tax breaks, long term savings as well as higher property values.1 Big architecture firms in Singapore like DP Architects are starting to have their own in-house sustainable design department. Their primary role is to advise the architects on sustainable design solutions, supported through climate simulations. This chapter will be discussing about: i. The goals of Climate Optimization in Architecture ii. Examples of Climate Optimization in Singapore iii. Current tools that aid architects with quantitative analysis The discussions will attempt to define climate optimization in the context of this thesis and act as a primer for later chapters.

01 | Importance of Climate Optimization

Architecture

Goals of Climate Architecture

Optimization

Climate Responsive Design Climate responsive architecture takes into account site conditions like sun path and wind flow. Making use of this knowledge, architects can then apply geometrical constraints to their designs as well as allocate programs in an informed manner. The ultimate goal is to design comfortable yet energy efficient spaces. Optimization Optimization is a technique to help streamline designs into a few options which adhere to the single or multiple objectives as set by the designer at the start. Climate Optimization In terms of building climate Architecture optimization, the goals are to reduce dependency on mechanical systems. Optimization in this case can refer to building orientation to reduce solar irradiation or Interior Building to face a prevailing wind direction. These Space represent passive strategies that tap on a Spatial siteâ&#x20AC;&#x2122;s Energy characteristicsComfort to naturally cool or heat Needs up a building based on location.2

Building

Interior Space

Energy Needs

Spatial Comfort Fig 1a. Breakdown of Architecture

Architecture can be broadly classified into two big brackets:

i. Building - an inanimate object that cannot move on its own ii. Interior Spaces - these are designed for occupants who are able to make everyday choices Comfort range can be represented within a small area on a psychometric chart. Using this chart, one can then plot out viable strategies for particular climates.

Comfort and Range

For example, ventilation reduction of solar heat gain are strategies for tropical humid climates.3 Future explorations on comfort within interior spaces will thus be based upon this metric. Goal of Thesis Project

temperature Fig 1b. Comfort Range

moisture

Comfort Range

This thesis explores how multiobjective climate optimization can help architects make informed design decisions during residential design. Starting from the exterior building geometry, and down to interior floorplans, the goal is to show how the system can work hand in hand with design goals of an architect to produce a new climatically optimised residential typology.

01 | Importance of Climate Optimization

Fig 2a. Malay Vernacular

Examples of Climate Optimization in Singapore Singapore is a multi-cultural society with many overseas architectural influences. British officials brought with them colonial designs while Chinese businessmen brought over early versions of the shophouse typology. Malay Traditional House Construction Materials Before the influx of building styles, the vernacular was that of a malay traditional house, constructed out of locally sourced materials. Early settlers designed pitched roofs to allow rain water to slide off, utilised light weight materials to reduce thermal mass and adapted large overhangs to shade the living spaces. From this vernacular house, one can pick out the main climatic conditions which foreign architecture must respond to.

Fig 2c. Five-foot way

Fig 2b. Shophouse Lightwell

Chinese Peranakan Shophouse Light Well The shophouse has three main sections, the first being a public area whereby residents would entertain guests and this is directly connected to the outside. Separating the public zone from a private area is a light well that is open to sky. Due to the long geometry which shophouses possess, it creates dark spaces within. As such a lightwell is incorporated to allow daylight to filter through.

Five Foot Way In 1822, Raffles made it a rule to incorporate five foot ways, which are about 1.5m wide, as part of a shophouse facade. This was a strategy to provide shade and shelter from the sun and rain. The five foot ways were an addition to colonial style shophouses which had no sheltered shopfronts. This is because dry bulb temperatures in temperate european climates are much cooler and solar heating is not as uncomfortable as that of Singapore.

Besides admitting natural light, the opening can also be a ventilation strategy. Wind is able to flow through living spaces before being released through this light well. In order to combat high levels of rainfall, pitch roofs were extended to direct rain fall into the space below the light well. This design helps in channeling of rainwater away from main living spaces.

Adaptation of Overseas Styles to Local Climate The above examples show early attempts in optimizing to Singaporeâ&#x20AC;&#x2122;s climate. These were performed in the past, sometimes even without the help of architects.

In the current age, parametric modelling or simulation tools could be used to push forth this idea of climate optimization.

01 | Importance of Climate Optimization

Stakeholders

Stakeholders and Implementation

Design

Stage

Some of the main stakeholders in architecture include local authorities, the architect and the client. Each of them have an area of expertise that will theoretically translate into a well thought out architecture design.4 With the increase in demand for green buildings, government rating systems are becoming more prevalent. Achieving a high rating not only helps in increasing value of a development, it also helps in decreasing operational costs. This has led to a rise in green buildings like CapitaGreen by Toyo Ito and South Beach Towers by Norman Foster. Office towers like these stand out from the many international style glazed blocks which dominate every cityâ&#x20AC;&#x2122;s skyline. Decreased energy consumption has also led directly or indirectly to decreased operational costs.5

local authorities

client

architect

building codes

building budget

design of building

Fig 3a. Stakeholders in Sustainable Architecture

Fig 3b. South Beach Towers (foster & partners)6

Fig 3c. CapitaGreen (cct.com.sg)7

Climate Optimization within the Industry

As such, the motivation behind pursuit of climate optimization is becoming more apparent. Firstly, local authorities are presented with a high performance and energy efficient building. Clients receive a green rated building which is easy to sell and maintain while the architect can claim to have designed a unique building.

Since passive strategies are large scale moves that are best implemented during the design stage for maximum savings, climate consultants and engineers are roped in at the start to help define certain directions. Subsequent design changes are quickly tested using simulation software so that architects can perform informed tradeoffs. With existing computational tools and computing strength, simulations are getting increasingly fast and accurate. As such, complex models can be tested quickly and data design is also made possible. Companies like DP Architects adopt the above strategies, providing real time climate design support for architects and contributing to numerous green mark certified projects.

01 | Importance of Climate Optimization

Simulation Software/Tools Simulations can help architects with making informed design decisions. Available software aim to predict wind flow, shadow range, amount of daylit space and solar irradiance performance. The obtained metrics can be used for a variety of studies. For example, solar irradiation is the amount of radiant flux on a surface. Besides being useful for testing total sunlight incident on a building, it can also be used to identify key areas for photovoltaic panel placement. Below is a list of existing software and their capabilities:

Solar Simulation Solar Simulation

DIVA DIVA

Ladybug Ladybug

Honeybee Honeybee

Ecotect Ecotect

Solar Radiation Solar Radiation Daylighting Daylighting Point-in-Time Point-in-Time Daylight Factor Daylight Factor Climate Based Climate Based Daylight Autonomy Daylight Autonomy Useful Daylight Illuminance Useful Daylight Illuminance Annual Sun Exposure Annual Sun Exposure Fig 4a. Solar Simulation Table

Wind Simulation Wind Simulation

Computation Computation Fluid Dynamics (CFD) Fluid Dynamics (CFD)

Design Builder Design Builder

BIM HVAC BIM HVAC

Fig 4b. Wind Simulation Table

All the solar simulation softwares can be connected to Rhino/Grasshopper either directly or through an Application Programming Interface (API). In general, simulations with Ecotect are much slower than the others. Wind simulation takes a significantly longer time to be completed. With the exception of Butterfly, other softwares are not directly connected to Rhino/Grasshopper. The compability of software and simulation time would be explored further in Chapter 5: Inputs of Optimization.

Butterfly Butterfly

Autodesk Flow Autodesk Flow

01 | Importance of Climate Optimization

Designed building form is prepared for energy simulation.

Solar Study Massing is tested for solar performance. Surfaces are studied for relative solar performance.

Wind Flow Study

Illuminance Study

Areas of massing which experience high wind speeds are smoothen out.

Daylight performance is tested to determine if lighting levels are sufficient. Remedies are applied if otherwise.

Revised Model

Qualitative

Quantitative

Qualitative

Building Model

Final model achieved through independent simulations.

Fig 5. Work Process

Qualitative

Building Model

Designed building form is prepared for energy simulation. i. Qualitative - Form

Site

Current Process in Industry

Generation for

Qualitative

Quantitative

A typical optimization process in the industry can be split into three parts:

Form finding process is largely based on the architectâ&#x20AC;&#x2122;s perception of site and geometry. Other considerations might include material length and dimensions. Ultimately Multi Objective Pareto Front qualitatively. Optimizationthe building form is derivedAnalysis

Multiple variables Qualitative analysis of Quantitativemassing - models. Climate result in a paretoii. front which provides Optimization/ Simulations a range of models.

Separate simulations are performed for different metrics. Solar, wind flow and illuminance studies are done on the same model. Results are then analysed for problematic areas.

Revised Model

iii. Qualitative - Revision of Model Final model

achieved through Based on simulation results, the simulations. architect then revises his model to make it more adapted to the climate.

Characteristics of Singapore Climate i. Singapore Climatic Data ii. Contrasting Climatic Optimization Variables iii. General Strategy for Tropical Humid Climate

02 | Characteristics of Singapore Climate

Introduction This chapter centres around Singaporeâ&#x20AC;&#x2122;s climatic characteristics. The following sections would explore: i. Different Aspects of Singaporeâ&#x20AC;&#x2122;s Climatic Data ii. Identify Potential Contrasting Climatic Optimization Variables iii. Identify a General Strategy for the Tropical Humid Climate By performing the above explorations, the aim is to narrow down the scope to a few essential quantitative optimization inputs that will be touched on further in Chapter 5: Inputs of Optimization.

02 | Characteristics of Singapore Climate

Singapore Climatic Data Singapore is located 1.3 degree North of the Equator and is within the tropical humid climate range. This belt is characterised by intense sun shine and abundant rainfall. Due to Earthâ&#x20AC;&#x2122;s rotation, the tropical humid climate belt stays exposed to the sun constantly and thus Singapore has only one summer season.8

Tropical Climate Tropical Humid Climate

Singapore

Rainfall: 2329mm/yr Sun hr: 4-5 (wet months) Sun hr: 8-9 (dry months)

Fig 6. Tropical Humid Climate Region

Fig 7. Sunshine Hours (weather.gov.sg)9

Sunshine Hours Sun hours is defined as the total hours whereby a surface receives direct sunlight of at least 120 Watts/m2. The number of hours is most affected by cloud cover as the duration that the sun is up is constant throughout the year. Thus sun hours are significantly less during the wet months as compared to dry months.10

02 | Characteristics of Singapore Climate

Rainfall Jun 21 Mar/Sep 21

90º 75º 60º 45º

Dec 21

30º 15º

S Fig 8. Sunpath of Singapore

Sunpath

From Fig.9b, one can identify the rainy season to be from November to January, due to their higher amount of rainfall (mm). The same cannot be inferred from Fig.9a which shows roughly the same number of raindays for each month. A rainday is defined as a day whereby total rainfall is 0.2mm or more.15 Comparing both sets of graphs, the non-rainy season could be characterised by many short showers as compared to heavy downpours during November to January.

Singapore’s sun rise and sun set timings are relatively steady at 0700 and 1900 respectively. This means that there are a total of 12 hours each day whereby the country experiences day. Fig. 8 shows the sunpath diagram for Singapore. Despite a minor shift during the Summer and Winter Solstice during June 21st and December 21st respectively, Singapore largely experiences overhead sun. There is also a very strong East West axis in terms of overheard sun.

By looking at Fig.7 and Fig.9, one can tell that Singapore experiences a fair amount of direct sun per day. During the dry months, almost 75% of the day is exposed to direct sun while the figure halves to 40% during the wet season. Although the sun is a good source of natural lighting, it is also a huge source of heat. As such, it is important to manage the amount incident on a building.

Fig 9a. No of Raindays per Month (weather.gov.sg)12

Fig 9b. Amount of Rainfall per Month (weather.gov.sg)13

02 | Characteristics of Singapore Climate

Fig 10. Hourly Temperature (weather.gov.sg)14

Fig 11. Relative Humidity (weather.gov.sg)15

Hourly Variation of Temperature Temperature is more easily relatable as it stimulates ones senses directly. Higher values of temperature directly relates to a hotter space. However, temperatures are transient and hard to capture through simulation. Surface temperature is affected by a variety of conditions. Wind speed, material thermal properties and solar access are just a few factors that might affect temperature.16 The main takeaway from the temperature data is the small temperature gradient between the day and night. The peak in temperatures during noon could be partially due to a lack of shade as the sun is directly above. This means that sunlight is direct upon all surfaces which are parallel to the ground. Relative Humidity Fig 11. shows a general decrease in relative humidity at noon with a global minimum present during the dry season in February. By comparing both Fig.10 and Fig.11, spots of lower relative humidity coincide with those of higher hourly temperatures. This suggests that afternoons are hot and drier whereas mornings are cooler but also wetter.

02 | Characteristics of Singapore Climate

Fig 12. Hourly wind speed and direction (weather.gov.sg)17

Fig 13. Wind Rose (sg)

Fig 14. Monthly wind speed (weather.gov.sg)18

Wind Speed Fig.13 shows the wind rose diagram for Singapore. It shows the two main predominant wind directions as North-NorthEast as well as the South-South-East. These two directions are most used by the industry when performing CFD simulations. Fig.12 shows the hourly wind speed and direction in Singapore. The random nature of wind pattern shows how unpredictable wind is. However, when cross referenced with Fig.13, which demonstrates hourly relative humidity, the higher wind speeds during noon seem to suggest that it has helped with decreasing humidity levels.

02 | Characteristics of SIngapore Climate

Contrasting Variables

Climatic

Optimization

From the previous section, it is obvious that the three main characteristics of Singaporeâ&#x20AC;&#x2122;s climate are: i. Solar Impact ii. Rainfall iii. North-South Predominant Wind The climate is a mixture between many factors. It is impossible to study only one in isolation. The following section shows some of the contrasting wants between variables that might create interesting optimization results.

Daylight vs Solar Irradiation Wind Flow vs Wind Driven Rain A common strategy to cool down a space is to encourage wind flow through it. However, when uncontrolled, it could also result in high volumes of rain being directed into a living space. This becomes apparent when we compare Fig.9a and Fig.9b. During the rainy season in January, monthly wind speed is at a high. This means that chances of high wind speeds and high rainfall to occur at the same time is high. Thus some care should be exercised when designing for wind ventilation.

Both qualities depend on direct or indirect sun rays. While maximising daylighting within a space can help reduce electric lighting loads, it could also lead to increased levels of solar irradiation. This increase would firstly lead to higher dependency on cooling loads and also indirectly lead to a warmer space. It is thus important to balance both aspects so as to tap on the the passive advantage of long sunshine hours.

