March 2 Thesis Project _ The Gateway to Zero Carbon Future City

Manchester school of architecture

2021 CPU[Ai] Studio 3

THE GATE WAY TO ZERO CARBON CITY

C+O2 → CO2

////////////////////////////////

Yan Chen/ Yirui Chen/ Linyu Li

2021 CPU[Ai] Studio 3

Manchester school of architecture

Summary STUDIO 3 × CPU[AI] 2021

SUMMARY

THE GATE WAY TO ZERO CARBON CITY

Within this project, we work with Manchester City Council and the Northern Gateway developers as a consultant to engage on a range of challenges in the Northern Gateway development. This portfolio demonstrates the whole design process with three main aspects: Firstly, by targeting the challenge of ‘Zero Carbon Future City’, we fully analyse the Northern Gateway data, formulate the specific design problem, define the theoretical framework and design principles.

Based on previous studies, we secondly develop a generative design tool that aims to provide multiple iterations for the design targets. At this stage, the spatial strategy, computational workflow and evaluation criteria are key drivers not only for experimenting with tool function but also adopting previous theories and design principles.

In the last aspect, the data of generated iteration are analysed and evaluated based on the ‘Zero Carbon Future’ challenge and related researched criteria, which also can be interpreted as the response to the thesis design problem. In addition, the design tool interface and the architectural atmosphere of the generated results will also be presented as key points.

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2021 CPU[Ai] Studio 3

Manchester school of architecture

Thesis Statement STUDIO 3 × CPU[AI] 2021

THE GATE WAY TO ZERO CARBON CITY

THESIS STATEMENT

Due to the city’s impact on climate change, the issue we investigate is how to design the future city towards Zero-carbon, minimizing operational energy demand and the possibility of production of energy through renewable resources. With this target, we put the lens on the question of how to design the urban morphology (novel urban form and building typology)through a generative approach to deal with potentially contradictory correlation among morphological compactness, building solar optimization and green space distribution, and thus achieve the significantly improved design outcomes that aim at minimizing building energy demand/carbon emission and enhancing the potential of renewable energy generation.

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Content STUDIO 3 × CPU[AI] 2021

CONTENT_

05 11 22 31 50 73 82 101 113 120

Chapter 1

[PROJECT INTRODUCTION] Chapter 2

[DESIGN PROBLEM IDENTIFICATION & THEORY RESEARCH] Chapter 3

[DESIGN TOOL INTRODUCTION] Chapter 4.1

[TOOL MECHANISM_ URBAN & BLOCK LEVEL] Chapter 4.2

[TOOL MECHANISM_ BUILDING LEVEL] Chapter 4.3

[TOOL MECHANISM_ TEST AND EVALUATION] Chapter 5

[RESULT ANALYSIS]

Chapter 6

[TOOL INTERFACE REVIEW & MANUAL]

Chapter 7

[PROJECT DELIVERABLES]

Chapter 8

[CONCLUSION]

2021 CPU[Ai] Studio 3

Manchester school of architecture

THE GATE WAY TO ZERO CARBON CITY

Chapter 1

PROJECT INTRODUCTION

1.1 BRIEF INTRODUCTION 1.2 EXISTING SITE CONDITION 1.3 CHALLENGE SELECTION 1.4 PROJECT ROADMAP

This chapter will introduce the Northern Gateway project and our choice of the challenge. Moreover, we demonstrate the overall project roadmap and our current stage.

5

AERIAL OF NORTHERN GATEWAY 2021 CPU[Ai] Studio 3

Manchester school of architecture

THE NORTHERN GATEWAY

Source: http://northerngatewaymanchester.co.uk/

6

2021 CPU[Ai] Studio 3

1.1 BRIEF: RESILIENT URBAN DEVELOPMENT IN NORTHERNGATEWAY

Manchester school of architecture

Introduction STUDIO 3 × CPU[AI] 2021

The Northern Gateway is a major regeneration project being undertaken as a joint venture by Manchester City Council with the Far East Consortium (commercial partner).

The project is focused on residential development. The current population of the area is 35,000. The area is being considered an extension to the city centre and will need to function with similar density.

The concerns of FEC revolve around the need to address commercial viability and infrastructure provision. The MCC wishes to ensure a wide range of typologies and a family orientated development looking at long-term sustainability of the population and the legacy of the development project.

THE NORTHERN GATEWAY

Source: 1.CPU_Brief_ST1 2.The Northern Gateway Strategic Planning Framework https://www.room151. co.uk/funding/leveraging-council-land-value-thejoint-venture-approach/

CPU will study the Northern Gateway development as a consultant for MCC. We will examine the development from a resilient urban future perspective. We will address some parts of the identified MCC focus areas above in Studio 1, Studio 2 and Studio 3.

CPU_Aterlier Group Research document & Manchester Building Typology Research Collection

7

1.2

Introduction STUDIO 3 × CPU[AI] 2021

EXISTING SITE CONDITION EXISTING SITE

Information About The Northern Gateway

3km

2km

COLLYHURST 1km

0km

EXISTING SITE

Manchester City Centre 1km

LOWER IRK VALLEY

POPULATION

35,000

GREEN AREA

316.206m2(58%)

DENSITY

445.327m2(12%)

2km

3km

3km

2km

1km

0km

1km

2km

3km

NEW CROSS

The Northern Gateway is Manchester’s single most significant and ambitious regeneration potential. The region encompasses approximately a third of the expanded city centre and reflects a scale of growth that necessitates a holistic approach that considers short, medium, and long-term possibilities.

Rail Station

Bus Stop

Railway Source: 1.STRATEGIC REGENERATION FRAMEWORK 2.The Northern Gateway Strategic Planning Framework https://www.room151.co.uk/funding/leveraging-council-land-value-thejoint-venture-approach/

8

New Added 15,000 Residential

2021 CPU[Ai] Studio 3

Manchester school of architecture

CHALLENGE 1 How can a balance between public and private spaces foster a sense of community and belonging in new urban morphology. Ensure public spaces are active throughout the day and evening and do not adversely interfere with a residential setting (in terms of noise, ASB etc.)?

CHALLENGE 2 The distribution of facilities, amenities and community spaces is an essential aspect of successful residential development. How do we design to ensure this aspect of sustainability in urban strategy and design.

1.3 CHALLENGE SELECTION

CHALLENGE 3 How can a new urban development be designed to change and adapt with its residents (from students to young professionals, families and aging)

THE GATE WAY TO ZERO CARBON CITY

CHALLENGE 4 How can a network of high-quality open and public spaces support well-being and enhanced diversity. Integrating green spaces/public realm towards wellness and mitigation of climate change? Ecologies? How can you integrate green environments and the City River Park ecosystem?

CHALLENGE 5 How can you design for sustainable movement and minimise motorised transport use? Consider last mile/3 mile responses including transport oriented design, walk-ability, cycling and technological disruptions (CAV).

CHALLENGE 6 How to design zero-carbon future cities (is urban morphology adequate). How do you understand the environmental impact of future cities.

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2021 CPU[Ai] Studio 3

Manchester school of architecture

Introduction STUDIO 3 × CPU[AI] 2021

1.4 PROJECT ROADMAP STEP 1 • ANALYSING CHALLENGE RELATED DATA • FORMULATING DESIGN PROBLEM • THEORY RESEARCH • DEFINING DESIGN PRINCIPLE

To Demonstrate Our Work-flow Of Entire Project And Key Focuses In Different Studios

STEP 2 • DEFINING THE SPATIAL STRATEGY • CLARIFYING THE DESIGN TOOL LOGIC • EXPERIMENTING THE POSSIBILITY OF THE

STEP 4

• FINALIZING THE

• DESIGN TOOL APPLI-

TOOL’S FUNCTION

CATION

STEP 5 • INTERFACE USABILITY AND VISUALIZATION

BOTH AT URBAN LEVEL AND BLOCK LEVEL

• OUTCOME ANALYSIS

• BUILDING UP THE IN-

• DESIGN OUTCOME VISUALIZATION

TERFACE

TOOL Within this step, we finalize the

The design tool aims to gen-

The procedure of interface is

COMPUTATIONAL AP-

generative design process in

erate multiple iterations with

clear demonstrated step by

PROACH

low carbon urban form gener-

comparable data readout both

step, as well to each step’s

ating, whilst detailing the solar

in urban level and block level.

result.

typology at the block level.

The results are fully analysis,

The architectural atmosphere

The interface aims to not only

which aims to evaluate how rel-

is also seen as the key aspect

showcase the generation result

evant research and design tool

to demonstrate.

but also provide detailed data

development respond to the

and allow the user to compare

initial challenge set and thesis

and analyse.

research question.

• DEFINING INITIAL

THE GATE WAY TO ZERO CARBON CITY

STEP 3

optimization based hybrid

STUDIO 1

STUDIO 2

STUDIO 3

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2021 CPU[Ai] Studio 3

Manchester school of architecture

THE GATE WAY TO ZERO CARBON CITY

Chapter 2 2.0 CHAPTER SUMMARY

DESIGN PROBLEM IDENTIFICATION & THEORY RESEARCH

2.2 CLIMATE PROJECTION 2.3 DEMOGRAPHIC PROJECTION 2.4 MANCHESTER ENERGY USAGE ANALYSIS 2.5 UK CO2 EMISSION ANALYSIS 2.6 CO2 EMISSION ANALYSIS IN CONSTRUCTION INDUSTRY 2.7 MANCHESTER BUILDING EMISSION 2.8 DESIGN PROBLEM MAPPING

This chapter will demonstrate the challenge-related data analysis, design problem formulation, theories and design principles. Moreover, a theoretical framework is proposed to show how we apply the studied theories to the defined design problems and be the initiatives of design tool development.

2.9 APPLY STUDIED THEORY TO THE DESIGN PROBLEM 2.10 THEORETICAL FRAMEWORK 2.11 DESIGN PRINCIPAL STUDY

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STUDIO 3 × CPU[AI] 2021

2.0

CHAPTER SUMMARY DATA ANALYSIS

DESIGN PROBLEM CONSIDERATION

EXTREM CLIMATE SITUATION IN THE FURTURE

Low Density Urban Pattern

START POINT

CO2 EMISSION SITUATION IN UK AND MANCHESTER Agriculture 10%

Transport 28%

32%

Business 18%

Energy Supply 23%

High Energy Demand of in-use building

30% Domestic

Brief: Resilient Urban Future CO2 EMISSION OF BUILDING INDUSTRY

MMC Challenge Picking Up: Zero Carbon City

22%

185 MtCO2e

was total operational and embodyied carbon footprint of the building environment

831 MtCO2e

was the total carbon footprint of the UK in 2014

DEMOGRAPHIC INCREASE

LOW WALKABLITY OF GREEN AMENITY

DEFINED ISSUES

Resilience Theory

The compactness of urban form has the potential in impacting the building energy usage and walkability of green amenities.

Meanwhile, it provides the solution to demographic increasing

Panarchy Theory Complex Adaptive System

COMPUTATIONAL METHOD CONSIDERATION

Genetic Algorithm CONTRADICTORY CORRELATION IN VARABLES SET Energy Efficient Urban form

Non-domestic

MANCHESTER CO2 EMISSIONS IN 2017

UK GHG EMISSION ANALYSIS

Compact Morphology

38%

Transport

Residential 15%

THEORETICAL FRAMEWORK & DESIGN PRINCIPLES

42%

349 MtCO2e

was attribute to the build environment

Data of project

Green Amenity walkability demand

DESIGN PROBLEMS

Solar Potential

CO2 CO2 CO2

APPLIED HIGH LEVEL THEORY

To enhance building surface's solar irradiance potential can contribute to renewable power generation, which acts as the vital aspect towards to zero-carbon future

CO2

CO2

Urban Morphology

Green Amenity Distribution

Solar Optimized Urban & Building form CO2

Urban form design principle for Energy Efficiency

Ideal and efficient green amenity distribution can significantly increase the walkability, which also provide opportunity to reduce the co2 emission

Urban form design principles for solar potential

LOW CARBON URBAN FORM & SOLAR OPTIMIZED HYBRID BUILDING TYPOLOGY

CO2

CO2

CO2

CO2

Green Amenity Walkability and Distribution CO2

Circle Patching for Green Amenity distribution

INITIAL STRATEGY CONSIDERATION

CO2

CO2

CO2

CO2

APPLIED DESIGN PRINCIPLES & METHOD

DESIGN TOOL EXPERIMENT

12

2.2

2.3

CLIMATE PROJECTION

DEMOGRAPHIC PROJECTION

Overview

Further Research

Overview

• Winter precipitation is expected to increase significantly • Summer rainfall is expect to decrease significantly • But when it rains in summer there may be more intense storms • A greater number of cooling degree days • A decreasing number of heating degree days • Drier summers • Wetter winters

With in the high emissions scenario for the 2050s. The research indicates that under the high emissions scenario for the 2050s, the central estimate of increase in annual mean temperature across Greater Manchester is 2.4– 2.5°C; it is very unlikely to be less than 1.8°C and is very unlikely to be more than 3.6–3.7°C. Therefore, regarding to the environmental impacts, the consideration of future urban development and building design should mainly focus on providing the better control of comfortable internal temperature. That means more energy consumption and greenhouse gas emission.

Population projections suggest that while the total population will increase, the 16-64 age range will see a net decrease for the region, with ages of 65+ increasing. As 16-64 can be regarded as the primary working age range here, the net increase in job sectors while this working age will decrease suggests that there will be a larger number of commuters living outside the region coming to work in Manchester over the next twenty years.

Further, net migration is negative, predicting that people will be moving out of the region.