02 | Characteristics of Singapore Climate

Fig 15. Psychometric Chart

General Strategy for Tropical Humid Climate Fig.15shows the climate of Singapore mapped onto a psychometric chart. As seen, the number of hours which are within the red comfort zone is minimal. This means that in general the climate is considered to be uncomfortable for prolonged stay.

02 | Characteristics of SIngapore Climate

reduce thermal heat gain

General Strategy for Tropical Humid Climate

increase ventilation

Fig 17. General Strategy

In order to help create more comfortable spaces, the two main strategies derived from the photometric chart are: i. decrease dry bulb temperature ii. decrease humidity These two strategies directly stretch the comfort range upwards along the humidity scale and also sideways along the dry bulb temperature scale, thereby encompassing more hours within Singaporeâ&#x20AC;&#x2122;s climate. Linking back to various climatic data as detailed in previous sections, passive strategies could include:

Conclusion The analysed passive strategies would be explored further with case studies in Chapter 3: Sustainability in Residential Design. Identifying the sun and wind as major passive strategies has also helped in angling the discussion in Chapter 5: Inputs of Optimization.

i. reduction of thermal heat gain through building orientation away from East-West sunpath ii. encouraging ventilation which would help cool the space by ensuring a steady exchange of air. Having consistent wind flow also helps to remove excess humidity from the air.

Sustainability in Residential Design i. General Strategies for Climate Adaptation ii. Sustainable Strategies for Residential in Singapore - Stirling Road Slab Block: Solar Performance - Treelodge at Punggol: Staggered Block Arrangement - Moulmein Rise: Cross Ventilation iii. Trends in Residential Design iv. Evaluation Metrics

03 | Sustainability in Residential Design

Introduction Singapore has one of the highest population density in the world. As such, high rise modular housing represents an economical and efficient way to relieve the stress on housing. This chapter aims to study: i. How residential in Singapore has adapted to the local climate ii. How residential in Singapore has adapted to the wants of citizens Although the two studies are listed as separate points, they are often interlinked. For example, residents avoid purchasing units that face the East and West due to basic knowledge on solar impact within their living spaces. Other issues might include trade-offs like having wide angles of unobstructed views versus spaces which suffer from high solar radiation due to the resultant large windows.

Study Methodology Based off the general strategy of reducing thermal heat gain and maximising ventilation, as explored in the previous chapter, three case studies were selected that will show how residential design has adapted to climate as well as residential wants. The case studies include: i. Stirling Road Arrangement of rooms which result in different solar performance, utilising buffer spaces to create comfortable spaces ii. Treelodge at Punggol Block arrangement to encourage wind flow across blocks and communal spaces iii. Moulmein Rise Design details, room arrangement and building orientation to encourage cross ventilation Besides studies on the climatic performance and analysis to test for successful adaptation, the chapter ends with an overall study on residential layout that will help inform decisions in later chapters.

03 | Sustainability in Residential Design

Brief History of High Rise Residential Typology in Singapore Since 1960, the Housing and Development Board (HDB) has been reponsible for providing enough affordable yet quality homes for Singaporeans. The rate of construction,

affordability aimed at young couples as well as an overall high quality has led to HDB flats being home to over 80% of Singaporeâ&#x20AC;&#x2122;s resident population.21

Taking over from Singapore Improvement Trust (SIT), HDBâ&#x20AC;&#x2122;s first challenge was to provide enough flats to house the then population of 1.6 million.22 This led to the design of the first slab blocks which are still standing along Tanglin Halt. Primarily designed for efficiency, these blocks are modular, quick to construct and were a huge upgrade in terms of sanitary solutions.

Fig 18. Tanglin Halt Estate (teolida.com)18

Moving forward, HDB kept reinventing themselves by adapting international style blocks to the tropical humid climate of Singapore. Besides understanding the impact of building orientation, more studies were made using computation fluid dynamics (CFD) to encourage natural ventilation of residential spaces.

Treelodge at Punggol represented a huge step forward as they were the first residential precinct to be awarded the Green Mark Platinum Award. Designed to allow for wind flow, treelodge aimed to channel wind across blocks as well as into the communal spaces. Besides government efforts in embracing sustainable housing, some private sectors have also taken up the challenge. Moulmein Rise was inspired by the Malay vernacular houses which aim to maintain a steady rate of wind flow even during the monsoon season. Translated into monsoon windows, Moulmein Rise is well known for its passive cooling qualities.

03 | Sustainability in Residential Design

Location Stirling Road was the first area to be introduced to high rise residential and the surroundings have been constantly changing. As compared to the nearby Dawson estates, this area is much older looking and dominated by a demographic of residents in the late sixties. Communal Spaces

Fig 19. Massing Orientation as a Strategy

Stirling Road Housing These were the first residential blocks designed by HDB. Built up to seven levels, each with three vertical circulation cores, units are modularly stacked above each other. The slab block is completed by its long horizontal corridors which defines the facade.

Some small neighbourhood shops were integrated into the ground floor providing grooming services and mini marts. Furniture is also placed wherever possible to provide some interaction spaces for residents. However, in general, communal activity is most observed within the block corridors.

When it was first built, stirling road was devoid of surrounding buildings. However, in modern day Singapore, trees and other blocks have grown to shade some of the lower levels. Massing Strategy The long slab block is oriented to have its long sides facing the North South axis. This minimizes solar impact within units as the East and West direct sun are avoided. The main facade is looking out to Stirling Road which has more opportunities for views.

Fig 20. Context around block (coloured red)

03 | Sustainability in Residential Design

Fig 21. Commercial area on ground floor

Fig 22. Stirling Road Facade

Fig 23. Corridor Photos

Fig 24. Stirling Road Floorplan

Typical Floorplan | Stirling Road HDB 0

10m

03 | Sustainability in Residential Design

public

private

LR K BR

LR T

exterior

corridor 1

parapet - sunlight - wind - rain

3 bedroom/ living room

bedroom/ kitchen

- tinted window - wooden fire door - neighbours to peer in

- tinted window - remove humidity - remove odours - rain - sunlight - wind

Fig 25. Study diagram for Stirling Road

exterior

unit

03 | Sustainability in Residential Design

Stirling Road Housing Fig 25 shows a portion of the block plan. From the diagram, one can easily make out different layers of space. Besides that, the diagram also shows the various types of boundaries that separate each layer.

type a type b T

8am

LR K BR

LR T

Climate Adaptation

10am

LR K BR K T T K BR

LR T K BR K

LR T T

3pm

LR K BR

However, an interesting observation is that the corridor has become an extension of oneâ&#x20AC;&#x2122;s living space. It is common to see clothes drying and potted plants along these corridors. Furthermore, the semi-enclosed nature provides a relatively comfortable informal space as compared to the outdoors.

In terms of room arrangement, people would generally prefer type B as it offers more privacy. This is evident in later studies on unit arrangements.

1pm

Adapting to Local Demand

Moving forward, the design of buffer space can be explored further as it helps to shield apartment spaces from the harsh outdoor elements.

12pm

Besides acting as a sunlight filter, the corridor also helps in filtering wind driven rain. This means that during a storm, residents in A can still open up their bedroom windows while those in B would be inviting rainwater indoors if they do likewise.

In terms of solar performance, A fares much better as the corridor naturally shades the unit. This means that diffused lighting is allowed to filter through and direct solar impact is at a minimal. On the other hand, B which is directly connected to the surroundings receives a fair amount of direct sunlight leading to glare and thermal comfort issues. (refer to fig. 26)

The slab block mainly consists of two unit types as seen from the diagram. Type A has its bedroom facing the corridor while type B has its one facing the exterior surroundings.

T T

5pm

LR K BR

LR T

Illuminance

3000 lux

Overlit

Fig 26. Study on illuminance levels at various times of June 21st

03 | Sustainability in Residential Design

Fig 27. Block Orientation to create longer wind shadow

Treelodge @ Punggol

Location Sited near the punggol waterfront, the area is currently empty to the North. As such, the estate still experiences uninterrupted wind flow and views in that direction. As a town, punggol was conceived slightly differently to other traditional mature estates. Instead of a main town centre, punggol is made up of many smaller neighbourhood centres. Treelodge is located within one such central node with Punggol MRT station, Punggol Interchange and Waterway Point all reachable by a 10 minute walk.

This public housing project won Singaporeâ&#x20AC;&#x2122;s first Green Mark Platinum Award. Comprising of 7 residential blocks, a podium carpark and pockets of green spaces, the project can be seen as a landmark in terms of sustainable public housing in Singapore.

Massing Strategy In general, the residential blocks were strategically placed in a staggered manner such that no residential block is within the wind shadow of its neighbour. This arrangement also created a wind tunnel through the central communal area. Each residential block was also oriented 45 degrees from the North as this creates a larger wind shadow, generating a large low pressure zone. Since wind flows from regions of high to low pressure, the orientation helps in interior ventilation.23

Fig 28. Context around Block

Communal Spaces

+ podium carpark exists a Above the green space which connects to all the blocks. This zone is populated with facilities like children play areas, + community gardens and also fitness facilities.

Designed+ to be the central activity hub for residents, the residential blocks were oriented to encourage wind flow into this central spine. +

Fig 29. Communal Space on Ground level24

03 | Sustainability in Residential Design

Fig 30. CFD Results25

Fig 31. Aerial View of Podium Block26

Fig 32. Playground 27

BR T BR

T BR

Fig 33. Typical Floorplan Treelodge at Punggol

BR T BR

BR T

K BR

T BR

Typical Floorplan | Treelodge at Punggol 0

10m

03 | Sustainability in Residential Design

BR T BR

open

mix of 3 room and 4 room units - balconies along facade act as break out spaces which are naturally ventilated due to block placement

T BR

enclosed

open

enclosed

open

enclosed

open

lift lobby

3 room units

wind tunnel

- common area is open and allow for wind flow across the block

- kitchen and living room arranged to create internal wind tunnel that encourages ventilation

- at block level, adjacent units were shifted apart to create a wind tunnel - allow more wind flow across site

Fig 34. Study diagram of Treelodge block plan

K BR

03 | Sustainability in Residential Design

Treelodge@Punggol Fig.34 is a diagram of a typical Treelodge block plan. In order to tap on prevailing wind, blocks are designed to be porous, as seen in the many open layers across the plan.

Climate Adaptation Buffer spaces like balconies are placed along the facades to help provide shade as well as provide breakout spaces that are naturally ventilated. Besides having wind tunnels across the block, the unit itself also have their common areas arranged in a spine. Placed closed to the block wind tunnel, it is oriented to the direction of wind flow. The porous block design encourages wind flow into the communal shared spaces to help remove heat from the building, thereby decreasing cooling load.

Adapting to Local Demand As compared to Stirling Road HDB, Treelodge has its bedrooms placed furthest away from the corridor. Corridor space in front of a unit is also designed to be a destination rather than a transient spot. For Stirling Road, the corridor was a space of high activity as neighbours have to regularly cross paths to get to their unit. In this case, only the lift lobby retains such a transient nature.

Fig 35. Wind tunnels across block

There is also a shift away from slab blocks towards more slender massings. This is because slender blocks have more exposed surface area for each apartment as compared to slab blocks. This increase leads to more openings for bedrooms but also lead to other issues like increased solar irradiation.

03 | Sustainability in Residential Design

Location

+ + -

Fig 36. Wind flow strategy

Sited along Moulmein Road, the area is dominated by private housing. Moulmein Rise is the tallest building within its immediate region with low rise bungalows to its South. Nearby amenities include a shopping mall, a primary school and also Novena MRT station. Moulmein Rise is situated just outside Tan Tock Seng Hospital, which forms the landscape to the North of the building. The design architect probably choose to design the buildingâ&#x20AC;&#x2122;s main face to be in the South due to the wide angles of view.

Moulmein Rise The private property contains a total of 50 apartments with 48 typical apartments and 2 penthouse apartments. Common amenities include a 50m lap-swimming pool, a tropical garden, a gym and underground parking. Each floor houses 2 apartments arranged side by side along the East-West axis and are thus open on three sides. Massing Strategy Building geometry was oriented north-south, exposing majority of facade to the predominant wind direction. This also means that the East and West facing facades are minimised to reduce direct solar impact. The facade is also lined with a few types of modules, namely: overhangs of 0.6m and 1.0m, mesh like panels and full height windows.

Fig 37. Context around block

Communal Spaces Unlike public housing, communal areas are + +of private + + developers. In this not a priority case, common facilities are tucked into the - represent ground level and the only chance for encounters within the estate.

03 | Sustainability in Residential Design

Fig 38. Moulmein Rise Facade28

Fig 39. Monsoon Window29

Fig 40. Swimming Pool30

K BR

BR T

Fig 41. Typical Floorplan of Moulmein Rise

Typical Floorplan | Moulmein Rise 0

10m

03 | Sustainability in Residential Design

higher humidity

lower humidity

exterior 1 window/ balcony/ monsoon window - windows across entire south facade - sunlight - wind - rain

north half

south half 2

living room/ kitchen

bedroom/ balcony

- common areas are placed along north south axis - open nature - encourage wind flow by having open spine

- significantly less windows on north facade - remove humidity - remove odours - rain - sunlight - wind

Fig 42. Study diagram of Moulmein Rise

exterior

03 | Sustainability in Residential Design

Moulmein Rise Fig 42 is a diagram of a typical East Side unit. The unit can be split into two main halves with the south being more open and the north having more interior partitions. Due to the nature of

the block plan, entry into the apartment is via a private lift. As such, arrangement of rooms can be based purely on encouraging wind flow.

Climate Adaptation Buffer spaces seen in Stirling Road has been incorporated here in the form of smaller balconies. These define Moulmein Riseâ&#x20AC;&#x2122;s facade and also act as protection from outdoor elements. Number of openings in the South is also significantly more than those in the North. This tries to create a low pressure zone on the North and wind entering from the South can flow towards the low pressure zone, thereby achieving natural ventilation. Another observation is that rooms with higher humidity like the kitchen and toilets are placed towards the North, near the outlet of the designed wind flow. This helps in removing excess moisture from the apartment, thus creating an even more comfortable interior space. Design details like monsoon windows allow wind to pass through even during rain. These windows were incorporated into the apartment as a parapet, allowing one to lean over it when peering out though the living room windows.

high pressure

wind direction

Adapting to Local Demand Arrangement of rooms in Moulmein Rise shows a fine balance between the demands of residents and also some form of climate adaptation. The bedrooms are all placed on the periphery, packing their many partitions away from the main wind spine. As a result, the living room and kitchen spine acts as a open wind tunnel while the bedrooms still enjoy uninterrupted views and daylighting.

low pressure

Fig 43. Wind flow from high to low pressure zones

In terms of connection to communal areas, Moulmein Rise seems to be targeted at the higher income families. Private lift landings mean that interaction between neighbours are limited to ground floor amenities. These highly private apartments are due to the fact that single loaded blocks are more effective when designing for ventilation. The resultant long and thin building only allow for no more than two units per level.