Population Growth In Thousands

-20 - -10 -10 - 0 0 - 10 10 - 20 20 - 40 40 - 80 Precipitation projection graph

Temperature projection graph

Temperature projection graph

1991-2016

80 - 160

2016-2038

3100

Total population in Manchester Date of Projection

Projected maxmum total population 3000

2900

2800

2700

2600

13

2038

2037

2036

2035

2034

2033

2032

2031

2030

2029

2028

2027

2026

2024

2025

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

GREAT MANCHESTER

1992

2500 1991

Precipitation projection graph

2.4

MANCHESTER ENERGY USAGE ANALYSIS

2.5

UK CO2 EMISSION ANALYSIS

Public

Industrial Process

Land use, land use change and forestry

Waste Management

Agriculture 10%

Transport 28%

Proportion of GM energy consumption by sector Residential 15%

Domestic Electricity 9%

UK GHG EMISSION ANALYSIS

Greenhouse Gas Emissions by Sector

Road Transport 28%

Domestic Gas 27%

In 2018, 28% of net greenhouse gas emissions in the UK were estimated to be from the transport sector, 23% from energy supply, 18% from business, 15% from the residential sector and 10% from agriculture.

Business 18%

Energy Supply 23%

Bioenergy and Waste 4% Non Domestic Other 2% Domestic Coal 1% Non Domestic Gas 15%

32%

Non Domestic Electricity 14%

38%

Transport

Non-domestic

MANCHESTER CO2 EMISSIONS IN 2017

Space Heating and Hot Water are estimated to account for 77% of domestic energy demand. Manchester, Salford and Trafford are the districts with the highest heat demand density areas.

Greenhouse Gas Emissions by Sector Manchester’s direct CO2 emissions come from our homes, workplaces and ground transport. In 2017 our direct emissions were 2.1 million tonnes.

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30% Domestic

3%

5%

-

77%

40%

38% 36% 17% 23% 20%

7%

5%

2%

Other

Don't know

Medium player

Small player

Domestic

Design Distribution 1% 1%

Infrastructure

160 140

Manufacture 15%

Outdoor lighting Waste Water Waste water

120 100 80

Heating Cooling Lightling Hot water

60 40 20

Operation 83%

70% 19% 4% 7%

Cooking and plug loads

0 Operational energy uses

26%

MtCO2e

41% 20% 36% 21% 34%

Better sound quality

EXISTING BUILT ASSETS

was generated by operational energy uses (e.g. heating)

41%

BY SECTOR

Non-Domestic

138

78%

Energy use in building

185 MtCO2e

was total operational and embodied carbon footprint of the building environment

Cooking 5%

48

MtCO2e

was embedded through new construction

Appliances 16%

55% PRODUCTS

10%

TRANSPORT

20%

Lighting 20%

Space Heating 53%

Water Heating 20%

CONSTRUCTION

BY DOMESTIC OPERATION

Source: • UKGBC Climate Change: https://www.ukgbc.org/climate-change/

Source: • DECARBONISING GREATER MANCHESTER’S EXISTING BUILDINGS (n.d.).

15

Energy efficiency of the building

22%

NEW CONSTRUCTION

BY LIFE CYCLE PHASE

Real time space allocation

was the total carbon footprint of the UK in 2014

MtCO2e

-

Large player

Commercial 14%

Domestic 27%

3%

Modularity allowing to rethink space allocation over time

MtCO2e

Transport 38%

was generated through plug loads and cooking in buildings

Existing BUILT ASSETS

831

Industry 18%

9%

Possibility to be connected with the exterior environment

44

Other 3%

29% 29% 28% 38% 24%

Better air quality

MtCO2e

was generated by direct emissions from road and rail transport

41%

User safety

119

86%

49%

was attribute to the build environment

The domestic accounts for a significant portion of carbon emissions from buildings in Manchester, with space heating being the largest contributor. The most effective approach, as the chart showing, is to improve energy efficiency.

loT allowing intergrated usage of all connected objects

349 MtCO2e

TRANSPORT

42%

The built environment contributes around 40% of the UK’s total carbon footprint. Almost half of this is from energy used in buildings (e.g. plug loads and cooking) and infrastructure (e.g. roads and railways) that has nothing to do with their functional operation. Despite erratic annual variations, the carbon footprint of the built environment has reduced since 1990. Insulation installation rates between 2008 and 2012 and de-carbonisation of grid electricity both contributed to this downward trend.

MANCHESTER BUILDING EMISSION

79%

2.7

MtCO2e

2.6

CO2 EMISSION ANALYSIS IN CONSTRUCTION INDUSTRY

2.8 Climate Projection

Heat Wave In Summer

MAPPING OF DESIGN PROBLEMS PRINCIPLE 1: Prioritize land use efficiency in both new town development and urban renewal

Morphological Compactness

PRINCIPLE 3:

Increasing Population

PRINCIPLE 4:

High Density Inhabitation Occupant Energy Demand

Land Use

Lack of Urban Green Space

Transportation In Construction

Operational Energy

Building Industry

High Density Development

Transportation

Create and maintain more quality public spaces for the general public

Green Amenity

PRINCIPLE 5: Keep in mind the energy and environmental performance of building operations PRINCIPLE 6: Striving for energy and resource efficiency in the industrial and commercial sectors; pursue industrial symbiosis and the “circular economy.”

PRINCIPLE 7: Improving waste recycling and implementing waste minimization mechanisms

RESOURCE EFFICIENCY

Demographic

Reduce private vehicle use through improved urban layout, efficient public transport networks.

PRINCIPLE 8: Restore and improve urban ecological water cycles PRINCIPLE 9: Transition from “city management” to “city governance”

INCLUSIVE URBAN GOVERNANCE

Colder Winter

Develop non-motorized transport as a major component of public Transportation

LOW CARBON URBAN FORM

PRINCIPLE 2:

PRINCIPLE 10: Establish clear socio-environmental thresholds and assessment mechanisms for urban infrastructure investments and financing to support green and lowcarbon development

16

Land-use

Energy Demand

Solar Potential

New urban form

Emergence

K

Morphology Plot Scale & Relationship

Through The Learning Of Resilience And Complex Adaptive System, We Can Understand Our Design Problems From Difference

Drive

Development limited New Block pattern

K

Emergence

Block

Negative[-] Positive [+]

Self-Organising

r

Original Urban fro m Need to develop

Adaptive Behaviour

The Adaptive behaviour also affects the process of urban resilience in different layers.

From the perspective of Resilience, we hope that our city can implement Zero Carbon in the future. So the adaptive behaviours will happen in the process of urban development in the future all the time, disturbance force at all layer of the adaptive behaviour.

When a threshold is reached, the system will enter another stage of evolution. We hope to realize zero-carbon form through this method in the future, which may come from the new architectural form and new spatial understanding brought by the progress of science and technology.

Building Density

r

Building Configuration

Original Block pattern Low efficiency

Building

Adaptive Behaviour Negative[-] Positive [+]

Source: • Adam Brennan, Manchester School of Architecture (Complexity Planning and Urbanism • Faucher, Jean-Baptiste. “A Complex Adaptive Organization Under the Lens of the LIFE Model: The Case of Wikipedia”. Egosnet.org. Retrieved 25 August 2012 • Source: https://www.thoughtworks.com/insights/blog/leading-living-breathing-and-agile-enterprise • Patricia Romero-Lankao, Daniel M. Gnatz , , Olga Wilhelmi, Mary Hayden, 2016. Urban Sustainability and Resilience: From Theory to Practice. MDPI • “Resilience and stability of ecological systems”. in: Annual Review of Ecology and Systematics. Vol 4 :1-23 • Walker, B., Holling, C.S., Carpenter, S.R., Kinzig, A.P., 2004. Resilience, Adaptability and Transformability in Social ecological Systems. Ecology and Society

Carbon emission problem

Green Space

APPLY STUDIED THEORY TO THE DESIGN PROBLEM

As stated in the concept of Complex Adaptive System, the Complex adaptive system can be multi-layered, but more often it is "self-organizing". So we made our question from a much layered perspective. We explore the method to implement Zero Carbon City from three layers. At the scale of Building, Block and Urban, the system organizes itself by related factors. However, the adaptive behaviour generated thus will affect adjacent layers. Feedback from the upper layer will also affect the adjustment of parameters to adapt to the new situation.

Self-Organising

Drive

Self-Organising

Drive

Lose value New Building types

Emergence

2.9

Negative[-] Positive [+]

Urban

Adaptive Behaviour

Building Type Building Hight Building Weight & Depth

r Building In Use Demolished

17

K

2.10

THEORETICAL FRAMEWORK

Demonstrate How To Apply The Study Of High Level Theories Into Different Scales Of Design Problems

Through the learning of Resilience Theory and Complex adaptive system, we defined the theoretical lens to understand and investigate the potential correlation among three design problems (Energy Efficiency of in-use Building, Solar Potential optimization and Green Amenity distribution) and how to solve them from a resilient and adaptive perspective. Three scales of self-organising produce adaptive behaviours to make cities adaptable. Adaptive behaviours will also become the drive of resilient cities, prompting cities to achieve evolution in the future. All these metrics and goals are to optimize Solar potential, Energy performance and Green Amenity distribution.

Urban Green Space

Energy Performance Remember

Remember

K

α

K

α

α

Revolt r

Drive

Negative[-] Positive [+]

Negative[-] Positive [+]

Adaptive Behaviour Emergence

Block

Self-Organising

Ω

Adaptive Behaviour Emergence

Negative[-] Positive [+]

Negative[-] Positive [+]

Negative[-] Positive [+]

Emergence

Adaptive Behaviour

Urban

r

Ω

Drive

Drive

Ω

Emergence

r

Revolt

Building

Self-Organising

Self-Organising

2 1

3

÷

4 W

H

N

H

18

Emergence

K

Solar Potential

2.11

DESIGN PRINCIPLE STUDY

Correlation Between Morphology & Energy Use, Solar Potential, Co2 Emission

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2 CO2

CO2

CO2

CO2

Energy

SPREAD MORPHOLOGY

SPREAD MORPHOLOGY

SPREAD MORPHOLOGY

Solar Potential

CO2

CO2

Urban Green Space

CO2 CO2

CO2

CO2

COMPACT MORPHOLOGY

Energy

CO2

CO2

CO2 CO2

CO2

CO2

CO2

COMPACT MORPHOLOGY

Solar Potential

CO2

CO2

CO2 CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

Traffic

CO2

CO2

CO2

CO2

SPREAD MORPHOLOGY

CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

COMPACT MORPHOLOGY

CO2

COMPACT MORPHOLOGY

Traffic

CO2

Urban Green Space

CO2

Key Spatial Aspect : 2 1

Havg

H4

3 4

H3

H2

W1 W2

H1

Plot Ratio

S/V Ratio

Average Height

Building Coverage

0.9 ≥ H/W

Source: CITIES AND ENERGY (Urban Morphology and Heat Energy Demand) Cheng, V., Steemers, K., Montavon, M. and Compagnon, R. (2006) ‘Urban Form, Density and Solar Potential,’ January.

H

Aspect Ratio

or H/W ≤ 1.1

N

Building Density 2

Main Street Orientation

Horizontal Layout

MOS <1.15

Randomness

19

Vertical Layout

Randomness

Green Space in Urban Area

Scatter degree of Green Space

Chapter 2

CO2 CO Emissions 2 Emissions

CONCLUSION

CO2 Emissions CO2 Emissions CO2 CO2 Sequestration Sequestration

CO2 CO Emissions 2 Emissions

-

+

+ -

-

What we propose: As the conclusion, we proposed a roadmap of how to achieve the ‘Zero Carbon Future‘ theoretically.

By combining the defined problems, high level theory and design principles, we should aim to achieve an urban form that can reduce the energy demand of in-use building, increase building surface solar irradiation potential and efficiently distribute the green amenities.

Adaptive Adaptive Evolution Evolution

Energy Energy InputInput

In-use Building Energy Usage

Solar Potential

Spatial Factors related to Energy Efficiency

Spatial Factors related to solar potential

Low Energy Low Energy InputInput

Low Energy Low Energy InputInput

Current Urban Form

Green Amenity Distribution

Complex Adpative System

Resilience Theory

Adaptive Adaptive

Scatter Green Space

Improve Compactness

Low-energy City City Low-energy Zero-carbon Zero-carbon City City

Spatial Factors related to Green Amenity distribution

CO2 Emissions CO2 Emissions CO2 CO2 Sequestration Sequestration

Adaptive Adaptive Evolution Evolution

Low-carbon City City Low-carbon

+

+ -

CO2 Emissions CO2 Emissions CO2 CO2 Sequestration Sequestration

-

+

+ -

CO2 Emissions CO2 Emissions CO2 CO2 Sequestration Sequestration

-

+ -

On-Site On-Site Energy Energy Generation Generation

Adaptive Adaptive

Evolution Evolution

Low Energy Low Energy InputInput

Renweable Renweable Energy Output Energy Output

Source: CITIES AND ENERGY (Urban Morphology and Heat Energy Demand) Cheng, V., Steemers, K., Montavon, M. and Compagnon, R. (2006) ‘Urban Form, Density and Solar Potential,’ January.