03 | Sustainability in Residential Design

Typical Floorplan | Stirling Road HDB

Fig 44. Stirling Road part plan

10m

Trends in Residential Design Besides the case studies from the previous section, studies were also conducted based on overall room arrangement for HDB units. The aim of the study was to identify trends in room arrangements and also placement of windows which will affect daylight levels within an apartment. Change in Nature of Corridor The corridor still remains as a circulatory route for residents. However, the transient nature is shifted into a destination spot. Long corridors in the past do not just stop at oneâ&#x20AC;&#x2122;s apartment and thus it acts as a shared space. However, the transient nature is lost as corridors now end right at the apartment entrance Change in General shape of Basic Unit BR

T BR

K T

BR T BR

Fig 46. Treelodge part plan

Fig 45. Sunlight penetration

LR From LR the modular and BR BR long shaped apartments with only its short sides exposed to the outdoor elements, the basic unit has rotated 90 degrees to have the Tlonger sides T not only helps facing North South. This BR in K lighting up aK narrow floorplan but also increases chances of ventilation.

T BR

Typical Floorplan | Treelodge at Punggol 0

10m

03 | Sustainability in Residential Design

5 Room

4 Room

3 Room

1960

1970

1980

1990

2000 window

main door

unit corridor

03 | Sustainability in Residential Design

1960s

3 room flat

1970s

3 room flat

1980s

1990s

3 room flat

2000s

4 room flat

3 room flat

Fig 48. Shift in apartment layout

Increased Level of Privacy As seen in Fig.48, the kitchen is slowly shifted nearer to the apartment corridor with bedrooms moving in the opposite direction. This means that the apartment can be imagined in a public to private gradient with bedrooms on the periphery. Increased Connection Surroundings

Exterior

The shift in unit shape can be identified in Fig.45 as well and this means that the longer side now forms majority of a blocks facade. Compared to a 1960s flat, modern day apartment arrangements now have higher number of windows and thus increased exposure to outdoor elements.

03 | Sustainability in Residential Design

Evaluation Metrics The case studies show how different strategies have impacted living spaces. One important aspect is to develop an evaluation system for each metric. Point-in-Time vs Cummulative For example, measurement of point-in-time versus cummulative values to evaluate amount of daylight or solar irradiation. Examples of point-in-time metrics include illuminance levels as well as CFD analysis. In order to get a more wholesome understanding of building performance, annual cummulative values would be more effective.

Another example would be the scale at which each metric is measured. Moulmein rise has a unit level strategy of interior ventilation while Treelodge aims to improve ventilation into urban areas through building porosity.

Selection of Metric to Reflect Scale At a smaller scale, measurements like solar irradiation would probably be less intuitive for the success of a space. As such, other metrics which relate more to human perception like temperature could be more relevant. On the other hand, metrics like useful daylight illuminance works regardless of scale. In conclusion, selection of metrics to best evaluate a space is an important aspect of this thesis and will be explored further in later chapters.

Multi-Objective Climate Optimization i. Limitations of a Linear Optimization Strategy - Interview with Industry on Linear Optimization ii. Introduction to Multi-Objective Optimization - Current Challenges of Multi-Objective Optimization iii. Theory of Pareto Optimal -Extraction of Approximated Pareto Front iv. Application to Design Process

04 | Multi-Objective Climate Optimiztion

Introduction This chapter explores how multiobjective optimization would be more helpful in providing architects with better informed trade-offs. Aimed specifically to improve the quantitative aspect of data design, multiobjective optimization can theoretically produce a series of massings which are all along the pareto front. The following sections would touch on: i. Limitations of Linear Optimization ii. Introduction to Multi-Objective Optimization iii. Theory of Pareto Optimal iv. Application to Design Process The aim of this chapter is to show that a multi-objective approach is more effective in simulating for climatic conditions as compared to a linear optimization strategy. This would be explored by identifying various limitations in a linear approach to achieve climate optimization and also the larger design options which results from having multiple objectives. The idea of an approximated Pareto Front will be discussed and utilised as a data extraction tool in the design stage.

04 | Multi-Objective Climate Optimization

Limitations of a Linear Optimization Strategy In order to gain more insight about climate optimization in the industry, an email interview was conducted with DP Sustainable Design (DPSD). (Appendix A) Their job scope consists of inhouse sustainable design consultation, energy simulation for climate optimization and they are recognised by the Building and Construction Authority (BCA) as one of the architecture firms with the highest number of Green Mark Platinum and GoldPlus Projects.

single objective result

An internship which lasted from August 2016 to December 2016 was also spent at DPSD where there were chances to have an in depth look at some of their climate optimization workflow.

pareto front

Based on the interview and the Fig 50. limitation of linear process internship experience, the workflow can be represented by the simplified chart as For example, if building A is seen in Chapter 1: Importance of Climate simulated for both sun and daylight, two Optimization, Fig.5. sets of results would be achieved. Correcting problematic solar irradiation areas might Describe briefly the current method of climate worsen daylighting values while correcting daylighting issues might increase solar optimization in the company. radiation. However, design solutions are proposed with little informed trade-offs as â&#x20AC;&#x153;The current method is for the there is no direct link between both sets of architecture to be designed, before climatic results.

analysis is provided to propose optimized Furthermore, architecture design solutions for the building.â&#x20AC;? is a complex problem which involves many The above was extracted from the aforementioned email interview. Current industry methods of climate optimization are mostly linear and different simulations are run parallel to each other. Proposal of solutions are then based off individual simulation results, with perceived trade-offs.

objectives to balance. On the quantitative aspect, designs might also lie below optimised options as climatic variables are difficult to visualise. Thus in short, the limitations of linear climate optimization can be broadly classified into:

tregenzai. sky Lack of consideration trade-offs generation

towards informed

ii. Linearly obtained design might still be below the pareto front

tregenza sky generation + simulation

simulation

Designed building form 04 | Multi-Objective Climate Optimiztion is prepared for energy simulation.

Solar Study Massing is tested for solar performance. Surfaces are studied for relative solar performance.

Qualitative

Building Model Designed building form is prepared for energy simulation.

Multi Objective Optimization

Pareto Front Analysis

Multiple variables result in a pareto front which provides a range of models.

Qualitative analysis of massing models.

Revised Model

Qualitative

Quantitative

Introduction to Wind FlowOptimization Illuminance Multi-Objective Study Study AreasCompared to Single Objective of massing Daylight performance is Optimization, quantitative analysis tested withto determine if which experience Multi-Objective Optimization runs the same high wind speeds are lighting levels are smoothen simulations at out. the same time. This sufficient. means Remedies are that trade-offs are considered with applied each if otherwise. generation. Referring to the case of building A in the previous page, multi-objective optimization considers both daylighting Revised and solarModel radiation issues simultaneously to provide a series of options which has the least amountFinal of model total sacrifice on solar irradiation achieved through and daylight. independent simulations.

Qualitative

Quantitative

Qualitative

Building Model

Fig 51. Revised workflow

Final model achieved through simulations.

The above describes an updated work flow albeit with increased accuracy on the quantitative front. Massing models which will be qualitatively assessed are all optimised to the least amount of sacrifice on all variables.

04 | Multi-Objective Climate Optimization

Inputs and Outputs of Multi-Objective Optimization Multi-Objective Optimization involves several objectives which can be conflicting or competing within themselves. In an architecture design problem, there often exists more than two objectives that need to be optimised. These could be defined by the different stakeholders within the project group.

Contrasting Architects have Quantitative always been responsible for this design balance, be it qualitative or quantitative aspects of design. Below are some of the probable conflicts of interest in terms of architecture in Singapore.31

Variables

Solar Radiation

Daylighting

Natural Ventilation

Wind Driven Rain

Uninterrupted Views

Indoor Thermal Comfort

GFA

Building Height

Qualitative

Quantitative

Main Faรงade Orientation

Indoor Thermal Comfort

Form Sculpting

Solar Radiation

Social Spaces

Energy Demands

Fig 52. Contrasting Variables

If the evaluation is in the form of identifying how well an architect does this balancing act, then more often than not, percieved optimised results fall below the pareto front. The main advantage of MultiObjective Optimization is that it can help with making informed trade-offs such that each massing is always optimised to the best of its quantitative aspects.

04 | Multi-Objective Climate Optimiztion

Theory of Pareto Optimal Pareto Optimality has been around for a while and only with recent advances in technology has it been more widely utilised in the field of design. Professor Francis Ysidro Edgeworth was one of the first few to define an optimum for multicriteria economic decision making. This was further explored by Vilfredo Pareto who came up with the Pareto Optimum Theory which states that:

â&#x20AC;&#x153;The optimum allocation of the resources of a society is not attained as long as it is possible to make at least one individual better off in his own estimation while keeping others as well off as before in their own estimation.â&#x20AC;? Concept of Dominance

In short, when data with values representing two different objectives (x,y) are collected till infinity, there exists a data Extraction of Approximated Pareto Front frontier that represents a series of points which cannot relocate resources to make x or In practice, time is limited and thus data sample size would be limited. The Pareto y better without comprimising y or x. Front would have to be approximated based on data on hand. Making use of the properties of a multicriterion space, data can either be dominated or non-dominated. Dominance in this case refers to the overall performance of each solution. Given how x,y and z of solution b and c are all larger than that of a, b and c are said to dominate a. However, b and c are said to be a non-dominated set as x value of c is higher than that of b, although its other values are lower. Thus, the idea is to compare all points within a solution space and extract all the non-dominated data to form an approximated Pareto Front. This extraction method will be applied to multicriterion solution spaces in the context of this thesis.

04 | Multi-Objective Climate Optimization

Besides the exterior form, this thesis also explores how individual rooms can be optimised and as such produce theoretically different unit types across all levels.

Application to Design Process Multi-Objective Climate Optimization obviously improves the quantitative aspect of design. Given the advances in computation architecture and computer speeds, this process provides a bridge between data interpretation and architecture form finding.

With the advances in parametric design, Multi-Objective Climate Optimization provides a logic for form finding that can help with creating more complex spaces which were previously deemed impossible by hand.

This thesis attempts to marry both the design ideas of an architect with the calculation strengths of a computer to produce a new climatically optimized typology. One which retains high quality spaces that have high climatic performance.

Climate Data

Parametric Model

Inform

Input

Residential Design Studies

Site Studies

Inform

Application of Multi-Objective Climate Optimization on Residential in Singapore Architect Input

Output

Computer Input

Massing Pareto Front

Output Inform Selection

Selected Massing Input Inform

Parametric Floorplan Multi-Objective Climate Optimization on Unit Scale

Output

Solution for High Density Living

Climatically Optimized High Density Residential Typology

Thesis Design Framework

Parametric Inputs of Geometric Explorations Optimization i. Input Selection Criteria ii. Performance of the Building Massing - Solar Radiation - Wind Flow iii. Performance of Individual Units - Daylighting - Solar Impact

05 | Inputs of Optimization

Introduction This chapter explores the various inputs for multi-objective optimization. The discussion would center around two key aspects: i. The reasons behind selection of inputs ii. Explorations to reduce simulation computing time while retaining high levels of accuracy Recap on Tropical Humid Climate As mentioned in chapter 2, Characteristics of Singaporeâ&#x20AC;&#x2122;s Climate, Singapore has a tropical humid climate. This can be identified by her high average rainfall, high sunshine hours as well as a small temperature range. Expectations of Residential in Singapore The main climatic strategy is thus to reduce thermal mass while encouraging ventilation across the living spaces. Both strategies attempt to bring indoor temperatures to a more comfortable range.33 This is most evident within SouthEast Asian vernacular with their deep overhangs and light weight porous envelope.

Singapore is one of the densest cities in the world with 7,987.52 people per square kilometer.34 As such, residential buildings are predominantly high rise. Based on the studies conducted on HDB floor plans, there is a demand for more natural lighting within oneâ&#x20AC;&#x2122;s home. Unobstructed views and privacy are also key to oneâ&#x20AC;&#x2122;s perception of a good apartment. This can be seen from the evolution of slab block arrangements with bedrooms looking out into the corridor to the modern day layout whereby each unit has their bedrooms and living room placed as far as possible from the corridor. Other design details include taller windows and implementation of monsoon windows that increase porosity with the outdoor environment.

05 | Inputs of Optimization

List of Inputs The optimization inputs can be grouped into two main parts:

i. Performance of the Building Massing a. Solar Irradiation b. Wind Flow ii. Performance of Individual Units a. Daylighting b. Solar Impact This starts to create trade-offs between the wants of an individual and the overall performance of the whole block. Below is a brief introduction to the resultant tradeoffs between inputs which will be explored further in the later parts of this chapter.

Performance of Building Massing

Pareto Front

Performance of Individual Unit

Performance of Building Mass Based on the strategy of reducing solar heat gain and maximising ventilation, the optimization inputs would include solar irradiation and wind flow across the building. Thus for an optimal building performance, it has to have minimal solar irradiation while retaining sufficient porosity to allow wind flow across it. Both inputs generates trade-offs as minimising solar radiation would naturally lead to a decrease in exterior surface area while maximising wind flow could lead to a porous geometry, thereby increasing exterior surface area. Performance of Individual Units Daylighting is an important aspect within units and a naturally lit apartment means that less energy is needed for lighting purposes. Solar impact refers to the flip side of having too much exposure to sun. This can come in the form of glare or an overheated space which leads to it being uncomfortable. In terms of trade-offs within individual units, unobstructed views and daylighting attempt to increase surface area exposed to the exterior while solar impact works against both.

Solar Pareto Irradiation Front

On this level of simulation, unit refers to the overall average performance.

Wind Flow

ASE

Pareto Front

UDI

Fig 53. Input tradeoffs

05 | Inputs of Optimization

Solar Irradiation Solar irradiation is a climate based metric which measures the amount of radiant flux on an area.35 Higher amounts of solar irradiation means that there is higher solar impact upon a surface.

Given that high rise buildings consists mostly of concrete, which has a high thermal mass, high solar irradiation values would indirectly result in a higher indoor temperature. This is because indoor temperature of an operating building is most likely cooler than that of outdoors due to mechanical cooling systems. As a result, heat which naturally transfers from high temperature zones to low temperature zones, would flow indoors. This phenomenon increases mechanical cooling load, thereby increasing energy demands. Within Singaporeâ&#x20AC;&#x2122;s context, the optimized solution would thus be to minimize amount of total annual solar radiation. The average annual solar irradiance of an unshaded surface is about 1,600 kWh/m2/ year.36 Compared to other temperate climates like Berlin which achieves values of 950 kWh/ m2/year.

Fig 54. Concrete stores heat and releases it when surroundings are cooler

treg gen

tregenza sky generation

05 | Inputs of Optimization

Comparing DIVA and Ladybug for Solar Irradiation Simulation Both simulation softwares work with similar concepts: i. Generate sensor points on test surfaces ii. Identify context which might impact solar studies iii. Tregenza sky is generated from which backward ray-tracing is performed iv. Solar irradiation values are stored within each sensor point DIVA and Ladybug both have Rhino/Grasshopper platforms which gives strong support for data design.37 Results are easily extracted as sensor point position, readings and the grid face areas are made available for further parametric manipulation. Resulting visuals are also intuitive and easy to understand.

Comparing DIVA and Ladybug for Complex Geometry When tested for more complex and curved surfaces, Ladybug is not as consistent in terms of solar irradiation results. Areas of the geometry would start to register 0 kWh/m2 readings, rendering the simulation software inconsistent for optimization tests. This also happens for surfaces which are oriented away from the 3 main axis of Rhino.

On the other hand, DIVA radiation simulation is more consistent and more adept at simulating complex forms.

Fig 55a. Diva vs Ladybug on simple box geometry

Fig 55b. Diva vs Ladybug on curved geometry

05 | Inputs of Optimization

tregenza sky generation + simulation

tregenza sky generation

simulation

Ladybug

Comparing DIVA and Ladybug for Simulation Set-up In general, DIVA is more user friendly with only a few key components DIVA required to perform a successful solar irradiation simulation.

DIVA

Fig 56. Diva vs Ladybug on simulation set-up

Ladybug is more complex but provide more control over the generation of the sky geometry. The main difference between both set ups is at step iii (Tregenza sky generation) as detailed under the previous portion,

Comparing DIVA and Ladybug for Solar Irradiation Simulation. For DIVA, the sky generation is combined with the simulation component and always runs when simulation is started. For Ladybug, sky generation is separated and the same sky is reused for further simulations. Ladybug solar irradiation simulations can thus run much faster since it skips the generation of the Tregenza sky for future simulations. This is helpful during multi-objective optimization as computing time can be reduced significantly.

Comparing DIVA Processor Usage

and

Ladybug

for

Both DIVA and Ladybug allow for multiple logical processors to be used in parallel thereby increasing simulation speeds based on the hardware. However, Ladybug takes up a significant amount of memory space and over a period of time this could slow down other inputs which are most likely running during multi-objective optimization.

Conclusion for Comparison In conclusion, although Ladybug might be faster in terms of generating solar simulations, their results are highly inconsistent as compared to those of DIVA.

In order to achieve optimized results which would be relevant, DIVA would thus be the simulation tool of choice.

05 | Inputs of Optimization

Method of Scoring for Solar Irradiation As mentioned, solar irradiation values are stored within sensor points. In order to get a sense of the total value, the sensor point values are multiplied with their respective test areas. Subsequent manipulation of solar irradiation data can then have a huge impact on the resultant optimised form. If all the data is summed up to achieve a cummulative value, exposed surface area starts to play a big part within this optimization problem. Results are always slimmer at the top as this helps in reducing exposed area to the sun. Thus a massing which is in a contextless site will always be of the smallest surface area.

Fig 57. Solar Radiation

05 | Inputs of Optimization

Wind Flow Ventilation is another key strategy in providing comfortable spaces within the tropical humid climate. High air exchange helps in removing moisture from the air, improving the evaporation rate of pespiration and also helps in cooling down a space in general. Current methods to simulate wind conditions mainly circle around Computation Fluid Dynamics (CFD) software. These simulation are largely point in time with a predetermined predominant wind direction (mainly from the NNE and SSE directions) based on Singaporeâ&#x20AC;&#x2122;s wind rose. The resultant graphic shows turbulent flow around the edges of buildings, showing wind shadows and places with high wind pressure. Theoretically, CFD studies in both predominant directions can get a good sense of wind flow across oneâ&#x20AC;&#x2122;s site or around a designed building. The aim of performing wind flow analysis is to ensure that there are no high pressure areas that might result in spaces with uncomfortably high wind speeds and also no dead zones which will result in stale indoor spaces.

Fig 59. Singapore Wind Rose

Generating a Wind Score System As mentioned above, some of the drawbacks of CFD is the long computing hours and this means that wind studies are preferably left out of optimization problems. Fig 58. Example of CFD result graphic

Characteristics of CFD in General Most of the CFD software in the market works with Openfoam in the background. Setting up a wind simulation model largely involves the same steps as well: i. Generating surfaces that would be affected by wind flow ii. Setting up virtual wind tunnel iii. Setting up direction whereby wind enters the wind tunnel Results are similar to fig.58 which show a point-in-time graphic of wind flow around the target area. However, the main drawback from applying CFD to optimization problems is the long hours required for running the simulations. Each iteration takes significantly longer and also requires large amounts of computing power. Furthermore these iterations are also point-in-time, ignoring the unpredictable nature of wind. For example, wind direction changes randomly during the Inter-Monsoon Period.38

The wind score system tries to reduce simulation time significantly by studying relative performance of surfaces. All 8760 hours of wind data is also incorporated into the scoring, to create a more climate based wind tool. Now, besides the two predominant wind directions, optimization can be based upon other directions, as seen in the wind rose diagram.

05 | Inputs of Optimization

Concept Behind Wind Score The wind score works on the concept of providing clear routes for wind of comfortable speeds, while obstructing wind of high uncomfortable speeds.39 Wind speeds of 3m/s and below are considered as comfortable as they are light breezes. This provides opportunities for breezy breakout spaces within the residential design. On the other hand, wind speeds above 3m/s result in lighter objects like leaves to be in constant motion. This could cause a problem for residents as the leaf litter from nearby spaces could be swept indoors. Fig 60. Beaufort Wind Scale

Calculation of Wind Score Calculation of wind score depends on the target surface normal. The angle between surface normal and wind direction is calculated and given a normalized score. Extracting the wind direction and velocity from a Singapore weather file, the data is split into two parts:

i. Wind Speed < 3m/s Since wind speed is comfortable, the normalized score is biased towards surface normals which are perpendicular to wind direction. ii. Wind Speed > 3m/s On the other hand, normalized score is biased towards surface normals which are parallel to wind direction. Fig 62. Normalised Score

All the scores are then added up to give a total cummulative wind score for each surface.

Fig 61. Wind Score

05 | Inputs of Optimization

Analysis of Wind Score The first few simulation tests were performed on a flat field, assuming that wind direction and velocity is unaffected by surrounding context blocks.

Relative results could be obtained within seconds and these start to show which surfaces would be prioritised based on climatic wind data. Below are the results for a simple box block which shows that the surfaces parallel to the North-South predominant wind direction score higher as compared to those that are facing the axis. The more porous geometry on the right also starts to have a higher score and this relative study suggests that increased porosity is preferred to a simple box block.

Fig 63. Wind score test run

05 | Inputs of Optimization

Comparison to CFD Results Below shows a comparison between CFD results and Wind Score results for a site along Toa Payoh Lorong 4. Both were performed on the same 3D model. Analysis period for Wind Score was tweaked to only include wind speeds from June to September. This is because CFD results were based on Southwestern Monsoon Season whereby the predominant wind is from the South. Wind score reflects how wind friendly each surface is. Thus, the more red a surface is, the more desirable the wind is near that surface. When comparing both sets of results from the same time period, the wind score appears to be giving a good prediction of surface performance. For example, the dark blue faces are all reflected as dead zones in the CFD results. On the other hand, orange surfaces manage to get 3m/s of wind.

Conclusion Initial tests of the wind score are promising and looks to be in line with simulation results. Given that the wind score only takes a few seconds to do a comparitive study, this metric would be very helpful when running optimization.

Fig 64. Wind score vs CFD result

05 | Inputs of Optimization

Daylighting Daylighting refers to the concept of allowing natural light into a space such that electric lighting dependence is minimised during the day. In terms of analysing how successful a space is lit, there exists several types of metrics. The following sections discuss various climate based daylighting scoring systems which were considered for optimization inputs.

Fig 65. Daylight Factor42

Daylight Factor (DF) Daylight factor is the percentage of indoor work plane illuminance compared to the outdoor illuminance as measured on a horizontal plane. The key to this system is that the percentage is based off cloudy sky conditions which means that there is no direct solar beam.40 This is a quick relative study to compare how deep daylight can penetrate into a space. However, the percentage does not show if there are glare issues and underlit areas. There are also no predetermined lower and higher treshold which a user can set to make it a focused study. Finally, Singapore receives high levels of direct sunlight, especially during the non Monsoon seasons where cloud cover is low.41Therefore Daylight Factor can only be applicable for a short amount of year hours and this means that it should not be an input for this optimization.

Fig 66. Daylight Autonomy43

Daylight Autonomy (DA) Daylight Autonomy is the percentage of time in a year that a point in the work plane achieves a higher illuminance level than the set lower treshold. This means that credit is only given when illuminance levels is above a pre-determined lower treshold. Compared to DF, DA starts to take into account the local climatic conditions like sun angle, making it a more well rounded analysis metric. The lower treshold avoids giving credit to underlit spaces which is helpful when analysing the impact of daylighting in a space. However, a separate system still has to be implemented to check for glare as DA still credits areas whereby illuminance levels are overlit.

05 | Inputs of Optimization

Comparing DIVA and Honeybee for Simulation Set-up Both simulation software have Rhino and Grasshopper platforms which is useful for data design. However, Honeybee does not offer as much flexibility in terms of defining range of useful daylight. This is despite the higher number of components required to create a set up. Fig 67. Useful Daylight Illuminance44

Useful Daylight Illuminance (UDI) UDI is an improvement to DA. Firstly, there are lower and higher tresholds which help to separate the data into three bins. The first being percentage time within 0-300 lux, defined as the underlit bin. The second being percentage time within 300-3000 lux, which is the useful daylight range. The last bin is the overlit bin which is percentage time above 3000 lux.

DIVA, on the other hand is more user friendly, with a more compact parametric set up. The most important factor is that DIVA allows user control over treshold levels and this is why DIVA would be chosen as the simulation engine for UDI optimization.

Conclusion for Daylight Scoring System UDI which considers both underlit and overlit situations would be chosen as the input for optimization. The resultant graphics are also easily relatable to a successfully daylight space. In order to obtain UDI, DIVA daylighting simulation would be performed with UDI set at 300-3000 lux.

In general UDI is the most well rounded metric system as the useful daylight range can be directly relatable to how successfully daylit a space can be. Overlit and underlit bins are also a bonus as one can then strategise accordingly to let in less direct light or direct diffuse lighting deeper into a space. The aforementioned lower treshold of 300 lux is selected as this level of illuminance is sufficient for comfortable reading.45 On the other hand, 3000 lux is selected as the upper treshold based on studies done by the Building and Construction Authority (BCA) for glare within buildings.

05 | Inputs of Optimization

Solar Impact Solar impact refers to amount of direct daylight which is let into a space. A high amount of direct sunlight can result in glare, making spaces uncomfortable for reading or Solar Radiation daily chores. Over time, the direct sunlight Solar radiation is the amount of also increases temperature of the spaces, radiant flux on an area.46 It consists of both making it even more unusable. diffused and direct sunlight and can be used to study relative solar impact across models. Control over amount of solar impact is thus However, the values obtained do not directly relate to temperature or glare which are the important to ensure that a daylit space can be well main objectives of this study.

used. The following section discuss metrics which can help to test for solar impact.

Furthermore, the absolute values of solar radiation are difficult to relate to. While illuminance levels are easily observed by an individual, solar radiation which is radiant flux, does not directly link to any sensation felt by the human body. Thus solar radiation should be primarily used to study performance of the building massing as a whole. Annual Sun Exposure (ASE) Annual Sun Exposure is a metric that represents how much of space experiences too much direct sunlight. The result could come in the form of glare or increased space temperature. ASE is measured in percentage of floor area with minimal of 1000 lux for at least 250 of occupied hours per year.47

Fig 69. Glare

Compared to solar radiation, ASE gives a more direct relationship between solar impact and oneâ&#x20AC;&#x2122;s perception of spatial comfort. Higher percentages could mean more glare and wamer spaces, making them undesirable. Therefore, ASE can be minimised while UDI is maximised to allow for informed tradeoffs within each individual unit.

05 | Inputs of Optimization

daylighting

massing volume

scoring

simulation

UDI

6 2 5

parametric model with variables

>300 Lux <3000 Lux

create sensor points

ray-tracing with daysim

maximize % hours useful daylight illuminance >300 lux and <3000 lux

scoring

ASE hours

minimize hours annual sunshine exposure

Fig 68. Useful Daylight Illuminance & Annual Sun Exposure

Method of Scoring The selected daylight metrics are to maximise UDI and minimise ASE. Total values for each floor will be cummulated before an overall average is achieved. DIVA daylight simulation churns out both metrics using the same algorithm. As such, this reduces the amount of simulation time needed. Together with manipulation of radiance parameters, simulation time can be reduced further while retaining accuracy during optimization process.

05 | Inputs of Optimization

Simulation Results

Cumulative Sum

ASE Output

Useful Daylight Illuminance Annual Sunshine Exposure

Values across nodes added up into total sum value

Output value represents total hours with too much exposed sun

High

Low

Averaged Value

UDI Output

Average value of all nodes is calculated to consider lower percentile of values

Output value represents average frequency floorplates are within 300 and 3000 lux

High Low

Solar Irradiation Output

Weighted Cumulative Sum

Simulation Results Solar Irradiation calculation

Output value represents weighted sum value of total incident solar irradiation

Values across nodes multiplied by area fraction and summed

total nodes

â&#x2C6;&#x2018;[

High

(solar irradiation)i

surface.areai total surface area

Low

Weighted Cumulative Sum

Simulation Results Wind Score calculated on wall surfaces

Wind Score Output

Output value represents weighted sum value of total wind performance

Values across surfaces multiplied by area fraction and summed

total walls

High Low

]

High

Low

â&#x2C6;&#x2018;[ High Low

(wind score)i

surface.areai total surface area

]

05 | Inputs of Optimization

Summary of Optimization Inputs The aim of this section is to produce a set of metrics which best measure performance of a building. Another aim was to minimise simulation time but yet retain a high level of accuracy. This list of input calculation details different considerations due to the nature of each simulation type. It is important to also notice that the massing changes with each passing iteration and the data communication with the program can be designed with these metrics to steer it in a certain direction or to include as many conditions as possible.