+

On-Site On-Site Energy Energy Generation Generation

On-Site On-Site Energy Energy Generation Generation

Achieve Negative Energy Demand

-

Equilibrium of On-site Energy Generation and Energy Demand

20

On-site Energy Generation

21

2021 CPU[Ai] Studio 3

2021 CPU[Ai] Studio 3

2021 CPU[Ai] Studio 3

Manchester school of architecture

THE GATE WAY TO ZERO CARBON CITY

Chapter 3 3.1 INTENTION OF DESIGN TOOL

DESIGN TOOL INTRODUCTION

3.2 DESIGN TOOL FUNCTION 3.3 TARGET USERS 3.4 SPATIAL STRATEGY OF COMPUTATIONAL PROCESS 3.5 DESIGN TOOL PSEUDO CODE 3.6 INTERFACE PREVIEW

This chapter demonstrates the overview of the design tool, which includes the tool’s intention, main functions, potential user, applied spatial strategy and computational coding logic. The previously identified design problems studied theories & principle and proposed concept are critically considered through the tool’s development.

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3.1

INTENTION OF DESIGN TOOL

The Initiatives and Reasons For Developing The Generative Design Tool, which are based on Previous Data Analysis and Theoretical Research

TO MANIPULATE MULTIPLE PARAMETER AND VARIABLES TO CONTROL AND OPTIMIZE

DEMANDS FOR MULTIPLE RESULT SIMULATION AND & EVALUATION

Grid Width

Electricity Consumption Simulation

Grid length

Energy

Energy

Energy Efficient urban form

Electricity Generation Simulation

Landuse Ratio

Program Ratio

Input Info

Energy

Opposite demand for urban form

2021 CPU[Ai] Studio 3

To Achieve Research design target & the Fomulated Computational logic

Opposite demand for urban form

Ideal Green Amenity Distribution

Green Amenity Orientation

Solar Irradiation Simulation

Hight Constraints Factor

Building Orientation

Building Position Factor

Green Amenity Walkbility Evaluation

Building Sunlight Cutting Angle

Solar Envelope Optimization Factor

Residential Unit Number Evaluation

Hybrid Typology Option

Solar Irradiation Optimization

The complex correlation requires generative process to conduct different iterations for further evaluation

Multiple Variables to Control

Energy Related Spatial Factor Evaluation

It is infeasible to manipulate the variables to generate multiple iterations manually. The adjusting variables corresponding to the evaluation feedback is the essential step for generative design

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2021 CPU[Ai] Studio 3

TO DEAL WITH CONTRADICTORY CORRELATION BETWEEN KEY DESIGN TARGETS

3.1

INTENTION OF DESIGN TOOL

The Feedback And Inspiration of Tool Development From The Conversation with Northern Gateway Developer and MMC Matt Doran :

THE FURTHER DEVELOPMENT OF THE TOOL SHOULD ALSO FOCUS ON HOW TO MAKE THE OUTCOME MORE EASIER TO UNDERSTAND VISUALLY

FEEDBACK AND VOICE FROM INDUSTRY

Multiple Data Readout for further Investigation

Matt Doran :

DOSE YOUR METHOD ONLY PROVIDE THE OPTIMIZED 'ZERO CARBON' OPTION, OR IT ALSO ALLOW OTHER CRITICAL METRICS TO BE SET AS THE KEY TARGET UNDER THE 'ZERO CARBON' AMBITIONS ?

2021 CPU[Ai] Studio 3

Other crucial metrics of planning should also be consider as part of your design target. It will be ideal if you can allow user to decide which metric is the primary target to archive. Meanwhile the result can be compared with other iterations for further investigation.

Architectural Visualization at Urban & Block Level

IT IS VITIAL TO THINK HOW YOUR METHOD/ RESULT GUIDES/ BENEFITS OTHER PRACTITIONERS WITHIN THE PROJECT? We had a chance to communicate with the actual planner and developer of the Northern Gate regeneration project before we step into to the final tool build up.

Matt Doran (from Manchester City Council) and Tom Fenton (FEC) did provide us with vital feedback on previous studies. More important, their voice did inspired us in shaping our tool functions.

Tom Fenton : The computational method is efficient to conduct different iterations of planning/design proposal. It will be more useful if this approach can provide relevant data comparison or any initial design guidance for users and other practitioners. Especially in this ‘Zero Carbon’topic, the specific data readout can help users set the baseline for further design work.

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Interactive Design Target set

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The ‘Zero Carbon’ is any relatively new but urgent topic for our community, therefore the result of this study should be more easier to understand not only for professions but also for local residents.

3.2

DESIGN TOOL FUNCTION

Proposed Function That Identifies Both By Design Challenge And Industry Practitioners Feedback

MULTIPLE PARAMETER AND VARIABLES TO CONTROL AND OPTIMIZE

DEMANDS FOR MULTIPLE RESULT SIMULATION AND & EVALUATION

INTERACTIVE VISUALIZATION OF OPTIMAL RESULT

GENERATE ZERO CARBON URBAN FORM IN MULTIPLE SCALES

2021 CPU[Ai] Studio 3

The tool aims to be applied into both at urban level and block level to generate multiple iterations by following ‘Low carbon urban form’ Principles

GENERATIVE RESULTS COMPARISON AND EVALUATION Enable users to select their focused design target and metrics under the zero-carbon future design target.

Meanwhile, to generate the data benchmark for further investigation.

By implementing previous studies and reflecting on practitioners feedback, we set three main functions of the tool.

The multiple application scale allows different professions to use the tool. The evaluation of design results could be more specific, which is benefited by the generative design process. The interactive visualization provides both quantitative and qualitative investigation for whole level users.

FUTURE SCENARIO PREDICTION & HYBRID TYPOLOGY GENERATION Based on user-selected iteration, the tool initially predicted urban growth scenarios, which is mainly based on the demographic increase. Moreover, the hybrid building typology can also be generated as the further optimized version for solar potential enhancement and future change adaptation.

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Provide an interactive experience both in data analysis and architectural visualization. It will allow not only professional but also general users to explore how the ‘towards Zero Carbon‘ design impact urban, block and building .

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CONTRADICTORY CORRELATION BETWEEN KEY DESIGN TARGETS

DESIGN TOOL TARGET USER

Target Users Related To the Zero-carbon Future Design Challenge The tool is mainly intended to help urban planner and architects generate and investigate the urban form towards the zero-carbon future city and provides multiple iterations to meet secondary design requirement under the zero-carbon challenge.

GENERATE ZERO CARBON URBAN FORM IN MULTIPLE SCALES

Local Resident Since the ‘Zero Carbon Future‘ being a new but urgent topic, the resident can use the tool to better understand how this trend will impact their community and how the building will be.

The tool also provides interactive fly through at urban level and block level to allow both professional and general user to easier understand the ‘Zero Carbon’ Urban form.

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Urban Designer The Urban Designer can utilize the tool to generate Low carbon urban form massing as the starting point. According to the data readout of iterations, they can also select their optimal options (with more than one criteria).

GENERATIVE RESULTS COMPARISON AND EVALUATION

FUTURE SCENARIO PREDICTION & HYBRID TYPOLOGY GENERATION

Student

Architect Besides getting the initial urban scale guidance on design, the architect also can explore the single building’s solar optimized geometry and basic readout regarding the solar panel, energy demand, GFA etc.

Students can utilized the tool to set the initial massing of relevant design projects.

INTERACTIVE VISUALIZATION OF OPTIMAL RESULT

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3.3

3.4

STAGE 1

SPATIAL STRATEGY BREAKDOWN

An Overview Of The Spatial Strategy For Computational Process

SITE DATA INPUT & REFINE

STAGE 2

STAGE 3

WALKABLE GREEN AMENITIES GENERATION

STAGE 10

SOLAR PANEL SYSTEM

STAGE 5

URBAN PATTERN GENERATION

BLOCK LEVEL GREEN AMENITY CONSOLIDATION

STAGE 9

STAGE 6

LAND-USE PROGRAM ALLOCATION

DECENTRALIZED LAND-USE SPATIAL STRATEGY

STAGE 8

STAGE 7

ELECTRICITY STORAGE

HYBRID TYPOLOGY GENERATION & SCENARIO ADAPTATION

BUILDING FORM OPTIMIZATION

PLOT LAYOUT & BUILDING POSITION OPTIMIZATION

27

PARCELLATION & PLOT TYPOLOGY

Demonstrate The Logic And Sequence Of Computational Method. Meanwhile To Show The Key Variables That Can Mainly Impact The Result Of The Process

STUDIO 3 × CPU[AI] 2021 Start STAGE 1 Evaluation Feedback loop

Reference the site boundary

Existing elements to retain

STAGE 1: The primary street system, transportation hub, site entrance and super-block are generated after inputting the basic site condition accordingly by the designer. Within this stage, all of these elements will be referenced as the entire process’s start points.

New subdivision of super block

Feedback loop 3

Hybrid Typology Application STAGE 8

Residential minimal requirement and target & Population target STAGE 2

Walkable Green Amenities Generation

Run the Generation

Evaluation of influence area coverage if STAGE 3

Ideal dimension & location

Ratio>=85%

if

Run the Optimization Variables set 7.1: Numbers of floor unit area to be Omitted

Variables set 1: 1. Existing Greenspace influence range.

2. New Green Amenities dimension & Corresponding influence range.

3. Variable dimensions

Increase the facade area for solar irradiation

Variables set 7.2: Floor numbers for solar accessibility Step the building massing for Solar accessibility

Optimization of building form

Selected model for testing

Selected model for testing

Selected 3D model

Ratio<85%

STAGE 7

Feedback loop 2

Urban Pattern Generation (Superblock subdivision) Variables set 2: 1. Super-block boundaries 2. Number of entrances of the super block 3. Subdivided plot width & length 4. Main orientation of grid

Evaluation based on performance Metric & Critical Critical set 1: 1. Energy-related spatial aspects 2. Annual Solar irradiation of facade 3. Metrics of population, green amenity and landuse 4. Electricity Consumption & Generation

Optimised Building Massing for Testing

Run the Generation STAGE 6

Block dimension & geometry

Run the Optimization

New Green amenity & patch consolidation

STAGE 7: The optimized masterplan massing model will be evaluated by set Criteria regarding in-use energy, solar irradiation and metrics of green amenity per-person. Meanwhile, it will send the feedback to variables sets of previous stages to generate the next iteration of the massing model

STAGE 10: The optimized model will be evaluated by set Criteria regarding in-use energy, electricity generation solar irradiation and metrics of green amenity per person again to demonstrate each iterations’ final performance.

END & Export the Finalized 3D model

Run the Generation

Key transportation hub and points

STAGE 3: According to the urban block dimension studies for our design aims, the selected width/length (64-128m) of the block is applied to generate the appropriate urban pattern within each super/mega blocks.

Meanwhile, the generated green patch information will be consolidated into the new urban pattern.

STAGE 8 & 9: By receiving the selected model from the previous stage, the building massing form will be further optimized to increase the solar irradiation and enhance solar accessibility. Moreover, the hybrid typology strategy (Modular + solar panel+Energy hub) will be applied to optimized massing to enhance on-site electricity generation and adoption to the future demographic scenario

STAGE 9

Evaluation based on performance Metric & Criteria Critical set 2: 1. Annual Solar irradiation of facade 2. Metrics of population, green amenity and land-use 3. Electricity Consumption & Generation

Key transport routes

STAGE 2: In this stage, the main agenda is to maximise green amenities’ walk-ability by generating the ideal size and location of new green space/ patches correspondingly with existing greenspace. it can help reduce the energy usage of people’s transportation

STAGE 5 & 6: The building typologies will be assigned into blocks according to the land-use allocation. Within each defined zone, the allocation factor will help in generating multiple iterations even under the same land-use ratio. Moreover, each building massing within plots can also be optimised by changing the position factor & Hight factor.

Feedback loop 3

Reference the pre-set (manually controlled) site elements

Main Procedure

STAGE 4: The classification of different program zones will be determined according to the transportation hub influence range. (200m and 400m).

Within each zone, the land-use will be defined differently to achieve the most efficient mixed-use arrangement. The ration between each type of land-use can be adjusted.

STAGE 10

Input Data From

Feedback loop 1

3.5

DESIGN TOOL PSEUDO CODE

Variables set 6: 1. Location factor (variation factor)

Variables set 3: 1. Orientation of consolidated green patch (North, South, West, East)

if Plot coverage<80% Run the Generation

Different types of land-use zone

Optimization of building position within plot

Buildable Block geometry

STAGE 4

Initial 3D massing for optimization

Land-use spatial arrangement strategy

STAGE 5

Run the Generation

Run the Generation

Variables set 5: 1. Building density (further subdivision of plot) 2. Height constraints of building massing 3. Building orientation

Land-use Allocation Variables set 4: 1. Single use / mixed use cluster 2. Ratio of different land-use programs

if Plot coverage>=80%

Plot boundary land-use program arrangement Greenspace

Assign building typologies

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3.6

INTERFACE PREVIEW

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2021 CPU[Ai] Studio 3

The Interface Of Design Tool That Aims To Be Easier To Use And Provide Interactive Experience To Explore The Design Outcome With Corresponding Data Readout

29

Chapter 3

STAGE 1

CONCLUSION

Start Reference the site boundary Reference the pre-set (manually controlled) site elements

STAGE 2

Walkable Green Amenities Generation Evaluation of influence area coverage

STAGE 3

CHALLENGE 6

Green Amenity Distribution

DESIGN PROBLEM

Green Amenity walkability demand

LOW CARBON URBAN FORM & SOLAR OPTIMIZED HYBRID BUILDING TYPOLOGY

How to design zero-carbon future cities (is urban morphology adequate). How do you understand the environmental impact of future cities.

Ideal and efficient green amenity distribution can significantly increase the walkability, which also provide opportunity to reduce the co2 emission

STAGE 4

Ideal dimension & location

If

Ratio>=85%

If

Ratio<85%

Urban Pattern Generation (Super-block parcellation) Block dimension & geometry

New Green amenity & patch consolidation

STAGE 5

Buildable Block geometry

land-use spatial Arrangement strategy

FEEDBACK LOOP 2

Different types of land-use strategies

Block level land-use Allocation

STAGE 6

Compact Morphology The compactness of urban form has the potential in impacting the building energy usage and walkability of green amenities.

Meanwhile, it provides the solution to demographic increasing

Energy Demand & Efficiency

-Plot boundary -land-use program arrangement -Greenspace

Assign building typologies

STAGE 7

Initial 3D massing for optimization

STAGE 7

if Plot coverage <80%

if Plot coverage>=80%

Optimization of building Position within plot

STAGE 8

To enhance building surface's solar irradiance potential can contribute to renewable power generation, which acts as the vital aspect towards to zero-carbon future

Optimised Building Massing for Testing

Selected iteration for next optimization

Optimization of building form Optimised Building Massing for Testing

Solar Potential Low Density Urban Pattern

Feedback loop 1

At this stage, we mainly finished up the research and experiment of the generative design tool. The coding logic and workflow had been formulated step by step with implementing low carbon urban form relevant research and proposed spatial strategy. By doing so, the design tool can target to generate more convincingly outcome to respond to the selected challenge. The following chapters will demonstrate the detail of the tool working mechanism, tool’s result analysis and interface manual

STAGE 1

STAGE 9

Hybrid building Typology Generation

Evaluation based on performance Metric & Criteria

FEEDBACK LOOP 3

Evaluation based on performance Metric & Criteria STAGE 10

END & Export the Finalized 3D model

30

Selected iteration to export

2021 CPU[Ai] Studio 3

Manchester school of architecture

THE GATE WAY TO ZERO CARBON CITY

Chapter 4.1

4.1.0 WORKFLOW RELATES TO URBAN LEVEL GENERATION 4.1.1 STEP 1: SITE INFO INPUT & REFINE MECHANISM

TOOL MECHANISM URBAN & BLOCK LEVEL

4.1.2 STEP 2: GREEN AMENITY SIMULATION MECHANISM 4.1.3 STEP 3: URBAN PATTERN GENERATION & GREEN AMENITY CONSOLIDATION MECHANISM 4.1.4 STEP 4: LAND-USE DEFINING & PROGRAM ALLOCATION MECHANISM 4.1.5 STEP 5: BUILDING TYPOLOGY ASSIGNING MECHANISM

This chapter demonstrate the key mechanism behind the urban level urban form generation. The main logic, parameters and method will be introduced by steps.

4.1.6 STEP 6: BUILDING POSITION & HEIGHT OPTIMIZATION MECHANISM 4.1.7 STEP 7: BUILDING SOLAR OPTIMIZATION MECHANISM

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2021 CPU[Ai] Studio 3

URBAN & BLOCK LEVEL

STEP 1

STEP 2

STEP 3

INPUT SITE INFO AND REFINE

RUN GREEN AMENITY DISTRIBUTION SIMULATION

RUN URBAN PATTERN GENERATION

STEP 5

STEP 6

STEP 7

ASSIGN INITIAL BUILDING TYPOLOGY

INITIAL MASSING POSITION & HIGHT OPTIMIZATION

ITERATION SELECTION AND DATA INVESTIGATION

STEP 4

CONSOLIDATE AMENITY INTO URBAN PATTERN

GENERATE LANDUSE SPATIAL STRATEGY

PROGRAM ALLOCATION 2021 CPU[Ai] Studio 3

4.1.0

WORKFLOW RELATES TO URBAN LEVEL GENERATION

STEP 8 BUILDING MASSING SOLAR OPTIMIZATION

32

STEP 9

STEP 10

HYBRID TYPOLOGY GENERATION

ITERATION SELECTION AND DATA INVESTIGATION

4.1.1

STUDIO 3 × CPU[AI] 2021

STEP1 MECHANISM

Input parameter, Computational Logic, Relevant Research and Key Result of Step 1

STEP 1

INPUT BASIC SITE INFORMATION

INPUT THE SITE INFO & REFINE

Positive effect Negative effect

Low Carbon Urban Form

As the research showing, transportation is one of the key elements to realize the low carbon urban form. It includes two aspects of traffic and public transportation. In terms of traffic, our approach is mainly to improve the efficiency of bicycles and sidewalks so that people can get around comfortably without using cars. The second point is to define bus lanes to improve bus usage efficiency and reduce car usage.

Land-Use

Transportation

Walkability

Green Amenity

Traffic

Public Transportation

Cycling

Connectivity

Morphological Compactness (Relate to Building in-use Energy)

Ability to Transfer

Covered Area

In-use energy of transportation

Car usage

Multi-modal Street

Essential Street Network

Solar Potential

Principle of Street Network dimension

Bus lane

Bicycle paths

High connectivity

High walkbility

A separate bus lane improves the efficiency of buses and ensures punctuality.

Bicycle paths increase the possibility of cycling, and improve the efficiency and safety of cycling.

Clear intersections reduce confusion between different types of transport and improve the connectivity of public transport and bicycle lane.

Widening the sidewalk can accommodate more pedestrians and improve people’s walking comfort.

3.5m

7m

2m

3m 6m 20m

1.5m

User can input basic existing site condition or manually adjusted site info based on their design target

3m

1.5m

2m

3.5m

7m

SECONDARY ROAD - BOULEVARD

3.5m

5.5m

2m

3m 9.5m 20.5m

3.5m

3m

2m

5.5m

3.5m

SECONDARY ROAD

3.5m

SIDEWALK

BICYCLE PATH

ROADWAY

BUS LANE

6.5m

2m

1m

3.5m

3m

13m 26m

3m

3.5m

1m

2m 6.5m

3.5m

MAIN ROAD

CO2 EMISSION

Source: Global Designing Cities Initiative, Damian Holmes, Global Street Design Guide

33

4.1.1

STUDIO 3 × CPU[AI] 2021

STEP1 MECHANISM

Input parameter, Computational Logic, Relevant Research and Key Result of Step 1

STEP 1

INPUT BASIC SITE INFORMATION

INPUT THE SITE INFO & REFINE

Combined with our research in Studio1, we believe that in order to solve the problem of transportation from the public level, we need to achieve three goals: C o n n e c t i v i t y, A b i l i t y to Transfer and Cover Area. Therefore, we hope to combine TOD and P+R modes to optimize the public transport system.

Positive effect Negative effect

Low Carbon Urban Form Land-Use

Walkability

Transportation

Green Space

Traffic

Public Transportation

Cycling

Connectivity

Solar Potential

Morphological Compactness (Relate to Building in-use Energy)

Ability to Transfer

Covered Area

Car usage

In-use energy of transportation

Transit Oriented Development Influence Zone 400m

Transit Supportive Area Transit neighbourhood

400m Transportation Hub

User can input basic existing site condition or manually adjusted site info based on their design target

P+R Transportation Hub Principle

P+R Transportation Hub Principle 800m Transit Supportive Area

Essential public Transportation hub position and network

400m

Transit neighbourhood

Bicycle parking

Generated Initial Landuse strategy

Relying on the Transportation Hub, Establishing a mixed area with functions of office, business and residence.

Service

3

Commerce

13

Office

10

Residence

64

Mixed-Use Residential Area

Transportation Hub

Car parking lot

P+R Transportation Hub 800m Transit Supportive Area

400m

Source: 1. National Transit Oriented Development (TOD) Policy 2. Neufert, E., Neufert, P. and Kister, J. (2012) Architects’ data. 4th ed, Chichester, West Sussex, UK ; Ames, Iowa: Wiley-Blackwell 3. "Park and ride - politics, policy and planning". Town and Country Planning Association. March 2010. Archived from the original on 2016-03-04. Retrieved 2012-01-19.

4. Trujillo, D. F. (2016) ‘Pursuing Sustainable Transport through Spatial Planning: a case study of the Stuttgart Region.’ Unpublished.

Bicycle parking and car parking have been added to provide more convenient ways for people to transfer.

Transit neighbourhood

The various service facilities within Transit neighbourhood can provide support for the pure residential area in the transit supportive area.

Pure residential area

Office Commerce

Transportation Hub

Public Service

Pure Office Area

34

Mixed-Use Commercial Area

Retail Green Space/Public Space Residence

Mixed-Use Office Area

4.1.2

STUDIO 3 × CPU[AI] 2021

STEP2 MECHANISM

Key Distribution & Dimension Studies of Urban Green Amenities and Patches

STEP 2

INPUT BASIC SITE INFORMATION

RUN GREEN AMENITY DISTRIBUTION SIMULATION

Based on the relevant papers on the study of heat island effect, we summarized the scope of the impact of green space on heat island effect. Within the range of 50-60 meters from the green space, they are all areas that can be affected by a single piece of green space.

The service scope of a single piece of green space will affect the way people choose to travel when they reach a certain piece of green space. This will also affect the construction of Zero Carbon City to some extent. When

Positive effect Negative effect

Low Carbon Urban Form Land-Use

Transportation

Accessibility

Green Space

Morphological Compactness (Relate to Building in-use Energy)

Diversity

Distribution

Car usage the area is 1 hectare or larger, the service radius of green space is about 800 to 1000 meters, while when the area of green space is 2500 square meters, the service radius of green space is about 300-500 meters. When the green area drops to around 400 square meters, the impact will be only 100200 meters.

Solar Potential

Heat island effect

Idea Distribution mode

Distribution with Multiple size & High walkability

Car Usage

Heat Island Effect

CO2 EMISSION

Based on the research of Green Amenity dimension and impact scope, the tool will utilize the circle packing to simulate the ideal distribution of new green amenity that can guarantee 10 mins walking distance and maximise the reduction on urban heat island effect

Defining Green Space Size

M

M

10

0M

20

50

130M

800-1000M 100M

10

0M

300-500M

50

M

50M

20

Site Existing Green Space

100-200M

M

Service Area

Cooling Effect of Green Space

Service Area

Service Area

Urban level green space

Cooling Effect of Green Space

Block level green space

Source: 1. Strategic Regeneration Framework MANCHESTER NORTHERN GATEWAY 2. Bousmaha Baiche, Nicholas Walliman, Architects’ Data 3. Statista, Average number of adults per household in the United Kingdom (UK) in 2018-2019

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Service Area

Service Area

Cooling Effect of Green Space

Service Area

Neighbourhood green space

4.1.2

STUDIO 3 × CPU[AI] 2021

STEP2 MECHANISM

The Application of Circle Packing in New Green Amenity distribution to enhance the walkability

STEP 2

INPUT BASIC SITE INFORMATION

RUN GREEN AMENITY DISTRIBUTION SIMULATION

Based on the research of Green Amenity dimension and impact scope, the tool will utilize the circle packing to simulate the ideal distribution of new green amenity that can guarantee 10 mins walking distance and maximise the reduction on urban heat island effect Please check out the generation process video: https://youtu.be/ dgC_cWkS8N0

A circle packing is an arrangement of circles inside a given boundary such that no two overlap and some (or all) of them are mutually tangent. A circle packing is an arrangement of circles inside a given boundary such that no two overlap and some (or all) of them are mutually tangent. The generalization to spheres is called a sphere packing. Tessellations of regular polygons correspond to particular circle packings. There is a well-developed theory of circle packing in the context of discrete conformal mapping.

Idea Distribution mode

In this theory, we can regard the circle as the influence area of our green space to reduce the heat island effect, and the centre of the circle is the location of the green space.

Circle Packing Simulation

Site Existing Green Space

Defining existing Green Area

Step 1: Define the boundary, Set random points

Finding uncovered areas

Step 2: Simulate the movement between points to find the equilibrium point

Set random green space locations

Source: 1. Williams, R. “Circle Packings, Plane Tessellations, and Networks.” §2.3 in The Geometrical Foundation of Natural Structure: A Source Book of Design. New York: Dover, pp. 34-47, 1979.

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Step 3: Present the area of green space according to the present green space size

The simulation of the balance of green space location

4.1.3

STUDIO 3 × CPU[AI] 2021

STEP3 MECHANISM

Key Dimension Researches, Circle Methods, Adjustable Variables of Urban Pattern Generation Land-use

STEP 3

Positive effect Negative effect

Low Carbon Urban Form Transportation

INPUT DATA FROM PREVIOUS STEP Walkability

RUN URBAN PATTERN GENERATION

Morphological Solar Potential Compactness (Relate to Building in-use Energy) Public Transportation

Traffic

Cycling

Green Space

Connectivity

Car usage

Ability to Transfer

Covered Area

In-use energy of transportation

Measure the length of the sides of the super block and define the internal subjective road factors explicitly.

The initial road structure is obtained by shifting the corresponding distance.

Add control points to prepare for move

Set the radius of the circle according to the length and width of the block size.

Simulate according to the method of Circle Packing.

Determine the new road grid after the simulation.

Export the generated road, and determine the intersection.

Use the same method to generate the mesh in the other direction.

Combine the pre-set road with the generated road to output the super block’s road network structure and block

SUPER BLOCK

Optimized Main Street

Ne

igh

orti

bo

ve

Are

a

rh oo

d

it C

or

e

Transportation Hub

BLOCK SIZE - SINGLE FUNCTION AREA

32 - 64M

200M 400

M

64 - 176M

Single Function Block 0M

80

64

32

64 - 192M

Length: 176

64

BLOCK SIZE - MIXED-USE AREA

DEFINING BLOCK SIZE

64 - 192M

Mixed-used Block Width:

64-128M

Width:

64-128M

Please check out the generation process video: https://youtu. be/5LXD-oa-sVM

Transit

T rans

64 - 176M

A c c o r d i n g t o t h e r esearch of Andres Sevtsuk, the walkability of a city is not linearly correlated with the size of the block. Therefore, the tool aims to generate the urban pattern in multiple dimension to find the optimal urban pattern to archive the low carbon urban form

Transit Su pp

32 - 64M

KEY PARAMETERS CONTROLLED BY TOOL

Our Super block range will be defined between the main road and the secondary road. These super blocks will be further divided into sub-blcoks based on the functionality we define and will follow the size we define.