In general, cumulative sums were used extensively for metrics which do not take into account usage of spaces. UDI had its value averaged because the optimization process has to take into account the performance of each level. For example, a building which has its top level fully daylit but its bottom level totally dark will give the same cumulative sum as that of a building with half both of its bottom and top level daylit. With the average taken for each level first, the metric is more well-rounded. Weighted sums were introduced to reduce the impact of outliers and reduce data variance. This is because some massings have a large exposed surface area and the program might deem it as undesirable, bringing optimisation away from large surface areas. However, the design intent is to include as many types of geometries as possible and thus weighted sums were introduced. Even if spaces were to receive high levels of solar irradiation, they could be designed as night pavillions or even green decks.

Designing Constraints i. Design Exploration through Constraint Design ii. Design Space for Parametrically Generated Massing iii. Design Space for Parametrically Generated Unit

06 | Designing Constraints

Introduction This chapter explores how different constraints can help in framing oneâ&#x20AC;&#x2122;s solution space. It can be used to drive permutations between parameters or be used to specifically target a small area within a solution space. The following sections would touch on: i. Desigb Exploration through Constraint Design ii. Design Space for Parametrically Generated Massing iii. Design Space for Parametrically Generated Unit The aim is to provide a sense of how the multi-objective optimization problem is approached and subsequently how the parametric model underwent iterations to generate the desired solution space.

06 | Designing Constraints

Design Exploration through Constraint Design The design process for this thesis started off by thinking first about constraints. Early attempts at optimization revolved around solar irradiation minimization due to the relatively quicker computation process. This resulted in building shapes which had minimal surface area as surface area is a major component in solar irradiation calculation. As a result, form finding results were not spectacular and other objectives like daylighting were added to start increasing the design search space. Hierachy of Variable Impacts This thought process was influenced by “Design Innovation through Constraint Modeling” by Axel Kilian and also explored by Robert Vierlinger in “A Framework for Flexible Search and Optimization in Parametric Design”. Both authors look in depth into the relationship of each constraint and how a variable can indirectly affect search results. Kilian showed an adjacency diagram which related different appliances within a home and the relative impact areas of each. This formed an early study into how one can design mobile networks within an apartment.

For example, movement of a point can change a voronoi diagram drastically. Firstly, its most immediate impact is to change the aspect ratio of the cell. If a GFA limiter is in place, the next level of impact would be the total height of this voronoi cell. Through the combination of resultant variables, both created by a point, total surface area can be generated. The voronoi block results are something which cannot be visualized without the aid of computers and this serves to show that constraint design can help produce previously unconceivable permutations. Vierlinger also agrees by stating that “Human minds with computers to aid them are our principal productive resource. Understanding how that resource operates is the main road open to us for becoming a more productive society and a society able to deal with the many complex problems in the world today.”

This point is further reiterated by Tero Narahara in “Multiple-constraint Genetic Algorithm in Housing Design” when he stated that “ Complexity in design today can be addressed through computational means that can resolve a problem without the designer knowing in advance its formal solution. Instead Vierlinger Diagram a series of constraints alone can be enough to A similar approach is adopted by produce algorithmically formal solutions that Vierlinger which looked at how to minimise resolve complexities that surpass a designer’s structure and reduce maximum displacement capabilities.” of the supported room. Each author looked at the hierachy of impacts a variable can have.

Design Space Metrics

Geometrical System

Space Usability

Spatial Hierachy

Global Design Space

Potential to create large numbers of different geometries

Minimise areas whereby potential usage is limited

Various types of communal areas and potential site connections

All design possibilities

Iteration 1

Voronoi Cells

Space Usability Geometrical System Spatial Hierachy

Iteration 2

Modular Grid Blocks

Spatial Hierachy Space Usability

Geometrical System

Iteration 3

Snake Voronoi Cells

Spatial Hierachy Geometrical System

Iteration 4

Space Usability

Spatial Hierachy

Voronoi Extrusions

Geometrical System

Space Usability

Iteration 5

Shifted Voronoi Blocks

Space Usability

Geometrical System

Spatial Hierachy

06 | Designing Constraints

Design Space for Generated Massing

Parametrically

The parametrically generated massing refers to the building form which will be sited within a context. Further description for each iteration can be found in Chapter 7: Parametric Geometric Explorations. In terms of the exterior form, there were three main search metrics: 1) Geometric System 2) Space Usability 3) Spatial Hierachy Geometric System The size of this search space represents the amount of potential in generating large pools of different geometries. Thus basically, the larger this circle is, more types of geometry can be produced by the iteration. Space Usability Space Usability refers to the area in which potential programs can be located. Low usability could mean that there is not enough head space, the floorplate is slanted or awkward corners that do not function as a usable space. The larger the size of this search space, the larger the area usable for programmatic placement Spatial Hierachy This refers to the different types of spaces which could arise from the massing itself. Varied spaces could be different types of public spaces like high rise terraces, urban courtyards or caved like urban spaces sheltered by the massing Global Design Space This simply refers to all types of design possibilities.

Aim of Design Space Across iterations, the idea was to increase the solution search space as much as possible. This was because at the scale of a building, there is more geometrical flexibility. Moreover, given the potential to simultaneously test for multiple objectives, the geometry can afford to be more complex and purposeful. Iteration 5 was an amalgamation of all the strategies which start to show potential in terms of geometry, space usability as well as a myraid of communal spaces. The three search metrics were strictly kept to and thinking in that direction has helped in defining the unbounded global search space.

Design Space Metrics

Geometrical System

Apartment Convenience

Spatial Hierachy

Global Design Space

Potential to create large numbers of different geometries

Overall success of how apartment can function

Various types of common areas that contribute to living spaces

All design possibilities

Iteration 1

Central Living Room

Apartment Convenience

Geometrical System

T BR

Kitchen Bedroom Toilet Verandah Living Room

Spatial Hierachy

Iteration 2

Convex Living Room

V BR

K LR

Kitchen Bedroom Toilet Verandah Living Room

Apartment Convenience

Geometrical System

Spatial Hierachy

Iteration 3

Spine and Branch

BR LR V LR

T LR

Kitchen Bedroom Toilet Verandah Living Room

Spatial Hierachy

Apartment Convenience

Geometrical System

Iteration 4

Circular Joint

Apartment Convenience

LR BR

LR V

Kitchen Bedroom Toilet Verandah Living Room

Spatial Hierachy Geometrical System

Iteration 5

Orthogonal Joint

Apartment Convenience

V LR

Kitchen Bedroom Toilet Verandah Living Room

T T

Spatial Hierachy Geometrical System

06 | Designing Constraints

Design Space Generated Units

for

Parametrically

The parametrically generated unit refers to the unit layout which will be sited within the massing. Further description for each iteration can be found in Chapter 10: Parametric Floorplan Explorations. In terms of the exterior form, there were three main search metrics: 1) Geometric System 2) Apartment Convenience 3) Spatial Hierachy Geometric System The size of this search space represents the amount of potential in generating large pools of different geometries. Thus basically, the larger this circle is, more types of geometry can be produced by the iteration. Apartment Convenience An apartment is defined as convenient when the space functions as an apartment. This refers to bedrooms being situated at the end nodes of adjacency diagrams, having toilets situated close to bedrooms, allocation of sufficient toilets etc. The larger the circle, the more controlled the adjacency diagram. Spatial Hierachy This refers to the different types of spaces which are present in a unit. Different types of spaces can include a verandah which connects directly to the block circulation, a verandah which is connected to the facade, functioning more as a look out deck, smaller living rooms which acts as a mini holding space for bedrooms etc. Global Design Space This simply refers to all types of design possibilities.

Aim of Design Space Initially a similar approach to the massing was applied to the units. However, through further explorations, it was evident that spatial heirachy should instead take precedence. No matter how interesting the geometry was, the apartment would not work if the rooms do not match up. At this smaller scale, the constraints start to be designed to target a more specific area within the global design space. Thus in short, it can be said that for the massing, there is a want to increase solution space but for the unit, it is about targeting a small but relevant solution space.

Parametric Geometric Explorations i. Geometry as a Climatic Strategy ii. Iteration 1: Voronoi Cells iii. Iteration 2: Modular Grid Blocks iv. Iteration 3: Snake Voronoi Cells v. Iteration 4: Voronoi Extrusions vi. Iteration 5: Shifted Voronoi Modules

07 | Parametric Geometric Explorations

Introduction This chapter details how each parametric iteration was conceived. Based off a library of climatic strategies, the aim is to incorporate as many conditions as possible with the search space. Referring back to Chapter 6: Designing Constraints, including more conditions is equivalent to a larger geometrical system solution search space. The following sections will cover: i. Geometry as a Climatic Strategy ii. Iteration 1 | Voronoi Cells iii. Iteration 2 | Modular Grid Blocks iv. Iteration 3 | Snake Voronoi Cells v. Iteration 4 | Voronoi Extrusions vi. Iteration 5 | Shifted Voronoi Modules Parameters and constraints for each iteration will be looked at and further analysis on the geometrical performance will be provided. Selected massings attempt to show the potential of each design iteration. At the end of this chapter, the parametric model which has the largest search space will be placed within a site and tested for its response to a context.

07 | Parametric Geometric Explorations

Location within Design Framework The design of a parametric massing model through constraints is the first input into multi-objective climate optimization. By designing the model to include qualitative conditions, it would help one to select from the generated quantitative front. The general strategy here is to expand the search space as the scale of a massing still allows for a large pool of geometry to remain relevant as a residential design.

Constraint Design

Inform

Climate Data

Parametric Model

Inform

Input

Residential Design Studies

Site Studies

Inform

Application of Multi-Objective Climate Optimization on Residential in Singapore Architect Input

Output

Computer Input

Massing Pareto Front

Output Inform Selection

Selected Massing Input Inform

Parametric Floorplan Multi-Objective Climate Optimization on Unit Scale

Output

Solution for High Density Living

Climatically Optimized High Density Residential Typology

Thesis Design Framework

07 | Parametric Geometric Explorations

Geometry as a Climatic Strategy The aim of the parametric massing model is to explore how different geometries can function as climatic strategies and space framing agents. The area of interest lies in the urban scale and ties in with those optimization goals as explored in Chapter 5: Inputs of Optimization. The urban scale here refers to communal areas like rooftop decks, ground level common areas and terraces which form as a result of the massing geometry. Qualitative Analysis

Application of Climatic Strategies

With the inception of these communal spaces, qualitative analysis can now include decisions on how successful the said spaces can work for different sites.

The list on the following page covers a range of possible climate strategies formed through geometry. Conditions include scale, whether the massing is a tower or a slab and this affects the amount of solar exposure or wind flow.

Combined with quantitative data achieved from multi-objective climate simulation, the architect can now best situate a building design within any boundary.

Other space framing techniques like courtyards or checkerboard placement create an enclosed or breathable communal zone respectively. This is in addition to how the geometries perform in terms of wind ventilation. Terracing techniques include shifting blocks or staggering them and this again affects how light penetrates deeper regions of the floorplate. The iterations try to include testing conditions for as many strategies as possible so as to generate massings that apply the best permutation on site.

07 | Parametric Geometric Explorations

Iteration 1 | Voronoi Cells Iteration 1 was done to test out how the parametric model would respond to climate optimization. It can be described as a simple population of cells across site with chamfered walls framing urban courtyards of different scales. The generation process is as follows:

1) Points generated across site 2) Voronoi 3d algorithm is applied 3) Boolean action is performed to remove every other cell

Parameters:

Constraints:

Position of each Point Boolean Removal

Site Boundary Volume

Space Usability 03 Iteration 1 Generation Process

Geometrical System Spatial Hierachy

Spatial Hierachy Geometrical System

Space Usability

07 | Parametric Geometric Explorations

Iteration 1a

Analysis of Geometry

Iteration 1b,c,d (top to bottom)

Due to the lack of a GFA condition, massings tend to become flat in order to minimise solar irradiation. However, the chamfered surfaces start to frame urban courtyards of different scales and this porosity leads to a higher wind score. However, the chamfered surfaces are steep and the parametric model is still very uncontrolled. Generation of geometry has also not given much thought about structure as there are many floating floorplates.

Iteration 1e,f,g,h (top to bottom)

07 | Parametric Geometric Explorations

Iteration 2 | Modular Grid Blocks Iteration 2 mainly investigated if the optimization inputs would generate a mixture between slab and tower typologies. The generation process is as follows: 1) A square grid is mapped across the site 2) The maximum height of each cell is set 3) Heights are generated for each cell

Space Usability

Geometrical System

Parameters: Height of Each Cell

Spatial Hierachy

Constraints:

Site Boundary Volume Size of Grid

Spatial Hierachy 03 Iteration 2 Generation Process

Geometrical System

Space Usability

Spatial Hierachy Geometrical System

Space Usability

07 | Parametric Geometric Explorations

Iteration 2a,b

Analysis of Geometry

Iteration 2c,d

Iteration 2 is definitely more spatially usable as compared to iteration 1. The optimization process also churned out towers of varying heights which also started to create coutyards in the vertical axis. This condition also creates spatial hierachy which was less obvious in iteration 1. Instead of constraining the GFA, there was a condition to maximise total average daylighting levels. Since daylighting takes into account the size of a floorplate, the contrasting wants of minimising total surface area and solar irradiation is offset.

Iteration 2e,f,g

07 | Parametric Geometric Explorations

Space Usability Geometrical Iteration 3 | Snake Voronoi Cells System Iteration 3 was an initial attempt at designing the pareto Spatial front results. Previous iterations left the designing to the computer Hierachy and results were less controlled. This iteration had a snake ike form logic to it so that desired types of urban courtyards can be created

The generation process is as follows:

1) Controlled generation of points cloud 2) Points selected within cloud 3) Intersected voronoi are generated in shape of snake Spatial Hierachy Geometrical System

Space

Usability Parameters:

Point Location Snake polyline

Constraints: Site Boundary Volume Size of Grid

Spatial Hierachy

Geometrical System

Iteration 3 Generation Process

Space Usability

Spatial Hierachy

Geometrical System

Space Usability

07 | Parametric Geometric Explorations

Analysis of Geometry When sections were cut across the massings, common spaces of various scales start to become apparent. The continuous nature of the snake polyline also accounted for structural concerns as cells are now generated with a logic of being stacked or next to one another. Instead of simple geometry being tested in iteration 1 and 2, the geometry now has its own logic and heirachy of edges. There is an added layer of self shading generated by the jutting edges of the generated parametric model.