64

128

64

192

Length:

Source: 1.Andres Sevtsuk, Raul Kalvo and Onur Ekmekci, Pedestrian accessibility in grid layouts: the role of block, plot and street dimensions

Two types of block dimension range were decide to meet the requirement of mixed-sue block and single function block. Block selection of two scales not only meets the high efficiency, but also meets the needs of different functional buildings on the site.

KEY VARIABLES ADJUSTMENT Depending on the Super Block’s functionality, The Tool set different parameter ranges to generate multiple iterations for further comparison

BLOCK FUNCTION RULES The Block Function will be determined by the distance from block location to the nearest transportation hub

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4.1.3

STUDIO 3 × CPU[AI] 2021

STEP3 MECHANISM

To Demonstrate The Logic, Method And Adjustable Variables Of Green Amenity Consolidation

STEP 3

INPUT DATA FROM PREVIOUS STEP

Zoom in a super block.

At the urban level, The tool has simulated ideal distribution of green spaces to meet the needs of citizens and increase the walkability. At the block level where the road is divided, the tool needs to consolidate the green amenity to actual urban pattern based on the its location After the ideal position and area are determined, we can change the orientation of the green patch to further affect the energy consumption performance of the city. When the orientation of the green space changes, it will have a direct impact on the surrounding architectural form and the overall layout of the site.

CONSOLIDATE AMENITY INTO URBAN PATTERN

Consolidate the green space according to ideal position and area.

Generated Urban Pattern

Get the ideal green space in block level.

Based on the generated urban pattern and simulated green amenity location, the tool will consolidate the green amenity into actually plot. The adjustable orientation parameter allows tool to generate multiple iterations

Simulated Green Amenity Location KEY PARAMETERS CONTROLLED BY TOOL

Iteration Samples

Simulated New Green Amenity Location

Key Steps

Green Amenity On-site Orientation: Please check out the generation process video: https://youtu.be/97ubiDRWQk

North

South

East

West

North-south Iteration 1

North-south iteration 2

East-West iteration 1

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East-West iteration 2

Mixed iteration 1

4.1.4

STUDIO 3 × CPU[AI] 2021

STEP4 MECHANISM

To Demonstrate The Logic And Rules Of Defining The Land Use Spatial Arrangement Strategy

STEP 4

INPUT DATA FROM PREVIOUS STEP

GENERATE LAND USE SPATIAL STRATEGY

According to the previously defined land use distribution in master plan level, the more detailed spatial arrangement strategy will be generated according to the main transportation hub influence range. Within 200m range, the arrangement Type 1 mainly includes commercial, office and public programs. The Type 2, located between 200m-400m range, will be allocated with programs of commercial, office , public and residential. Differently, the Type 3 will only have commercial and residential programs. The exact ratio between different programs within same type is adjustable based on different scenario.

Land-Use

Single Function

Transportation

Mixed-Use

Green Space

Walkability

Connectivity

HIGH RATIO

OFFICE PROGRAM

LOW RATIO

MEDIUM RATIO

PUBLIC SERVICE PROGRAM

COMMERCIAL PROGRAM

MEDIUM RATIO

LOW RATIO

MEDIUM RATIO

OFFICE PROGRAM

PUBLIC SERVICE PROGRAM

RESIDENTIAL PROGRAM

TYPE 2

Rules Setting:

Transportation hub position and Influence Scope

Covered Area In-use energy of transportation

TYPE 1

The design tool will automaticly define the landuse spatial arrangement according to the urban pattern information and transportation hub position

Land Use Efficiency

Car usage

HIGH RATIO

Solar Potential

Morphological Compactness (Relate to Building in-use Energy)

Public Transportation

Traffic

COMMERCIAL PROGRAM

Urban Pattern with New Green Amenity

Positive effect Negative effect

Low Carbon Urban Form

more than 400m

within 400m

within 200m

Source: 1. Purwantiasning, A. W. (2017) ‘Understanding the Concept of Transit Oriented Development Through Proposed Project of Manggarai, Jakarta Selatan, Indonesia’ p. 12

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LOW RATIO

COMMERCIAL PROGRAM

TYPE 3

HIGH RATIO

RESIDENTIAL PROGRAM

4.1.4

STUDIO 3 × CPU[AI] 2021

STEP4 MECHANISM

To Demonstrate The Logic, Approach, Key Variables of Program Allocation within Different Land-use Spatial Strategy Types

STEP 4

TYPE 1

INPUT DATA FROM PREVIOUS STEP

PROGRAM ALLOCATION

Defined Land-use Spatial Strategy & Urban Pattern

70%

20%

10%

60%

30%

10%

50%

40%

10%

40%

50%

10%

Commercial

Office

Public Service

Commercial

Office

Public Service

Commercial

Office

Public Service

Commercial

Office

Public Service

TYPE 2

KEY PARAMETERS CONTROLLED BY TOOL & USER

According to the landuse Spatial arrangement strategy, the tool will allocate 4 different programs to the corresponding zone types.

The tool tends to achieve the diversity program mix and decentralized allocation by iteratively adjusting the program ratio factor and location factors.

Commercial Ratio

10%

55%

70%

Commercial Office

Office Ratio 20%

25%

10%

10%

Public Service Residential

45%

35%

Commercial Office

10%

10%

Public Service Residential

35%

45%

Commercial Office

10%

Public Service Residential

45%

TYPE 3 Public Service Ratio 5%

10%

Residential Ratio 10%

90%

Location Seed Mixed Distribution

Isolated Distribution

10%

90%

20%

80%

30%

70%

Commercial

Residential

Commercial

Residential

Commercial

Residential

Source: 1. HTTPS://WWW.IPSWICH.GOV.UK/SITES/DEFAULT/FILES/NCD37_-_URBAN_DESIGN_COMPENDIUM_1_-_ URBAN_DESIGN_PRINCIPLES.PDF

40

10%

35%

35%

Commercial Office

10%

20%

Public Service Residential

4.1.4

STUDIO 3 × CPU[AI] 2021

STEP4 MECHANISM

To Demonstrate The Logic, Approach, Key Variables of Program Allocation within Different Land-use Spatial Strategy Types

STEP 4

TYPE 1

Public Service

INPUT DATA FROM PREVIOUS STEP

10%

Office

PROGRAM ALLOCATION

Commercial

Defined Land-use Spatial Strategy & Urban Pattern

Adjust location Factor to generate different iterations

Iteration 1: Office program aggregation (a)

Iteration 2: Office program aggregation (b)

Iteration 3: Office program aggregation (c)

Iteration 4: Commercial program aggregation (a)

Iteration 5: Commercial program aggregation (b)

Iteration 6: Mixed distribution

Iteration 1: Program separate-aggregation

Iteration 2: Office Linear distribution

Iteration 3: Commercial Linear distribution

Iteration 4: Commercial program aggregation (a)

Iteration 5: Residential centralized distribution

Iteration 6: Mixed distribution

Iteration 1: Aggregation distribution (a)

Iteration 2: Aggregation distribution (b)

Iteration 3: Linear Distribution (c)

Iteration 4: Balance Distribution

Iteration 5: Commercial focused distribution (a)

Iteration 6: commercial focused distribution (b)

40% 50%

TYPE 2

Residential

20%

KEY PARAMETERS CONTROLLED BY TOOL & USER

According to the landuse Spatial arrangement strategy, the tool will allocate 4 different programs to the corresponding zone types.

The tool tends to achieve the diversity program mix and decentralized allocation by iteratively adjusting the program ratio factor and location factors.

Public Service

10%

Office

Commercial Ratio

10%

70%

Commercial

Office Ratio 20%

35% 35%

45%

TYPE 3

Public Service Ratio 5%

Adjust location Factor to generate different iterations

10%

Residential Ratio 10%

90% Adjust location Factor to generate different iterations

Location Seed Mixed Distribution

Isolated Distribution

Residential Commercial

Source: 1. HTTPS://WWW.IPSWICH.GOV.UK/SITES/DEFAULT/FILES/NCD37_-_URBAN_DESIGN_COMPENDIUM_1_-_ URBAN_DESIGN_PRINCIPLES.PDF

70% 30%

41

4.1.5

STUDIO 3 × CPU[AI] 2021

STEP5 MECHANISM

The Essential Rules To Assign Initial Building Typologies To Program Arrangement Land_Use

STEP 5

Positive effect Negative effect

Low Carbon Urban Form Transportation

INPUT DATA FROM PREVIOUS STEP

Building Density

Green Space

FAR

PLot Coverage

ASSIGN INITIAL BUILDING TYPOLOGY

Solar Potential

Morphological Compactness (Relate to Building in-use Energy)

PLot Ratio

Horizontal Randomness

Building V/S Ratio

Aspect Ratio(H/w)

Reduce in-use Energy Demand

TYPE 1

TYPE 2

TYPE 3

Urban Plot with allocated Programs Transportation Hub

ESSENTIAL SEQUENCE FROM LAND-USE TO BUILDING TYPOLOGY

The allocated programs will be the key guidance for assigning the building typologies.

The design tool will evaluate each plot dimension to decide whether the typology can be assigned. It will also achieve multiple iterations by controlling the secondary parcellation of plots, building orientation, building footprint and hight.

50% COMMERCIAL

COMMERCIAL COMPLEX

Source: 1. CPU_BUILDING TYPOLOGY BOOKLET. LINK: HTTPS://DRIVE.GOOGLE.COM/DRIVE/FOLDERS/1QASEXXF493PS6NIMP4RM1FAJAQOB1PA8

MID-RISE COMMERCIAL

40% OFFICE

HIGH-RISE OFFICE 1

10%

35%

PUBLIC SERVICE

HIGH RISE OFFICE 2 (MIX WITH RETAIL)

PUBLIC SERVICE

COMMERCIAL

MID-RISE COMMERCIAL

35% OFFICE

CO-WORKING OFFICE + RETAIL

200 m from transportation hub

10%

20%

PUBLIC SERVICE RESIDENTIAL

PUBLIC SERVICE MID-RISE RESIDENTIAL

HIGH-RISE RESIDENTIAL

30% COMMERCIAL

MID-RISE COMMERCIAL

400 m from transportation hub 42

70% RESIDENTIAL

MID-RISE RESIDENTIAL

HIGH-RISE RESIDENTIAL

4.1.5

STUDIO 3 × CPU[AI] 2021

STEP5 MECHANISM

Tool Working Logic, Sequence and Key Parameters of Assigning Typology

STEP 5

STEP 5.1

STEP 5.2

STEP 5.3

INPUT DATA FROM PREVIOUS STEP

Specific typology dimension data

Urban Plot with allocated Programs

Determine the typology choice

A>B

A. Plot area B. Typology biggest footprint

Assign the building typology as Filled footprint

b. Typology Minimal depth

The building massing will be assigned with max footprint in the multiple position within plot

a≤b

Assigned as the open space No

Yes

Typology Hight (Various) Plot Boundary & Corresponding land use

Max

Min

Typology Width(Various) a≥b Max

Min

Typology length(Various)

Massing Orientation North

Evaluate

Yes

a. Plot shortest edge

South

East

West

The building massing will be assigned with max footprint to achieve the filled footprint

Ready for assigning typology

b. Typology Minimal depth

Max

Min Please check out the generation process video: https://youtu.be/ tniIB--CA1g

Other building typologies will be assigned to the plot

Directly assign typology

a≥b

a. Plot shortest edge

If the plot has central green patch

The different options of parcellation and building massing organization, which will determined both by the specific typology dimension and density requirement.

A≤B Evaluate

Plot Future Parcellation

The parcelled plot will be tested again with typology dimension before assigning the building massing

To decide whether need further parcellation

Evaluate

No

KEY PARAMETERS CONTROLLED BY TOOL

STEP 5.5

Further parcellation

ASSIGN INITIAL BUILDING TYPOLOGY

The design tool will evaluate each plot dimension to decide whether the typology can be assigned. It will also achieve multiple iterations by controlling the secondary parcellation of plots, building orientation, building footprint and height.

STEP 5.4

Parameter block typology will be assigned to the plot

a≤b

Assigned as the open space

The building massing will be adjusted according to the green-space accessibility requirement

Source: 1. CPU_BUILDING TYPOLOGY BOOKLET. LINK: HTTPS://DRIVE.GOOGLE.COM/DRIVE/FOLDERS/1QASEXXF493PS6NIMP4RM1FAJAQOB1PA8

43

Different options of building positioning within plot.

Although assigning the typology massing to plot without further subdivision parecllation provide reducing the density options, it provide more flexibility for building distance and H/W optimization

4.1.5

STUDIO 3 × CPU[AI] 2021

STEP5 MECHANISM

The Collection of Studies Typical Building Typology & Key Parameters to Control COMMERCIAL

COMMERCIAL

OFFICE

OFFICE

OFFICE

STEP 5 PARAMETRIZED TYPICAL TYPOLOGY

ASSIGN INITIAL BUILDING TYPOLOGY

60

COMMERCIAL COMPLEX

MID-RISE COMMERCIAL

OVERALL WIDTH (M)

OVERALL WIDTH (M)

180

85

33

OVERALL DEPTH (M)

25

For Achieving the generative typology assigning process, the tool will extract the typology information from preset parametrized typical building typology library and adopt it into different iterations.

OVERALL HEIGHT (M)

45

15

36

35

63

45

18

18

35

30

63

35

30

96

45

128

RESIDENTIAL

63

42 OVERALL DEPTH (M)

35

18

OVERALL HEIGHT (M)

96

45

45

OVERALL WIDTH (M)

OVERALL DEPTH (M)

OVERALL HEIGHT (M)

50

CO-WORKING OFFICE + RETAIL

OVERALL WIDTH (M)

OVERALL DEPTH (M)

PUBLIC SERVICE

24

35

45

128

45

12.5

RESIDENTIAL

RESIDENTIAL

63

OVERALL DEPTH (M)

30

35

PUBLIC SERVICE

OVERALL HEIGHT (M)

PARAMETER BLOCK

45

96

HTTPS://DRIVE.GOOGLE.COM/ DRIVE/FOLDERS/1QASEXXF493PS6NIMP4RM1FAJAQOB1PA8

Source:

HIGH-RISE RESIDENTIAL OVERALL WIDTH (M)

OVERALL WIDTH (M)

128

35

45

65

40

18

30

15

20

60

85

15

OVERALL DEPTH (M)

OVERALL DEPTH (M)

45

40

60

28

12.5

27

60

27.9

85

7

10

OVERALL DEPTH (M)

15

12

12.5

44

27

14.9

22

OVERALL HEIGHT (M)

OVERALL HEIGHT (M)

45

60

27.9

OVERALL DEPTH (M)

OVERALL HEIGHT (M)

OVERALL HEIGHT (M)

1. CPU_BUILDING TYPOLOGY BOOKLET. LINK: HTTPS://DRIVE.GOOGLE.COM/DRIVE/FOLDERS/1QASEXXF493PS6NIMP4RM1FAJAQOB1PA8

MID-RISE RESIDENTIAL

OVERALL WIDTH (M)

OVERALL WIDTH (M)

45

32

OVERALL HEIGHT (M)

OVERALL WIDTH (M)

18

Please check the CPU_Building typology Booklet for More Detail information

35

32

17.5

OVERALL HEIGHT (M)

36

63

45

HIGH-RISE OFFICE 2 (MIX WITH Retail)

OVERALL WIDTH (M)

OVERALL DEPTH (M)

75

20

HIGH-RISE OFFICE 1

18

93

100

4.1.6

STUDIO 3 × CPU[AI] 2021

STEP6 MECHANISM 1

Key Rules and Variables of Building Height Optimization. Targeting on Reducing urban Heat Island Effect and enhance the Facade Solar Potential Land_Use

STEP 6

Positive effect Negative effect

Low Carbon Urban Form

INPUT DATA FROM PREVIOUS STEP

Transportation

Building Density

FAR

Green Space

Plot Coverage

INITIAL MASSING POSITION & HIGHT OPTIMIZATION

Solar Potential

Morphological Compactness (Relate to Building in-use Energy)

Plot Ratio

Vertical Randomness

Reduce in-use Energy Demand

Horizontal Randomness

Aspect Ratio(H/w)

Increase Facade Solar Irradiation

RULE 1

RULE 2

Assigned Typology Massing

N The initial Massing model will be optimized regarding each building’s hight. Two rules are set by targeting on reducing the urban heat island effect and optimize the solar irradiation primarily

KEY PARAMETERS CONTROLLED BY TOOL Vertical Randomness Seed

Low

S Rule 1: From south to north, the height of each building’s typology will be optimized within its specified height limit. Buildings to the south will be lower, while buildings to the north will be higher.

Rule 2: The farther away the green space is, the taller the building will be to ensure that buildings far from the green space still have good views of the green space.

High

Typology Hight (Various)

Min

Max

Commercial

Commercial

Commercial

Office

Office

Office

Residential

Residential

Residential

Public Service

Please check out the generation process video: https://youtu.be/ i9zwJy16tzU

Samples Block Optimized by Rule 1

Samples Block Optimized by Rule 2

Source:

1. Cheng, V., Steamers, K., Montavon, M. and Compagnon, R. (2006) ‘Urban Form, Density and Solar Potential,’ January.

45

4.1.6

STUDIO 3 × CPU[AI] 2021

STEP6 MECHANISM 2

Demonstrate the Reasons and Rules of Building Position Optimization Within Plot To Minimizing The In-use Energy And Maximising The Solar Irradiation.

STEP 6

INPUT DATA FROM PREVIOUS STEP

As the generated result needs to meet multiple design targets which have potential contradictory correlations, the iterations should be evaluated and selected accordingly to conduct the optimal/balanced options for next stage.

Land_Use

Transportation

Building Density

Green Space

FAR

Plot Coverage

INITIAL MASSING POSITION & HIGHT OPTIMIZATION

Plot Ratio

Vertical Randomness

Reduce in-use Energy Demand

Model with Height Optimization Applied

Setback from Public

Define the movement Range The building massing is allowed to shift in multiple direction, which provide potential to adapte different demends acrroding to the design target

Horizontal Randomness Seed

Low

Horizontal Randomness

High

Aspect Ratio(H/w) (tend to 0.9 to 1.1)

Increase Facade Solar Irradiation

Setback from Residential

If the selected building massing plot coverage< 80%

KEY PARAMETERS CONTROLLED BY TOOL

Solar Potential

Morphological Compactness (Relate to Building in-use Energy)

Setback from office

The Tool will mainly control the building orientation and location factor within the plot to achieve the building position optimization—the step intends to optimise both in the Energy demands aspect and the Solar Irradiation aspect.

Positive effect Negative effect

Low Carbon Urban Form

Setback from commercial

Movement Constriants

Iteration 1

Iteration 2

Iteration 4

Iteration 5

Iteration 3

Generating Iterations

The optimization of building position will also considerate the constraints from surrounding context. The set-back from different buidling should be set as one of the rules.

Iterations will be generated for multiple rounds of evaluation, which can benefit in selecting most optimal positions to each building to achieve the design target in plots, block and urban level.

Location Seed The process will be undertake for every building massing on the site

0

100

Building Orientation North

South

East

Initial 3D Building Massing

Iteration Samples

West

Please check out the generation process video: https://youtu.be/ jj4Xkn4PNLc

If the selected building massing plot coverage≥80%

When the building's plot coverage over then 80%, its position will remain as the initial allocated, and will be import into next step of optimization with other massings

Plot Coverage: 32% Aspect Ratio (H/W): 0.48 Average Daily Solar Irradiation: 45131 kw/h

Source: 1. Mutani, G., Gamba, A. and Maio, S. (2016) ‘Space heating energy consumption and urban form. The case study of residential buildings in Turin (Italy) (SDEWES2016.0441).’ In. 2. All the diagrams drawn by author

46

Plot Coverage: 48% Aspect Ratio (H/W): 0.52 Average Daily Solar Irradiation: 45791 kw/h

Plot Coverage: 30% Aspect Ratio (H/W): 0.51 Average Daily Solar Irradiation: 45980 kw/h

Plot Coverage: 38% Aspect Ratio (H/W): 0.47 Average Daily Solar Irradiation: 46200 kw/h

4.1.7

STUDIO 3 × CPU[AI] 2021

STEP8 MECHANISM

Two Approaches of Building Form Optimization to enhance the facade solar irradiation and enhance the solar accessibility to surrounding buildings

STEP 8

INPUT DATA FROM PREVIOUS STEP

BUILDING MASSING SOLAR OPTIMIZATION

This method mainly focuses on increase the building’s neighbourhood sunlight accessibility by generating solar envelope and refine the new building form geometry. It will potentially trim the building volume that exceeds the solar envelope to gain the new shape.

Positive effect Negative effect

Low Carbon Urban Form Land_Use

Transportation

Green Space

Morphological Compactness (Relate to Building in-use Energy)

Horizontal Randomness

Vertical Randomness

Solar Facade Area

Solar Potential

Sunlight Accessibility to Surrounding building

Increase Facade Solar Irradiation According to the rules, identify the buildings that need to be optimized

Import the meteorological data of the building site and set the analysis time.

Define the buildings that need more exposure to sunlight

Model with Position Optimization Applied INPUT 1: DETERMINE THE BUILDING TO BE MODIFIED

The Tool will define the solar envelope of each building to increase sunlight from surrounding buildings. The pre-set cutting rules will reshape the building geometry.

INPUT 2: THE EFFECTED SURROUNDING BUILDINGS

Analyse the possibility of the sun hitting the building at different times.

KEY PARAMETERS CONTROLLED BY TOOL

INPUT 3: INSOLATION PARAMETERS

Solar envelope is generated to define the shape and height of the building.

Determine the parts of the building that exceed the limits of development.

Voxel Units to omit

0

200

THE ESSENTIAL LOGIC TO SOLAR ACCESSIBILITY TO SURROUNDINGS

DETERMINE THE SOLAR VECTOR

GENERATE SOLAR ENVELOPE

Floor numbers for solar accessibility

0

CONSTRICT THE OUTLINE OF THE BUILDING

Optimization Samples 13

Please check out the generation process video: https://youtu.be/ RjDrtApJCqg

Source: 1. Darmon, I. (n.d.) ‘Voxel computational morphogenesis in urban context : proposition and analysis of rules-based generative algorithms considering solar access’ p. 12.

2. Luca, F. D. (n.d.) ‘Buildings Massing and Layout Generation for Solar Access in Urban Environments’ p.

11.

3. Saleh, M. M. and Al-Hagla, K. S. (n.d.) ‘Parametric Urban Comfort Envelope’ p. 9.

4. Vartholomaios, A. (2015) ‘The residential solar block envelope: A method for enabling the development of compact urban blocks with high passive solar potential.’ Energy and Buildings, 99, July, pp. 303–312.

INPUT THE BUILDINGS TO BE CHANGED

GENERATE THE BUILDING INTO VOXEL MATRIX

ELIMINATE PART OF THE VOXEL BLOCK

47

OPTIMIZED OUTER CONTOUR

Buildings reduce floors and trimmed sections of buildings.

4.1.7

STUDIO 3 × CPU[AI] 2021

STEP8 MECHANISM

Two Approaches of Building Form Optimization to enhance the facade solar irradiation and enhance the solar accessibility to surrounding buildings

STEP 8

INPUT DATA FROM PREVIOUS STEP

BUILDING MASSING SOLAR OPTIMIZATION

This approach aims to enhance solar irradiation on the building facade as a key step of building form optimisations. This is an optimal solution to increase the solar potential when the massing and position have been set. Meanwhile, the impact on building’s FAR and layout should be carefully considered

Land_Use

Transportation

Green Space

Morphological Compactness (Relate to Building in-use Energy)

Horizontal Randomness

Solar Facade Area

Sunlight Accessibility to Surrounding building

Import the meteorological data of the building site and set the analysis time.

Model with Position Optimization Applied INPUT 1: DETERMINE THE BUILDING TO BE MODIFIED

INPUT 2: INSOLATION PARAMETERS

After pixelating the building, we can change the shape of the building by reducing the corresponding pixel blocks.

KEY PARAMETERS CONTROLLED BY TOOL

Vertical Randomness

Solar Potential

Increase Facade Solar Irradiation According to the rules, identify the buildings that need to be optimized

The tool will optimize the building massing form through the vector of the sun arriving at the building facade to obtain higher solar Irradiation on the facade. This optimization will progress simultaneously with previous one

Positive effect Negative effect

Low Carbon Urban Form

According to the rules we set, we will first identify the buildings we need to change. The process is manageable.

We need to analyse the effects of sunlight on the building throughout the year.

DETERMINE THE SOLAR VECTOR

We need to make sure that the form of the building is within the limits that we define.

Voxel Units to omit

0

200

THE ESSENTIAL LOGIC TO INCREASE FACADE SOLAR IRRADIATION

GENERATE THE BUILDING INTO VOXEL MATRIX

ELIMINATE PART OF THE VOXEL BLOCK

Floor numbers for solar accessibility

0

CONSTRICT THE OUTLINE OF THE BUILDING

Optimization Samples 13

Please check out the generation process video: https://youtu.be/_ BVACHVG_Ys

Source: 1. Darmon, I. (n.d.) ‘Voxel computational morphogenesis in urban context : proposition and analysis of rules-based generative algorithms considering solar access’ p. 12.

2. Luca, F. D. (n.d.) ‘Buildings Massing and Layout Generation for Solar Access in Urban Environments’ p.

11.

3. Saleh, M. M. and Al-Hagla, K. S. (n.d.) ‘Parametric Urban Comfort Envelope’ p. 9.

4. Vartholomaios, A. (2015) ‘The residential solar block envelope: A method for enabling the development of compact urban blocks with high passive solar potential.’ Energy and Buildings, 99, July, pp. 303–312.

INPUT THE BUILDINGS TO BE CHANGED

GENERATE THE BUILDING INTO VOXEL MATRIX

ELIMINATE PART OF THE VOXEL BLOCK

48

OPTIMIZED OUTER CONTOUR

Trimming buildings to get more sunlight.

Chapter 4.1

CONCLUSION 2021 CPU[Ai] Studio 3

The research-driven design solution and computational mechanism were fully investigated and implemented into the design tool development at each step of urban and block-level generation.

2021 CPU[Ai] Studio 3

By the end of step 8, the design tool has generated the optimized urban form at both urban and block levels.

The urban form and initial optimized building geometry will provide the basic geometry boundary for Hybrid typology generation at the building level.