Iteration 3a,b,c

section

residential

small scale common areas

residential

big scale common areas

residential

Spatial Hierachy

07 | Parametric Geometric Explorations

Spatial Hierachy Iteration 4 | Voronoi Extrusions Space Usabilityaspects Geometrical Iteration 4 tries to combine of iterationSystem 2 with those of iteration 3. The

walls now retain some edged profile which perform well in terms of wind into the urban spaces. Courtyards of various sizes are also generated with various types of self shading properties The generation process is as follows:

1) Controlled generation of voronoi grid 2) Grid surfaces selected 3) Extruded by block height

Spatial Hierachy

Geometrical System

Space

Parameters:

Usability Constraints:

Point Location Surface Selection Cell Height

Site Boundary Volume Size of Grid GFA

Spatial Hierachy

03 Iteration 4 Generation Process

Geometrical System

Space Usability

Geometrical System

Spatial Hierachy

07 | Parametric Geometric Explorations

Iteration 4a

Analysis of Geometry GFA is now used to control total usable space in each massing. This iteration retained some the vertical communal spaces of iteration 2 while possessing some of the surface geometry finish of iteration 3.The slanted edges start to suggest some form of compression and expansion for urban courtyards

Iteration 4f,g,h

System 07 | Parametric Geometric Explorations

Spatial Hierachy Geometrical Space Iteration 5 | Shifted SystemVoronoi Modules Usability Iteration 5 is a further development of iteration 4, adding overhangs to create even more chances for self shading and potentially terraces connected to the modules on higher levels.

The generation process is as follows: 1) Controlled generation of voronoi grid 2) Generated grid on more levels Spatial 3) Extruded by block height Hierachy 01

Geometrical System

Parameters:

Space Usability

Point Location Surface Selection Max Height per Module

Space Usability 03 Iteration 5 Generation Process

Geometrical System

Spatial Hierachy

Constraints: Site Boundary Volume Size of Grid GFA

07 | Parametric Geometric Explorations

Iteration 5a

Analysis of Geometry

Iteration 5b,c,d

This iteration shows more varied massings and thus represent a larger solution search space. There is also a high level of spatial hierachy as seen in the overhangs and vertical courtyards. Many of the climatic strategies are also embodied in this series. Control over the module height has also provided a logic for parameterizing floorplans.

Iteration 5e,f,g

Mapped to Floorplan Mapped to Facade Mapped to Floorplan Mapped to Facade

Based on data pa makes design de

07 | Parametric Geometric Explorations

Iteration 3

Snake Voronoi Cells Added control over form Spatial Hierachy due to geometry

Iteration 1

Voronoi Cells Quick population across site Test geometry as climate strategy Space framing with blocks

Multi-Objective Climate

Optimization Tested with various permutations of inputs to study results and provide feedback

Iteration 2

Modular Grid Blocks Geometry response to inputs Tower mix with short slab Varied Heights

Iteration 5

Shifted Voronoi Modules Communal areas of various sizes Self shade properties due to geometry Tower mix with short slab Input for Optimization Optimization Feedback Design Influence

Application to Site Massing geometry model generates interesting forms on its own and can be applied to site for further testing

Iteration 4

Voronoi Extrusions Space framing with blocks Tower mix with short slab Varied Heights

07 | Parametric Geometric Explorations

Application to Site Now that the model is applied to a context, minor adjustments like module size and levels per module would have to be made. The different sites will be covered in Chapter 8: Site Selection. It will also be interesting to see how the parametric model responds to both the site boundary and the context. All these provide additional feedback which will help to further improve the overall optimisation proposal.

Moving Forward After repeated tests, the optimization process and the parametric geometries are studied and improved. Starting with 2 objectives, the inputs increased up to 4 in order to study how the model responds to the optimization process and also the amount of time needed to collect a usable data size. The workflow is also improved to include GFA filtering before simulation, significantly cutting down on time needed. Besides that, file size is also managed by a few code modules, as this helps with reducing memory usage. The parametric model that was selected was iteration 5: Shifted Voronoi Modules. This is because it provides a starting platform for design of units as well as smaller interior circulation systems. The resultant geometry also acheives a large design space, ensuring a well-rounded optimization process.

Site Selection i. Population Distribution ii. Site Analysis: Hougang Central iii. Site Analysis: Punggol Waterway iv. Site Analysis: Queenstown Mei Chin

08 | Site Selection

Introduction This chapter details how 3 different sites from 3 different towns were selected based on: i. Population Distribution ii. Population Density iii. Existing Site Conditions The aim of this chapter is to identify key site drivers that can help understand how the pareto front responds to site boundaries, surrounding context and also unique town characteristics. All 3 sites would have distinctly different qualities which can affect how one selects design options off the various pareto fronts. The following content will first identify key characteristics before showing selected town photos and finally looking more in depth into the type of conditions found on site.

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08 | Site Selection

SEMBA -WANG

LIM CHU KANG

TUAS

WOOD -LANDS

YISHUN

TOA PAYOH

PUNGGOL

YEW TEE

CHOA CHU BUKIT KANG PANJANG

ANG MO KIO

BISHAN

YIO CHU KANG

SENG KANG

CHINESE GARDEN

TENGAH

BUKIT BATOK

BUKIT TIMAH

CENTRAL CATCH -MENT

NOVENA

SERAN -GOON

HOUGANG KOVAN

PASIR RIS

LOYANG

JURONG WEST

JURONG EAST

DOVER

ONE NORTH

QUEENS -TOWN

ORCHARD

NEWTON

ROCHOR

GEYLANG

PAYA LEBAR

TAM -PINES

SIMEI

PIONEER

BOON LAY

PANDAN GARDENS

CLEMENTI

TELOK BLANGAH

TANGLIN

RIVER VALLEY

KALLANG

MARINE PARADE

SIGLAP

BEDOK

BUKIT MERAH

OUTRAM

DOWN -TOWN

JURONG ISLAND

PULAU TEKONG

CHANGI

SENTOSA

AGE DISTRIBUTION

SG AVERAGE

Population Distribution across Singapore

POPULATION DENSITY (RESIDENTS/SQKM) 0-10000

YEARS

10000-20000 30000-40000 40000-50000

Population Distribution Singapore is a country which has been developed by towns. As such, each town has its own distinct characteristics. For example Bishan is known for its pitched roofs as well as connection to the Bishan AMK park. These form part of the town identity and any development should respond in some part to this distinct flavour. Selection Methodology The map above shows the relative position of each town, together with population density and distribution. On a whole, some general observations include: 1) Mature estates as defined by HDB have a population distribution highly similar to that of the national bell curve 2) Non-Mature estates are largely characterised by two peaks, one representing the generation who owns the apartment, and the other representing their offsprings

In order to obtain more constraints to test the parametric model, 3 different sites are selected based on the following criteria: Town: Population Densities Population Age Distribution Additional Site Characteristic Hougang: Low Density Non-Mature Estate Located next to town center Punggol: Medium Density Non-Mature Estate (new town) Located next to Punggol Waterway Queenstown: High Density Mature Estate Located with tall flats towards North-West

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08 | Site Selection

Population Living in Public Housing (%) 16 14 12 10 8 6 4 2

Singapore

0 0-4

5-9

10 - 14

15 - 19

20 - 24

25 - 29

30 - 34

35 - 39

Hougang Central GPR 3.0

Site Area 14,600 m�

Pop Density 11,000 inh/km�

40 - 44

45 - 49

55 - 59

60 - 64

65 - 69

70 - 74

Punggol Waterway GPR 3.0

Site Area 33,500 m�

Pop Density 14,000 inh/km�

Summary of Three Sites

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50 - 54

75 - 79

80 - 84

85<

Age Group

Queenstown Mei Chin GPR 4.2

Site Area 13,800 m�

Pop Density 22,000 inh/km�

08 | Site Selection

Population Distribution The graph shown on the previous page displays a stark contrast between all three sites. This data can be extrapolated into occupancy schedules plugged into simulation tools to get a better reflection of the town. This can be based upon population projected 5 years into the future as occupants will also age with the building. Simulation tools usually use a general occupancy schedule. However, Singapore has two distinct school holiday seasons, June and December. During this period, occupants usually sleep in later and this in turn has an impact on how the simulation will run. Singapore Calendar 2017 January

February

March Su

May

June

August

September

21 28

We 1

9 16

16 23 30

19 26

21 28

December

November

October We

July

7 21

April Sa

8 15

New Yearâ&#x20AC;&#x2122;s Day 2017 Sunday*, 1 January

Good Friday 2017 Friday, 14 April

Vesak Day 2017 Wednesday, 10 May

National Day 2017 Wednesday, 9 August

Deepavali 2017 Wednesday, 18 October

CNY observed Tuesday, 31 Jan

Chinese New Year 2017 Saturday, 28 January Sunday*, 29 January

Labour Day 2017 Monday, 1 May

Hari Raya Puasa 2017 Sunday*, 25 June

Hari Raya Haji 2017 Friday, 1 September

Christmas Day 2017 Monday, 25 December

Youth Day 2017 Sunday*, 2 July

Calender of Holidays

Day after National Day 2017 Thursday, 10 August

Children's Day 2017 Friday, 6 October

Teachers' Day 2017 Thursday, 31 August

http://SmartTuition.sg

Building Density Due to different levels of population density, the average building heights differ amongst all three sites. As expected, hougang which has the smallest population density also has buildings which are more widely spaced apart. In other words, the building density of each site reflects greatly the population density. Queenstown which has a small footprint and highest building density is expected to be affected by its context the most. On the other hand, Hougang is sited on a largely open field, meaning that the solution space would be free from context impact.

Site Boundary All three sites have distinctly different site geometries as well as Gross Plot Ratios. This directly impacts the site constraint and solution search volume during optimization. Site Characteristic Hougang site is located right next to a transport hub and the town center. As such, an important consideration would be connectivity and dealing with the mixing between public and private spaces. Punggol has its South side directly connected to the waterway, a spine which runs through Punggol Town. Lastly, Queenstown is one of the first towns to be planned by HDB. As such, historical residential blocks like the first point block, slab block and the iconic butterfly block are all situated nearby.

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08 | Site Selection

Punggol CC Hougang MRT Hougang Mall

Hougang Interchange

$ Hougang MRT

Midtown Residences Retail

Hougang Central

Commercial Spine

Connectivity Node Hougang Central Site

104

Communal Space

08 | Site Selection

Hougang Site Characteristics Based on its location within town, the site is central to a few residential precincts and also directly connected to an interchange and MRT station. Future developments plan for Hougang MRT to become an integrated with the downtown line as well. Besides that, the site is within 5 minutes of Hougang Mall, a 5 level shopping mall designed to serve neighbourhoods with its service oriented shops like barbers, supermarkets and bookshops. Further East, there are more shop houses which house decade old convenience stalls and spectacle shops. Communal spaces can be identified by large shelters and found peppered throughout the estate. These are usually populated by the older generation and estate agents.

Potential Design Strategies With the site being in a central location, there is a strong need to manage public and private spaces. Qualitative analysis of the pareto front could also take into account the impact which the massing might have upon the shorter commercial blocks. Some form of direct path to the many bus stops could also be provided so as to not disrupt many of the habits of residents in the area.

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08 | Site Selection

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08 | Site Selection

Punggol Waterway

Block 322 Bus Stop

Waterway Cascadia Playground

Punggol Cove Primary Nibong LRT

Sheng Siong Supermarket

Sumang LRT Waterway Terraces Coffeeshop

Punggol Waterway

Connectivity Punggol Waterway Site

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Lookout Points

08 | Site Selection

Punggol Site Characteristics Punggol Waterway is a main green spine running across the entire town. The site has its South side open to this park and different HDB precincts along the waterway deal with this condition differently. Waterway terraces opens up smaller courtyards to form urban living rooms with the green spine, whereas Waterway Cascadia steps their towers down to meet the same green spine. Although the site is dominated by residential blocks, most of the ground level is opened up as communal areas or as commercial spaces. It is also strategically Potential Design Strategies placed between two LRT stations which in turn connects to Punggol Interchange. The 4.5m tall LRT tracks could be a nuisance for residents and the massing could be turned away from it. The site is also the widest amongst all three and this could provide opportunities to tap on the different view points across the site. The site naturally contours down to meet the waterway. Thus the South side of the massing should deal with the condition, both from the point of view of a potential resident and from the point of view of a park user.

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08 | Site Selection

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First Point Block

Butterfly Block

Block 166 Bus Stop

Queens Condominium

Tiong Ghee Temple

Automobile Showroom

$ Neighbourhood Center

Historic Buildings

Tall Towers Queenstown Mei Chin Site

116

Short Bungalows

08 | Site Selection

Queenstown Site Characteristics Queenstown is a town which has witnessed the early years of Singapore. Starting with the first slab blocks along stirling road to the first point block, the town is filled with iconic residential blocks. In recent years, the 50 story Dawson Terraces and Dawson Skyville have also been constructed near the selected site. Most of the commercial activity takes place in the South-West area with communal spaces available in the framed spaces of neighbouring residential blocks. The fact that the site is framed by tall towers in the North and West might affect the pareto front and reduce the size of the design space. However it could represent a natural form finding method to stepping down a massing towards the short bungalows in the East.

Potential Design Strategies With the high number of historic buildings, the massing could potentially have gestures which respond to them. This site also has buildings of different heights on all sides. Thus there could be interesting play in terms of height treatment. Design choices could also deal with how the footprint breaks up from slabs to individual blocks from West to East of the site. Besides the climatic constraints, this site provides a myraid of qualitative higher order preferences for selecting form the pareto front.

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121

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Visualization of Data i. Understanding Multi-Objective Climate Optimization Data ii. Representing Multi-Objective Climate Optimization Data iii. Data Display Framework iv. Hougang Central Optimization Results v. Punggol Waterway Optimization Results vi. Queenstown Mei Chin Optimization Results

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09 | Visualization of Data

Constraint Design

Inform

Climate Data

Parametric Model

Inform

Input

Residential Design Studies

Site Studies

Inform

Application of Multi-Objective Climate Optimization on Residential in Singapore Architect Input

Output

Computer Input

Massing Pareto Front

Output Inform Selection

Selected Massing Input Inform

Parametric Floorplan Multi-Objective Climate Optimization on Unit Scale

Output

Solution for High Density Living

Climatically Optimized High Density Residential Typology

Introduction This chapter first looks at various methods of communicating climate optimization data as a design tool. This includes studies into various display domains and how each domain can contribute to the overall understanding of all derived information. The first section then ends with the design of a data display framework. Following which, the optimization results of each 3 sites as detailed in Chapter 8: Site Selection will be displayed with the aforementioned framework. Lastly, resultant design spaces derived from the pareto front will also be looked into, exploring how it might be useful as feedback for further iterations or as rough strategies for design decisions. The results of this chapter ties in with the highlighted portion of the overall design flow.

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Understanding Multi-Objective Climate Optimization Data Data Type Data type refers to what a set of numbers represent. It should be specific in terms of its description and some broad categories include: 1a: Length of time for measurement Point-in-time versus Climate Based calculations show vastly different data sets. Point-in-time is more concerned about a specific period of time and this include single day illuminance studies as well as CFD studies of a predominant wind period. Climate Based calculations refer to annual simulation data sets, including annual considerations like UDI. Climate Based gives a more accurate depiction of the overall performance of a space.

1b: Unit of Measurement In simple terms, daylight and solar irradiation both measure the amount of sunlight incident on an object. However, both use different units of measurement as they are interested in different aspects of sunlight. Although general patterns are similar, the absolute value and what they represent vary. Success of a daylit space is measured as within 300-3000 lux, and this same absolute value does not mean that the space is undesirable in terms of thermal comfort. It is thus important to understand what the unit type of each data represents.

1c: Unit Increase/Scale of Data When comparing across two metrics, it is important to know what the absolute value represents in their respective data sets. For example, if A (1000lux) and B (1500lux) are compared for illuminance levels. It is impossible to determine how much better B is compared to A. Within a data set, the range could be 0-100000lux which makes A and B almost equal in performance. However, if the range is 0-2000lux, B is better by 25%. Thus in order to properly compare between different metrics, it is important to consider the unit increase for both sets of data.

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Massing

Building to simulate

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Grids of 1m wide generated across floorplates

Understanding Multi-Objective Climate Optimization Data Area for intervention

2a: Communicated to Architect

Node Display

Mapped onto floorplan to understand performance of space

This part of data communication would affect qualitative design decisions. Based on the type of simulation, communication can be of a few types.

High Low

Surface Display

Summed node values to compare performance of different levels

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Level for intervention

Average Building Performance

Zonal Display

Averaged surface values to compare performance of different zones

Data Communication

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The data can be shown as nodes placed within a space and having a colour scale applied across. Examples include UDI whereby the method of communication tells an architect which zone is receiving enough daylight. Another example could be surface based communication whereby the performance of each surface is important for design decisions. This can come in the form of a sum value for each surface. Using the same example for UDI, having a sum value will represent a cummulative performance. This could be useful for comparing across rooms. The last example would be a zonal representation. This can come in the form of an averaged value for the entire apartment thus leading to more macro design decisions as compared to scrutinising each individual point node. Each example has its own merits and it is important to know what type of design decisions would result when designing a data display framework.

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Simulation Useful Daylight Illuminance Solar Irradiation Annual Sunshine Exposure Wind Score

Output Data

Node Data Node Data Node Data Surface Data

Data Processing

Computer

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Mapped to Floorplan Mapped to Facade Mapped to Floorplan Mapped to Facade

Based on processed data makes optimization design decisions

Based on data patterns makes design decisions

2b: Communicated to Computer Data communication to the computer is largely in the quantitative sense. Whilst an architect can appreciate UDI coloured across a room, a computer struggles to capture the pattern accurately. It has to read metrics as percentage of room lit up or whether the room has attained a minimum average value. In this case, surface or zonal display can help in communicating data to computers. It is thus important to understand what the computer would take in and output, managing this quantitative workflow in order to produce relevant results. Data communication within a system is most evident in this thesis when performing optimization. The resultant fitness value must be able to communicate design changes for the parametric model. Keeping this flow light is also helpful given the limitations in computing power.

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Representing Multi-Objective Optimization Data

Climate

Data Display Dimension Data display refers to how data can be communicated effectively and efficiently to architects. In the case of this process, the display should be able to show how each design

i. performs in comparison to others ii. and also across all 4 selected metrics as covered in Optimization:

Chapter

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3a: One Dimension Display This refers to comparison of performance only across one dimension. Besides conveying relative performance of a design in each metric, its is quite limited in its effectiveness in communicating comparison.

Useful Daylight Illuminance

One Dimension Display

3b: Two Dimension Display Otherwise known as a parallel coordinates system, this display is useful for displaying multiple metrics. By rearranging these axis, one can even explore how each metric relates to others. However, it is not intuitive enough for extraction of results because it does not show a designâ&#x20AC;&#x2122;s position within the data space.

Two Dimension Display

3c: Three Dimension Display Using 3 axis with an additional colour axis, 4 metrics can be shown at the same time. By plotting a design onto the data point space, it is easy and quick to understand relative performance in comparison to all 4 metrics. This display method is the most effective in communicating clustering of results.

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Data Display Design Each data display dimension has itâ&#x20AC;&#x2122;s own strength and weaknesses and by combining all three, the understanding and subsequent extraction of results will then be successful. The above image shows how collected results would be communicated to the designer. The display framework was achieved using Rhino/Grasshopper and thus can be seamlessly combined with existing optimization tools. Using the 3 dimensional properties of Rhino, exploration through the data domain is now possible. Also, only pareto front geometry will be passed into this framework which keeps the file light and minimizes lag time when filtering through results.

Massing The massing would be the design which was selected from the pareto front. Combined with existing properties of Rhino, one can then zoom into focus areas within the model. This allows a designer to start exploring potential spaces side by side a data display. Parallel-Coordinates Domain With additional information like average height, site coverage and no of blocks, the parallel coordinates domain starts to show how a particular massing performs both geometrically and climatically as compared to others. Data Domain This display is most useful for extracting clustered typologies. Based on the position within the 4 axis plot, one can cycle through the points, investigating how the massing differs across points which are nearby each other. Post Analysis Once typologies are extracted, focused studies on how the massing sits on site will be supplemented by additional inputs from the parallel coordinates domain.

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Hougang Central Optimization Results The next few pages show how optimization results can be communicated using those techniques explored in the earlier sections. This includes data display using the framework and post analysis of optimization results.

Generated Parametric Models Models which fulfil GFA Resultant Pareto Optimal Extracted Typologies

: n=984 : n=348 : n=25 : n=5

Recap on Site Characteristics Hougang Central is the least dense of all 3 sites and is connected directly to the MRT on the West side.

Pareto Front Results

In terms of context, the site is largely unaffected by neighbouring buildings. As such, the site is open to climatic conditions on all sides. More details can be found in Chapter 8: Site Selection.

Due to the open nature of the site, achieved results have a wide range of designs. Some are flushed to the North or South while some are built up more dense across the middle of the site. This means that the optimised design space is close to that of the constrained design space and almost all generated massings can be considered for simulation.

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Hougang Central Pareto Front The above graphic combines one dimensional data display with a rendering of the respective generated massing. This helps to allow designers to make links between geometry and their performance. However, as mentioned previously, it is helpful for comparison purposes but difficult as an extraction process. In general, the Hougang Pareto front is the most diverse due to it being least affected by surrounding context.

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Skate Bank Typology

Having a taller side exposes more surface area to the sun. As such, solar irradiation and ASE is high, but deep spaces receive higher levels of UDI.

Valley Typology

Low mounts cut down on exposed surface area. However, wind only passes through the vally. Thus the low wind score and high solar irradiation performance.

Wall Typology Wall across the site means that half the massing is exposed to morning or evening sun. As such, ASE is deceptively low but levels of UDI show that daylighting could be improved.

Tower Typology

Close proximity of towers lead to self shading properties within the valleys. As such, ASE is of medium range but wind score is higher.

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Snake Slab Typology

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The snaking form shields interior spaces from the sun. As such, exterior surface rake up high solar irradiation but results in good ASE and UDI.

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Data Display Framework Data Domain From the 4D graph, the pareto front is extracted using non-dominance as detailed in Chapter 4: Multi-Objective Climate Optimization. Based on the position within data domain, 5 typologies can be extracted. The clustering of data will be even more apparent with more iterations.

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Parallel Coordinates Domain Now that the count is reduced to 5, parallel coordinates system can be easier to read and this can be combined with further information like site coverage to start looking at how the urban context might relate to climate performance. An example could be to look for massings with less site coverage as the site deals with a lot of connectivity from the commercial side and transport side. As such, the Tower Typology and Snake Slab Typology could be explored further and units could be designd to encourage more ventilation given the relatively poor wind performance.

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Punggol Waterway Optimization Results Punggol had a larger pool of results due to the size of the design space. This is because the site is the widest and had the largest GFA out of all 3 sites.

Generated Parametric Models Models which fulfil GFA Resultant Pareto Optimal Extracted Typologies

: n=1013 : n=357 : n=29 : n=5

Recap on Site Characteristics Punggol Waterway is connected on the South of the site with an LRT track on the East side. In terms of context, the site is partially shaded on the periphery by surrounding residential buildings. The site is also wide and thus large areas are left exposed to climatic conditions. More details can be found in Chapter 8: Site Selection.

Pareto Front Results In general, the generated design space is taller towards the periphery of the site. This is because this zone is shaded by surrounding residential blocks. As a result, massings with blocks within the shade would score more favourably in terms of solar irradiation and thus more likely to be nondominated. As a feedback for future optimization, the design space could be skewed to generate massings within context shade so as to reduce computing time.

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Punggol Waterway Pareto Front Due to the wide site and GFA condition, the solution space is large and this generates a big pool of designs. Although the periphery fares better in terms of solar irradiation, the wide site and limited impact of context still conjures up some interesting climatic strategies. Some include taller buildings in the middle to create self shading conditions within the massing and also clustered forms which create deeper and shady spaces.

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Cluster Slab Typology

Tower Typology

Clustered form minimizes exposed area, leading to reduced solar irradiation but deep floorplates means lower UDI.

Increased exposed surface area leads to higher overall solar irradiation. However, shallow floor plates result in higher UDI.

Snake Slab Typology Similar to cluster slab, however snake like form means that self shading properties are generated. As a result ASE performance is better.

Urban Courtyard Typology Massive blocks obstruct wind flow but geometry results in less exposed surface area and solar irradiation.

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Monumental Block Typology Singular blocks arranged far away from each other exposes towers to higher ASE. Higher solar impact reduces UDI and increases solar irradiation.

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Data Display Framework Data Domain Partially due to the size of solution space, there is a larger form diversity for punggol. Typologies are more distinct compared to hougang as the optimization is tending towards formally similar designs.

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Parallel Coordinates Domain Each typology deals with the connection to punggol waterway differently. The urban courtyard typology has a stepped face that breaks into smaller terraces looking out towards the waterway. Monumental Block Typology on the other hand has the south side open and extends the waterway into the residential communal area in a different way. Besides being able to look at these formal strategies, one can now decide together with the existing data provided by the data display framework.

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Queenstown Mei Chin Optimization Results This site is largely constrained by both size and the way the context towers over. Also, the GFA is the highest as queenstown is the most dense town out of all 3.

Generated Parametric Models Models which fulfil GFA Resultant Pareto Optimal Extracted Typologies

: n=864 : n=265 : n=23 : n=5

Recap on Site Characteristics Queens condominium towers over the North side with point blocks providing significant shade on the West side. The East is largely open as it is primarily made up of short bungalows. In a larger urban context, the site is interestingly near the first point block and slab block as well as the latest projects on hyperdensity in dawson skyville and sky terraces. More details can be found in Chapter 8: Site Selection.

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Pareto Front Results The generated pareto design space is most affected by context as it is skewed towards the queens condominium. As mentioned for punggol, the side which is shaded gives favourable solar irradiation values. Given how the context affects the design space, future parametric models can work on this fact to further reduce computation time.

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Queenstown Mei Chin Pareto Front The density of the site provided less diverse types of form with the majority having tower like forms towards queens condominium. However, some massings step down towards the bungalows, suggesting a strong formal treatment. One potential problem with the generated forms would be the deep spaces that came about. Courtyards could be punctured to bring light in or porosity introduced to allow diffused light inward.

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Molehill Typology

Stubby Block Typology

Resembling a molehill but with small cuts through the mass, wind is channelled into the higher level communal spaces.

The short block with cut outs allow light to penetrate deep spaces. The geometry also has self shading properties which lead to favourable ASE and solar irradiation.

Distinct Tower Typology

Tower + Podium Typology

Large amounts of exposed surface are leads to relatively poor solar irradiation performance. However, light penetrates deep spaces and lead to better UDI.

This typology is mid range across all 4 metrics. Similar to the distinct tower typology, this typology is more compact leading to better solar irradiation performance at the expense of UDI.

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Stepped Typology

Stepped down on one side and a wall on the other, wind is blocked and thus wind score is low. However, the geometry shades lower levels which lead to better ASE performance.

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Data Display Framework Data Domain Although formally less interesting than punggol, the queenstown pareto front shows a wide spread in terms of quantitative data. Amongst the highly similar massings, this extraction method was much faster and yielded formally interesting typologies as well.

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Parallel Coordinates Domain The site is at the end of two roads and thus the building would be largely self contained. As such, the main metric to look at could be a high site coverage as the ground level does not need to be as open as that of hougang. With less ground open spaces, the communal spaces could occur higher up, breaking down the scale of the surrounding tall blocks.

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Parametric Floorplan Explorations i. Aim of Search ii. Iteration 1 : Central Living Room iii. Iteration 2: Convex Living Room iv. Iteration 3: Spine and Branch v. Iteration 4: Circular Joint vi. Iteration 5: Orthogonal Joint

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Introduction This chapter details how each parametric floorplan iteration was conceived. The aim is to find a system which can be injected into the various floorplate sizes of the generated massing. Referring back to Chapter 6: Designing Constraints, while it is benefitial to explore a large geometrical solution space for the massing, the driving factor at the floorplan scale is how the individual rooms start to relate to one another. The following sections will cover: i. Location within Design Framework ii. Iteration 1 | Central Living Room iii. Iteration 2 | Concave Living Room iv. Iteration 3 | Spine and Branch v. Iteration 4 | Circular Joint vi. Iteration 5 | Orthogonal Joint Similar to Chapter 7: Parametric Geometric Explorations, the sections will cover each iterationâ&#x20AC;&#x2122;s parameters and constraints followed by a series of floorplans that show how the rooms are laid out. Beginning as a geometry search, the iterations slowly morphed into a more focused space hierachy search.

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Aim of Search This part of the thesis details a search for a parametric unit model which can be placed within any unit shape. The parametric model can then be plugged into the massing to generate an entire precinct of units for optimization purposes. Potential challenges include deriving a spatial network and subsequently the openings of these generated units.

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Iteration 1 | Central Living Room This Iteration mainly created apartments which had a living room as a central zone. Distance between rooms was controlled and it was conceptualized as trying to find apartments which work within all the geometry. The main drawback was the lack of spatial hierachy as living spaces seem uniform despite the vast geometrical pool. 01

However, the position of verandah shows some potential in terms of using outdoor spaces as a balcony or even as an apartment entrance.

The generation process is as follows:

1) Points generated from a center point 2) Rooms of dimensions created 3) Convex hull of points created 02

Parameters:

Constraints:

Position of each Point Dimension of Room Position of Room

Distance of points Maximum Size of Room

03 Iteration 1 Generation Process

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Iteration 2 | Concave Living Room This Iteration was a further development from iteration 1. Still looking for geometry as a main design driver, the spaces are still largely uniform with the living room as a central point. The main drawback is the same as that of iteration 1, with the lack of spatial heirachy.