AERIAL VIEW OF OPTIMIZED NORTHERN GATEWAY URBAN FORM

49

2021 CPU[Ai] Studio 3

Manchester school of architecture

4.2.1 OPTIMIZED URBAN MASSING RESULT 4.2.2 THE NEW CIRCULAR RESOURCE MODEL 4.2.3 RESEARCH ON MODULAR ARCHITECTURE 4.2.4 WHY WE DESIGN MODULAR ARCHITECTURE

THE GATE WAY TO ZERO CARBON CITY

Chapter 4.2

4.2.5 APPLICATION THEORY ON DESIGN PROBLEM 4.2.6 THEORETICAL FRAMEWORK

TOOL MECHANISM BUILDING LEVEL

4.2.7 THE RESILIENCE OF MODULAR BUILDING 4.2.8 MODULAR ARCHITECTURE CASE STUDY 4.2.9 PROGRAM MIXED USE 4.2.10 SOLAR PANEL RESEARCH 4.2.11 SOLAR FACADE PRINCIPLES&STRATEGY

This chapter demonstrate the key mechanism behind the hybrid building typology generation. The key research, generation logic, design strategy, and outcome will be introduced.

4.2.12 SOLAR ENERGY BUILDING CASE 4.2.13 ENERGY STORAGE RESEARCH 4.2.14 HYBRID BUILDING GENERATION 4.2.15 ENERGY TOOL WORKING PROCESS 4.2.16 HYBRID TYPOLOGY 4.2.17 STUDIO3 BRIEF\CONCEPT

50

2021 CPU[Ai] Studio 3

STEP 1

STEP 2

INPUT SITE INFO AND REFINE

RUN GREEN AMENITY DISTRIBUTION SIMULATION

STEP 3 RUN URBAN PATTERN GENERATION

STEP 4

CONSOLIDATE AMENITY INTO URBAN PATTERN

GENERATE LANDUSE SPATIAL STRATEGY

PROGRAM ALLOCATION 2021 CPU[Ai] Studio 3

4.2.0

WORKFLOW RELATES TO BUILDING LEVEL GENERATION

BUILDING LEVEL STEP 5

STEP 6

STEP 7

ASSIGN INITIAL BUILDING TYPOLOGY

INITIAL MASSING POSITION & HIGHT OPTIMIZATION

ITERATION SELECTION AND DATA INVESTIGATION

STEP 8 BUILDING MASSING SOLAR OPTIMIZATION

51

STEP 9

STEP 10

HYBRID TYPOLOGY GENERATION

ITERATION SELECTION AND DATA INVESTIGATION

4.2.1

OPTIMIZED URBAN FORM RESULT

STUDIO 3 × CPU[AI] 2021

Optimized Urban Form Result Which Leading To Our Following Research And Design

This figure shows the optimized urban form evaluation result from Step 8. But it is still not sufficient to achieve the ‘Zero Carbon Future’ design target. We criticised it from three aspects: Reason 1 The optimized result is only the optimal low carbon urban form, somehow, it's still hard to meet the ambition of towards zero carbon future Reason 2 The continuous changes of urban conditions and environment in the future will pose challenges to the current urban form that tends to be zero carbon. In order to achieve the design goal of zero carbon future, it should not be limited to statically satisfying zero carbon under the current condition/context, but to meet the design goals of towards zero carbon in the scenario at different times in the future. In order to achieve this goal, the resilience of the city/building is particularly important.

Reason 3 Specifically in Manchester, the significant increase of demographic in Manchester will bring even more challenge in residential and public service demands.

Therefore, we will discuss in the following pages how we can achieve our ambitions for a zero-carbon city by introducing modular buildings generation within our tool.

52

2021 CPU[Ai] Studio 3

4.2.2 THE NEW CIRCULAR RESOURCE MODEL

Manchester school of architecture

Traditional Resource Model

TAKE

USE

MAKE

DISPOSE

Circular Resource Model MAKE

TAKE

REUSE/ RECYCLE

USE

DISASSEMBLE

Modular Building

Modular Building system provides potential to cope with the predictable and unpredictable environmental changes faced by future development and how to realize the flexible development of buildings in its life cycle.

Optimized Urban Form

The optimized urban form from previous step provide the essential building geometry boundary for further generation.

Increase unit

53

Replace units

Fill the Redundant Capacity

4.2.3

STUDIO 3 × CPU[AI] 2021

RESEARCH ON MODULAR ARCHITECTURE

Introduction Of The Concept And Origin Of Modular Architecture

Modular Architecture versus Integral Architecture Architecture can be classified in a variety of ways. Architecture can be divided into two types: modular and integral. In fact, architecture that is completely modular or fully integral is uncommon, and almost all architecture is somewhere in between.

On the one side, modular architecture allows for functionally decoupled component interfaces. In practice, this sometimes results in one-dimensional architecture, in which the functional elements of the building are mapped one-to-one to the design components. Integral architecture, on the other hand, is the polar opposite of modular architecture. Coupled interfaces between components are a feature of integral architecture. It has a more complicated (not one-to-one) mapping between functional elements in the function structure and design components.

What is Modular Design?

Modular design, also known as "modularity in design," is a design technique that divides a system into smaller parts known as modules or skids that can be generated separately and then used in various systems. Functional partitioning into discrete, scalable, and interchangeable modules, strict use of well-defined modular interfaces, and use of industry specifications for interfaces are all characteristics of a modular framework. The advantages of modular architecture are design versatility and cost savings. Modular homes, solar panels, and wind turbines are all examples of modular systems. The benefits of standardization and customization are combined in modular design.

Origin

Growing

Integral Architecture

New Module

Obsolete Module

In architectural projects, it is important to use a modular approach. Upgradability, serviceability, versatility, and other characteristics define the modular design. The advantage of modular architecture is that any module can be replaced or added without impacting the rest of the system.

Source: 1. Cohen, Jean-Louis. (2014). “Le Corbusier’s Modulor and the Debate on Proportion in France”. Architectural Histories, 2(1):23, pp.1-14.

2. Holtta-Otto, Katja. (2005). “Modular Product Platform Design”. Doctoral Dissertation, Helsinki University of Technology. Finland.

3. https://www.plataformaarquitectura.cl/cl/759050/primer-lugar-en-concurso-iberoamericano-de-viviendasocial-ix-biau-argentina/548e4dace58ece40d70000a7

Modular Architecture

54

4.2.4

STUDIO 3 × CPU[AI] 2021

WHY WE DESIGN MODULAR ARCHITECTURE

Analyse Its Advantages For Zero-carbon Cities From The Construction Process Of Modular Architecture

MAKE

Products

The built environment contributes around 40% of the UK’s total carbon footprint. 831MtCO2e was the total carbon footprint of the UK in 2014, 48MtCO2e was embedded through new construction. The buildings and modular homes feature also allow minimizing its ecological footprint in two different ways. Firstly, modularity means using the same module in multiple configurations enabling a large variety of designs without using diverse component types.

This modularity brings several advantages, such as reduced capital requirements and economies. Secondly, the short construction period of modular buildings means less transportation and lower carbon emissions.

REUSE/ RECYCLE

Cement 60 50 40

DISASSEMBLE

30

NEW CONSTRUCTION

USE

Rebar

48

According to previous studies, a 17 % rise in wood use in the construction industry could result in a 20 % reduction in carbon emissions from all building materials manufacturing. The pollution reduction is mostly due to the substitution of wood for brick, aluminium, and, to a lesser degree, steel and concrete, which consume far more energy in the manufacturing process than wood. Furthermore, wood is totally ecological and 100 % recyclable, and since it is processed in a shorter period of time, it produces less waste.

Sand

20 10 0

MtCO2e

was embedded through new construction

HCB

Transport and Construction

Coarse aggregate

Intergral Embodied Energy

Embodied CO2

Weight

Modular

The percentile portions of the individual building materials to the total weight, embodied energy, and CO2 emissions.

55% PRODUCTS

10%

TRANSPORT

20%

CONSTRUCTION

TRANSPORT

Buildings are made of a variety of materials, and the production of each one absorbs energy and emits CO2. The top five most commonly used building materials were listed in this report (cement, sand, coarse aggregates, hollow concrete blocks, and reinforcement bars).

Cement, hollow concrete blocks (HCB), and reinforcement bars (rebars) were found to be the largest energy users and CO2 emitters in the study. They were responsible for 94 % of the embodied energy and 98 % of the CO2 emissions when taken together.

CONSTRUCTION DURATION

Intergral Modular CONSTRUCTION

CONSTRUCTION DURATION

Modular building construction process, product systematization and industrialization can shorten the construction period and reduce carbon emissions

Source: 1.UKGBC Climate Change: https://www.ukgbc.org/climate-change/ 2. https://www.mdpi.com/2075-5309/9/6/136/pdf 3. Wood-based building materials and atmospheric carbon emissions 4.

ScienceDirect (n.d.). [Online] [Accessed on 4th March 2021] https://www. sciencedirect.com/science/article/pii/S1462901199000386.

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Adaptive Behaviour

Carbon emission problem New urban form

Emergence

K

Morphology Plot Scale & Relationship

Block

Drive

Development limited New Block pattern

K

Emergence

Self-Organising

r

Original Urban fro m Need to develop

Adaptive Behaviour

Building Density

r

Building Configuration

Original Block pattern Low efficiency

Building

Adaptive Behaviour Negative[-] Positive [+]

Source: 1. Adam Brennan, Manchester School of Architecture (Complexity Planning and Urbanism 2. Faucher, Jean-Baptiste. “A Complex Adaptive Organization Under the Lens of the LIFE Model: The Case of Wikipedia”. Egosnet.org. Retrieved 25 August 2012 3. Source: https://www.thoughtworks.com/insights/blog/leading-living-breathing-and-agileenterprise 4. Patricia Romero-Lankao, Daniel M. Gnatz , , Olga Wilhelmi, Mary Hayden, 2016. Urban Sustainability and Resilience: From Theory to Practice. MDPI 5. “Resilience and stability of ecological systems”. in: Annual Review of Ecology and Systematics. Vol 4 :1-23 6. Walker, B., Holling, C.S., Carpenter, S.R., Kinzig, A.P., 2004. Resilience, Adaptability and Transformability in Social ecological Systems. Ecology and Society

Self-Organising

Drive

Green Space

Negative[-] Positive [+]

Adaptive behaviour also affects the process of urban resilience in different layers. From the perspective of Resilience, we hope that our city can implement Zero Carbon in the future. So the adaptive behaviours will happen in the process of urban development in the future, disturbance force at all layers of the adaptive behaviour. When a threshold is reached, the system will enter another stage of evolution. We hope to realize zero-carbon form through this method in the future, which may come from the new architectural form and new spatial understanding brought by the progress of science and technology.

Negative[-] Positive [+]

As stated in the concept of Complex Adaptive System, the Complex adaptive system can be multi-layered, but more often, it is “self-organizing”. So we made our question from a much-layered perspective. We explore the method to implement Zero Carbon City from three layers. At the scale of Building, Block and Urban, the system organizes itself by related factors. However, the adaptive behaviour generated thus will affect adjacent layers. Feedback from the upper layer will also affect the adjustment of parameters to adapt to the new situation.

Urban

Through The Learning Of Resilience And Complex Adaptive System, We Can Understand Our Design Problems From Difference

Self-Organising

Drive

Lose value New Building types

Emergence

4.2.5

STUDIO 3 × CPU[AI] 2021

APPLICATION THEORY ON DESIGN PROBLEM

K

r

Building Type Building Hight Building Weight & Depth

Building In Use Demolished

56

Using The Theories Of Complex Adaptive System And Resilience To Understand The Relevance Between Different Factors And Urban, Block And Building, We Create A Theoretical Framework For The Development Of Zero-carbon Cities

Based on the concept of the Complex Adaptive System and Urban Resilience, we try to summarize our theory diagram to show how we view this problem. Guided by Resilience and Complex Adaptive System theories, we hope that our way of looking at problems is sustainable, controllable and adaptive. And the ability to adapt to and absorb changes in future development.

External Environment Population

Compact City

Local Requirement

Negative[-] Positive [+]

Block:

Urban:

Green Space

Negative[-] Positive [+]

Building Density

Plot Morphology Scale & Relationship

Block Configuration

Emergence

Energy Performance Climate

Temperature

Heat Island Effect

Impact and Feedback

Building: Building Type

Source: 1. Adam Brennan, Manchester School of Architecture (Complexity Planning and Urbanism 2. Faucher, Jean-Baptiste. “A Complex Adaptive Organization Under the Lens of the LIFE Model: The Case of Wikipedia”. Egosnet.org. Retrieved 25 August 2012 3. Source: https://www.thoughtworks.com/insights/blog/leading-living-breathing-and-agileenterprise 4. Patricia Romero-Lankao, Daniel M. Gnatz , , Olga Wilhelmi, Mary Hayden, 2016. Urban Sustainability and Resilience: From Theory to Practice. MDPI 5. “Resilience and stability of ecological systems”. in: Annual Review of Ecology and Systematics. Vol 4 :1-23 6. Walker, B., Holling, C.S., Carpenter, S.R., Kinzig, A.P., 2004. Resilience, Adaptability and Transformability in Social ecological Systems. Ecology and Society

Robustness

Building Weight & Depth

Emergence

Solar Potential Solar Irradiation

Heating Energy & Cooling Energy

Building Direct passive solar

Technology

Building Hight

Low Carbon Urban Form

Zero-carbon City

First of all, from the perspective of multi-layer, we established a Complex Adaptive system to understand the interplay and self-organizing behaviours among building, pattern and urban. And through the energy performance and solar ability. Two aspects of the evaluation of complex adaptive behaviours. Finally, we hope that this system can respond to the changes brought by the external environment from the four key elements of resilience. In addition, the system is given feedback from the angle of resilience to conduct a new round of adaptive behaviour. To achieve a highly adaptable and flexible low-carbon city form.