However, the way in which rooms start to break up the living room into smaller portions start to suggest more interesting ways of creating privacy within the unit. Smaller living room spaces hidden behind rooms could act as a secondary gathering spot.

Space Usability 02

The generation process is as follows:

1) Points generated from a center point 2) Rooms of dimensions created Apartment 3) Polyline traces all points in order Convenience Geometrical System

Spatial Parameters:

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Position of each Point Dimension of Room Position of Room Rotation of Room

Constraints: Distance of points Maximum Size of Room

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Iteration 3 | Spine and Branch

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Iteration 3 was done to explore more in terms of creating connections from broken down spaces. As seen the living room and verandah is broken up into up to Apartment 3 smaller zones and this starts to suggest Convenience porosity within a unit. The spine and branch model also starts to create spatial hierachy Geometrical and is closer to the ultimate goal. System Spatial However, Hierachy given that living spaces are not luxurious, smaller zones would sometimes suggest unusable and tight spaces as seen in the floorplan diagrams. The parametric model also does not have a room relationship included and as such some rooms are ridiculously far apart from one another.

Space Usability

The generation process is as follows:

1) Main Spine generated 2) Branches grow from spine 3) Rooms generated from points Geometrical System Parameters: Spatial Hierachy

Dimension of Room Rotation of Room no. of Points on Spine & Branch

Apartment Convenience

Constraints:

Distance of points Maximum Size of Room Maximum no. of Rooms

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Iteration 4 | Circular Joint Moving into a search for spatial hierachy instead of geometry, the aim was to generate an adjacency diagram which can then inform room wall generation. Space Usability

Apartment Geometrical Using circles as joints, this starts to Convenience limit maximum System distance of points and also allow for large degrees of freedom. As an Spatial adjacency diagram,Hierachy this iteration seems quite successful in terms of subdividing spaces. 01

However, the connection logic is still missing as sometimes the rooms placed along the main spine are rooms like the toilet or bedrooms which are not effective as connection spaces.

Spatial Hierachy

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The generation process is as follows:

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Iteration 5 | Orthogonal Joint Building on the previous iteration, this uses rectangles instead. With a similar logic, movement is now orthogonal.

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In terms of Spatial spatial hierachy similar types of diversity is Hierachy retained. However, the algorithm tends to create new rectangles form the edge instead of along Apartment the adge as expected. Convenience Geometrical System Both iteration 4 and 5 both have the same issue of connection as some of the branches cut across rooms and makes it difficult to generate connections. This logic seems to produce reliable spaces and thus will be explored further within a massing module itself.

Space Usability

The generation process is as follows:

Apartment 1) Point cloud created from rectangles Convenience 2) Spine and branch diagram created Spatial from rectangles 3) Rooms generated Hierachy Geometrical Parameters: System

Dimension of Rectangle no. of Points on Spine & Branch

Constraints: Distance of points Maximum Size of Room no. of Rooms

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Thesis Design i. Selection of Punggol Waterway ii. Selection of Massing Iteration iii. Design Concept iv. Program Distribution v. Typical Floorplan

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Introduction This chapter translates earlier research into a building design. The focus would be on one block of a massing and through it, the design would show the different ways to approach: i. Unit Design within Variable Floorplan ii. Terraces Generated by Massing iii. Circulatory System Based on site response and collected data, the urban courtyard typology of Punggol Waterway was selected as the focus block.

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Selection of Punggol Waterway Based on site geometry, punggol has the widest one and as such there are more self shading conditions as compared to the other two sites. This resulted in a large design space with massings which are diversely differentiated. This site shows the most potential in terms of best representing the goal of the thesis which is to utilise multi-objective climate optimization to drive a new residential typology. The site is also unique in terms of opportunity with its direct connection to the waterway. Furthermore, it is adjacent to waterway terraces I and II which are two of the landmark residential buildings in Singapore. Thus, looking from the point of view of optimisation design space and site qualitative values, Punggol was selected.

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Context Response Terraced faces gives potential for communal areas looking out into context

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Formal Shift Shift from clustered blocks to regular towers, matching context form

Selection of Massing Iteration Looking back at the 5 optimised typologies, each massing has a different boundary condition. The urban courtyard typology was selected for further development because of the way it suggests a type of gateway into its main courtyard. This gateway is also characterized by a series of terraces which have potential of being designed as interesting communal spaces. Lastly, the way in which the massing shifts in terms of form sits in very well with the overall geometry of the site. Waterway Terraces has a unique hexagonal form whereas the other HDBs around are all typical tower blocks. The generated form blends both together to give a formal shift from clustered form to the typical tower. As such, this massing had the most potential and was selected for further development.

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Module Concept Since the parametric model was built in modules, the concept is to utilise each module in a way to contribute to the overall performance of the building. Given the scale of the module, courtyards are punctured to allow light inwards and facilitate ventilation. Also, the openings are angled towards communal areas so that wind is channelled towards these spaces. Next the negative spaces can be used as module communal spaces, further breaking down the scale, creating a new residential typology.

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Block Concept The aim is to retain as much of the generated form as possible and incorporate overhangs or cnatilevers into the design of spaces. In the previous page, modules of cut outs were explored and by playing around with these modules, the negative spaces can be stacked next to each other to create a continous space. This is an advantage of having such a long and deep form.

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Urban Scale

Block Scale

Tower Scale

Module Scale

Type

Ground Level Urban Courtyard

Mid Level Communal Space

Connected Terrace

Negative Spaces

Description

Sports Courts Playground

Running Track Fitness Corner

Open Green Space/Plaza

Large Balconies

User

Precinct Residents

Block Residents

Tower Residents

Neighbours

Program

Formal Events/Activities

Less Formal Activities

Impromptu Meeting

Chance Encounters

Overall Design Concept Building on these strategies, communal strategies were devised for each scale. This is in line with the design constraints as explored in Chapter 6: Designing Constraints whereby spatial hierachy was an important metric. When placed within a context, all these generated terraces start to have identity. A terrace nearby the waterway could be a fitness corner whereas those nearby the primary school could be playgrounds. It is important not to create generic open plazas as these do not encourage regular usage.

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Program Distribution By looking at the model in terms of floorplates, 4 main towers which will be serviced by 4 circulation cores can be derived. Combined with the floorplan algorithm developed from Chapter 10, residential modules can be populated with the courtyard concept. As seen in the negative vs positive space diagram, different scales of negative spaces or communal spaces start to appear with various densities of residential. Smaller module size neighbour spaces combine to become tower scale communal spaces on level 4 which in turn merge to form a single block scale communal space on level 7.

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Residential

Residential Roof Terrace

Residential

Residential Community Garden Roof Terrace

Commercial Childcare Bicycle Rental Drop-Off

Residential

Residential Jogging Track Fitness Corner

Residential

Roof

Residential

Residential Roof Terrace

Residential

Negative Space Positive Space Circulation Core

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Typical Floorplan This diagram shows a typical floorplan with different modules/towers coloured with different colours and each unit coloured with various shades of the same colour. The floorplan generator allows one to adjust module porosity so that modules produced would have at least a certain amount of porosity. For example, this series were produced with 75% porosity and as such, each module has access to exterior conditions.

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Typical Residential Level

Block Communal Level

Ground Public Area

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Punggol Parc Vista

Module Scale Communal Space

Block Scale Communal Space

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Waterway Terraces II

Punggol Waterway

Waterway Terraces I

Precinct Gateway

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Conclusion Due to resource constrain, a full optimization is still unreasonable to perform. However, simulation or comparison of designs could take on a similar method to the wind score whereby CFD is simplified into vector comparison. With advances in technology and rise in demand for green designs, multi-objective climate optimization could be a relevant design strategy for future developments. In the near future, it could help with the arrangement of HDB units as they are of regular size and most of the metrics like unit mix are quantitative. Using the computational strengths of processing large amounts of data, architects now have the power to perform more informed decisions as compared to the past.

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References

1. Kishnani, Nirmal. Greening Asia: emerging principles for sustainable architecture. Singapore: BCI Asia, 2012. 2. Hanan M. Taleb, “Using passive cooling strategies to improve thermal performance and reduce energy consumption of residential buildings in U.A.E. buildings” (PhD diss, British University in Dubai, 2014) 3. “Thermal Comfort.” Occupant Comfort | Sustainability Workshop. Accessed April 18, 2017. https://sustainabilityworkshop.autodesk.com/buildings/ occupant-comfort. 4. “Home.” CapitaLand Commercial Trust. Accessed April 23, 2017. http://cct.com.sg/. 5. “South Beach Towers, Foster & Partners | Singapore ...” Accessed April 25, 2017. 6. Nirmal, op. cit. 7. Christoph Mitterer, “Optimizing energy efficiency and occupant comfort with climate specific design of the building” (2012) 8. “Weather and Climate in Singapore.” GuideMeSingapore. Accessed April 19, 2017. https://www.guidemesingapore.com/relocation/introduction/ climate-in-singapore. 9. “Climate of Singapore.” Climate of Singapore |. Accessed April 19, 2017. http://www.weather.gov.sg/climate-climate-of-singapore/ 10-11. Ibid 12. Jakubiec, Alstan “A Spatial and Temporal Framework for Analysing Daylight, Comfort, Energy and Beyond in Conceptual Building Design” 13. “Climate of Singapore.” Climate of Singapore |. Accessed April 19, 2017. http://www.weather.gov.sg/climate-climate-of-singapore/ 14-20. Ibid 21. ”Public Housing – A Singapore Icon.” Public Housing – A Singapore Icon | HDB InfoWEB. Accessed April 20, 2017. http://www.hdb.gov.sg/cs/infoweb/about-us/our-role/public-housing--a-singapore-icon. 22. Ibid 23. Alivia Mondal Follow. “Natural ventilation.” LinkedIn SlideShare. December 22, 2015. Accessed April 23, 2017. https://www.slideshare.net/AliviaMondal/natural-ventilation-56377030. 24-27. “Treelodge@Punggol | HDB InfoWEB.” Accessed April 25, 2017. http://www.bing.com/cweb%2fabout-us2four-role%. 28-30. “No.1 Moulmein Rise” Accessed April 25, 2017. http://www.akdn.org/sites/akdn/files/media/documents/AKAA%20press%20kits/2007%20AKAA/ Moulmein%20Rise%20-%20Singapore.pdf 31. Ciftcioglu, Ozer et al. “Adaptive Formation of Pareto Front in Evolutionary Multi-objective Optimization.” 32. Ibid 33. Lauber, Wolfgang. Tropical architecture: sustainable and humane building in Africa, Latin America and South-East Asia. Munich: Prestel, 2006. 34. “Country Comparison > Population density.” Population density - Country Comparison. Accessed April 25, 2017. https://www.indexmundi.com/g/r. aspx?v=21000. 35. “Solar Radiation & Photosynthetically Active Radiation.” Environmental Measurement Systems. Accessed April 25, 2017. http://www.fondriest.com/ environmental-measurements/parameters/weather/photosynthetically-active-radiation/. 36. “Solar Photovoltaic Systems.” EMA : Solar Photovoltaic Systems. Accessed April 25, 2017. https://www.ema.gov.sg/Solar_Photovoltaic_Systems.aspx. 37. Robinson, Darren. “Irradiation modelling made simple: the cumulative sky approach and its applications” 38. “Climate of Singapore.” Climate of Singapore |. Accessed April 19, 2017. http://www.weather.gov.sg/climate-climate-of-singapore/ 39. Beaufort Wind Scale. Accessed April 25, 2017. http://www.spc.noaa.gov/faq/tornado/beaufort.html. 40. “Calculating the daylight factor”. Accessed April 20, 2017. https://faculty.unlv.edu/kroel/www%20731%20spring%202006/daylight%20factor.pdf 41. “Climate of Singapore.” Climate of Singapore |. Accessed April 19, 2017. http://www.weather.gov.sg/climate-climate-of-singapore/ 42-44. “Useful Daylight Illuminance.” Daylighting Pattern Guide. Accessed April 25, 2017. http://patternguide.advancedbuildings.net/using-this-guide/ analysis-methods/useful-daylight-illuminance. 45. “Recommended light levels.” Accessed April 25, 2017. https://www.noao.edu/education/QLTkit/ACTIVITY_Documents/Safety/LightLevels_outdoor+indoor.pdf 46. “Solar Radiation & Photosynthetically Active Radiation.” Environmental Measurement Systems. Accessed April 25, 2017. http://www.fondriest.com/ environmental-measurements/parameters/weather/photosynthetically-active-radiation/#PAR1. 47. “Measuring Daylight: Dynamic Daylighting Metrics & What They Mean for Designers.” Sefaira. March 11, 2014. Accessed April 25, 2017. http://sefaira.com/resources/measuring-daylight-dynamic-daylighting-metrics-what-they-mean-for-designers/.

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Appendix A

What company/department are you from?

DP SUSTAINABLE DESIGN

General: What is the main aim behind performing climate simulations? The main objective is to create sustainable building profiles that will positively impact our society. We perform various environmental simulations and studies to give the architect insight into how the architecture will perform within the given context. Climatic analysis value-adds to the building designs by providing additional perspectives to the architecture.

When did the company start to perform climate simulations and what were the programs that were used? The programs used at the beginning were Star-CCM+ and Autodesk Ecotect, Autodesk Vasari. The software programs mentioned above have been phased out, with the exception of Ecotect, which has remained relevant.

How long does it take to perform the above simulations? The simulations vary in duration, depending on characteristics of the model, such as scale, level of geometry complexity, size of solver algorithm and computer hardware.

Describe briefly the current method of climate optimization in the company. The current method is for the architecture to be designed, before climatic analysis is provided to propose optimized solutions for the building.

What are the aspects of the above method or mentioned simulations that could be improved? (eg. Time taken, role that it plays during design stage) The design stage can include sustainable climatic considerations to reduce the time taken between initial and finalised ideas. Currently, the finalised ideas are often delayed as the building designs may experience changes to correct any climatic oversight, such as excessive heat gain or poor natural ventilation within the architecture. Numerous collaborations are moving towards including the role of environmental sustainability during the design stages. This strategy allows the architect to design buildings that have better climatic performance to tackle predictable problems that are constantly overlooked, until it is too late for drastic improvements to be made. 188

Appendix A

Referring to Residential Typology: What type of simulations are performed on the building massing? Are there any specific spaces of interest? The residential typology requires several simulations as the function of residential buildings has to cater for many criteria. The simulations performed usually revolve around the occupancy patterns as well as the general building profile. Solar and wind simulations are usually used to analyse the buildingâ&#x20AC;&#x2122;s performance.

Some of the key interests for this typology are Unit Layout, View, Thermal Comfort, Natural Ventilation, Daylighting and Accessibility.

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