Impact

Density

Human activities

Complex Adaptive System

4.2.6

STUDIO 3 × CPU[AI] 2021

THEORETICAL FRAMEWORK

Daylight Accessibility

Occupational Energy

On-site energy generation ability

Energy Consumption

Renewable energy ratio

Redundancy

Resourcefulness

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Rapidity

Resilience City

4.2.7

THE RESILIENCE OF MODULAR BUILDING

STUDIO 3 × CPU[AI] 2021

External Environment

The Use Of Modular Building Can Help Us Realize The Goal Of Resilience City In The Future From The Building Level Low Carbon Urban Form The realization of Low Carbon Urban Form is the basis for the realization of Zero Carbon City. However, to achieve Zero Carbon City, our city needs to be able to cope with various challenges in future urban development. Therefore, we need to implement Resilience City.

Modular Building can help us realize resilience. Modular Building’s flexibility and variability will enable us to realize the city’s flexibility against the external environment with minimum energy consumption in the process of future urban changes.

The tool outcome from previous step can help us to implement the low carbon urban form. And the optimized building form.

Population Explosion, Climate change,

Modular Building

Modular Building can make us more flexible in building form to cope with the predictable and unpredictable environmental changes faced by future development, and how to realize the flexible development of buildings in its life cycle.

Changes in functional requirements, Scientific and technological progress

Zero-carbon City

Increase unit For example, the future population will increase, and the city needs more new houses. We can provide more living space by adding units on the basis of the original building.

Building form optimization

Replace units When the city needs more commercial or other functional services, we can replace the units in part of the buildings to realize the transformation of the building functions, so as to adapt to the needs of residents in different periods.

Fill the Redundant Capacity At some point in the future, buildings will lose their vitality due to a variety of external reasons, and there will be a lot of redundant space created. We can turn it into green space or other public space to fill the space.

On the other hand, it further improves the urban green space coverage rate.

Increase unit

Replace units

Fill the Redundant Capacity

Resilience City

In future cities, maybe 50 years or more. Modular Building will form a series of buildings with new functions through the combination of different units, helping the city to realize the carbon cycle itself, thus realizing the goal of zero-carbon city Green space building

Energy production building

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4.2.8

MODULAR ARCHITECTURE CASE STUDY

Research on the PROJECT: Houthavens plot 1&2, Marc Koehler Architects

STUDIO 3 × CPU[AI] 2021

CONCEPT OF THE DESIGN

MODULAR ANALYSIS Tower

Basic unit depth from 10 to 25m.

PLOT1

Gallery

PLOT2

Basic unit width from 3 to 4m.

Block

Townhouse

The collective design of Houthavens Plot 1 & 2 is based on Open Building principles. Apartments were prepared as shell-like boxes stacked on top of each other. MKA and Space Encounters, among other architects, co-designed the interiors with each owner. The final design collaborates with diverse lofts and hybrid programs to support a more open and inclusive neighbourhood.

The casco-lofts were generally 5.7m wide, 18m deep, and have two lofts of 75m2 per floor, subdivided into two smaller units of 35 square metre. Homes ranged from XS (35m²) for the young urban professionals to XL (200m²) homes for large families. Multiple shafts allowed for a flexible layout and the location of kitchens and bathrooms to be freely configured. Five-metrehigh ceilings allow occupants to build in 70% extra floor space. To eliminate columns and allow open plan living, a 10cm thick CLT mezzanine was suspended from the ceiling with steel rods. While the building integrates the state-of-the-art sustainability features were designed to be climate neutral. It includes CO2 directed vents, solar panels, geothermal pumps, floor cooling with water from the canals, remote-controlled sun shutters and shared mobility and e-mobility options.

Units of different sizes are distributed on each floor to meet the needs of different groups of people.

XL

Each unit is directly connected to the central core to achieve a flexible layout.

Lofts 2 sides 240 to 480 m2

180 to 360 m2

120 to 240 m2

60 to 120 m2

Lofts 1 sides 120 to 240 m2

90 to 180 m2

60 to 120 m2

30 to 60 m2

3/4Lofts 2 sides 190 to 360 m2

135 to 270 m2

90 to 180 m2

45 to 90 m2

120 to 240 m2

90 to 180 m2

60 to 120 m2

30 to 60 m2

Apartment Lofts 2 sides

Apartment Lofts 1 sides

Houthavens Plot 1 & 2 provide various types of public and private spaces for different families in the community.

XS 60 to 90 m2

Source: • Superlofts Blok Y reinvents the suburban modern housing slab | Marc Koehler Architects (n.d.). [Online] [Accessed on 25th March 2021] https://marckoehler.com/project/superlofts-blok-y/.

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45 to 60 m2

30 to 60 m2

15 to 30 m2

2021 CPU[Ai] Studio 3

Manchester school of architecture

1 Population Increase 3100

Total population in Manchester Projected maxmum total population

3000

Date of Projection

2900

2800

2700

2600

4.2.9 THE CONDITION OF THE FUTURE CITY

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2 Buildings Increase

Present Condition Green Space area per capita: Energy Consumption: Carbon Dioxide Emissions:

Future Condition Under the current low-carbon city form, it can meet the increase of 150,000 households in the northern gateway in the future. However, as the population continues to increase, buildings will become denser, green space per capita will decrease, urban energy consumption and carbon emissions will increase, and the balance of zero-carbon cities will be broken.

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Green Space area per capita: Energy Consumption: Carbon Dioxide Emissions:

2021 CPU[Ai] Studio 3

Manchester school of architecture

Population Increase Projection 3100

Total population in Manchester Projected maxmum total population

3000

Date of Projection

2900

2800

2700

A Unit of Home

2600

Solar Panel

4.2.10 HOW TO DEAL WITH THE NEGATIVE IMPACT

2038

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Traditional Resource Model A Power-generating Unit

A Family

Amount

Amount Energy Consumption

CO2 Emission

Population

Population

Time

Time

In the traditional model, when the population increase will far exceed the original intention of the city design: provide space for 150,000 new homes for the northern gateway in the future, the carbon emission balance in the city will be broken, and carbon emissions will gradually increase.

New Proposed Resource Model Renewable Energy

Amount

Amount

Energy Consumption Population

In our design tool, we turn population growth into a driving force for zero-carbon cities. As the population increases, the number of power-generation residential units increases, which also can gradually achieve electricity surplus, thereby achieving carbon neutrality.

Population

CO2 Emission Time

Time

In the new model we designed, a family is used as a solar power generation unit, and as the population increases, the power generation capacity increases. The rising population has turned into a driving force for achieving a zero-carbon city.

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2021 CPU[Ai] Studio 3

4.2.11 MODULAR ADAPTIVE STRATEGY

Manchester school of architecture

2025

2045

In 2045, most residential modules will replace office modules to meet the housing demand after the population increases.

Starting from the top floor, the office modules of the office building are replaced by residential modules.

In 2045, residential building that has reached the height limit.

As the population continues to increase, the residential building that has not reached the height limit will continue to grow.

In the future, residential modules will be built above low-rise commercial and public building based on the previous building form.

In 2045, residential modules have been built above low-rise commercial and public building.

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4.2.12

STUDIO 3 × CPU[AI] 2021

S O L A R PA N E L RESEARCH

Introduce The Basic Composition And Working Principle Of Solar Panels

Sun Sunlight is made up of tiny packets of energy called photons. These photons radiate out from the sun, and about 93 million miles later, they collide with a semiconductor on a solar panel here on earth. The solar panel comprises several individual cells. Each of them has a positive and a negative layer, creating an electric field.

When the photons strike the cell, their energy frees electrons in the semiconductor material. The electrons create an electric current, which is harnessed by wires connected to the positive and negative sides of the cell.

The generated electricity is multiplied by the number of cell in each panel, and the panels in each solar array.

Photon

Solar Cell

Section of Cells The solar panel is made up of several individual cells, each with a positive and a negative layer, which create an electric field. It works something like a battery. So the photons strike the cell, and their energy frees some electrons in the semiconductor material.

Aluminium Frame Tempered Glass Encapsulant - EVA Solar Cells Encapsulant - EVA Back Sheet Source: • Solar Photovoltaic Technology Basics | Department of Energy (n.d.).

[Online] [Accessed on 25th March 2021] https://www.energy.gov/ eere/solar/solar-photovoltaic-technology-basics.

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4.2.13

SOLAR FACADE PRINCIPLES&STRATEGY

STUDIO 3 × CPU[AI] 2021

Demonstrate The Key Angles Of Solar Panel That Can Benefit In Maximising The Efficiency On Facade

In order to maximise the solar panel on the facade, the appropriate angle is curial to make the panel surface perpendicular to the sunlight direction.

Ideally, it should be a South facing roof, but East and West facing roofs also produce a good electrical output. Even a North facing roof will still work at 68% capacity. In the Summer the best roof pitch in South East England is around 28 degrees but in Winter as the sun dips to the horizon this becomes 74 degrees.

Potential Annual Rotation

28°

51°

74°

Key Principles of utilizing Solar Irradiation to generate power: A. The southly facing Roof and facade should be fully utilized. Meanwhile, the west and east facade should also be considered as the location for facade solar panel system.

B. The optimum panel angel is the main factor that will impact on the efficiency of power generation. The ideal solar facade system should be concerned as a kinetic system that can adapt different angle of sunlight C. The shading need to be considered as another issue for improve the generation efficiency. Meanwhile, the panel position on the facade should always collaborate with shading system to minimize the impact on interior view.

Ideal Average Angel in Winter

Ideal Average Angel in Autumn & Spring

Ideal Average Angel Summer

Jan

Feb

Mar

Apr

May

Jun

67°

59°

51°

43°

35°

28°

Jul

Aug

Sep

Oct

Nov

Dec

35°

43°

51

59°

67°

74°

Potential Daily Rotation

Source: • Hosseini, S. M., Mohammadi, M., Rosemann, A., Schröder, T. and Lichtenberg, J. (2019) ‘A morphological approach for kinetic façade design process to improve visual and thermal comfort: Review.’

Building and Environment, 153, April, pp. 186–204.

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4.2.13

SOLAR FACADE PRINCIPLES&STRATEGY

STUDIO 3 × CPU[AI] 2021

Demonstrate The Key Angles Of Solar Panel That Can Benefit In Maximising The Efficiency On Facade

The facade acts as mediator between the interior and the exterior environment and fulfills various functions.

The envelope can mitigate solar radiation, thereby offering reductions in heating/cooling loads, and improve distribution of daylight. Therefore, integrating PV modules into a dynamic shading system offers the possibility to fine tune the different functions, generate electricity, and balance energetic performance with architectural expression.

Min

Max Potential PV Panel Position Potential Shading Position

Key Principles of utilizing Solar Irradiation to generate power: A. The southerly facing Roof and facade should be fully utilized. Meanwhile, the west and east facade should also be considered as the location for facade solar panel system.

B. The optimum panel angel is the main factor that will impact on the efficiency of power generation. The idea solar facade system should be concerned as a kinetic system that can adapt different angle of sunlight

Potential PV Panel Position

C. The shading need to be considered as another issue for improve the generation efficiency. Meanwhile, the panel position on the facade should always collaborate with shading system to minimize the impact on interior view.

Source: • Nagy, Z., Svetozarevic, B., Jayathissa, P., Begle, M., Hofer, J., Lydon, G., Willmann, A. and Schlueter, A. (2016) ‘The Adaptive Solar Facade: From concept to prototypes.’ Frontiers of Architectural Research, 5(2) pp. 143–156. design process to improve visual and thermal comfort: Review.’

Building and Environment, 153, April, pp. 186–204.

Min

Max

Importance PV Electricity Generation

Interior VIew

Daylight

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4.2.14

STUDIO 3 × CPU[AI] 2021

ENERGY STORAGE RESEARCH

The Precedent For Energy Storage And How To Use The Stored Energy In The Hybrid Building Typology

Energy Flow In Our Design

Energy Hub APP for Families

Case Study: TESLA SOLAR ROOF One of Tesla’s solar roof products is called the power wall. It can be installed internally, externally and both.

As the grid goes down, power walls first store electricity and then sense outages, automatically turning into house energy supply. Power wall works differently with gasoline generators. It converts solar energy to electricity to keep home appliances works, with a low level of noise.

Therefore, in our design tool, we introduce a similar concept, called ENERGY HUB. It can store the electrical energy that has converted from solar panels.

Energy Hub

Solar panels convert sunlight into electrical energy, part of which is used directly by the family, and the other part is stored in an energy hub for household use when the intermittent happens

By downloading this APP, people can intuitively see the electricity generated by their home’s solar panels and their daily electricity consumption.

Source: • Solar Roof | Tesla (n.d.). [Online] [Accessed on 9th May 2021] https://www.tesla.com/solarroof.

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4.2.15

HYBRID BUILDING GENERATION

STUDIO 3 × CPU[AI] 2021

Step 8 Optimized Building Form

Building Modularity

Demonstrate The How The Design Tool generates The Hybrid Typology By Steps

STEP 9

INPUT DATA FROM PREVIOUS STEP

HYBRID TYPOLOGY GENERATION

Model With Solar Optimization

1

2

On the premise of following the building form, the tool divide the building into modules and stack them to form a new building.

3

Adding Details

The tool will generate the hybrid building typology based on solar optimized building geometry boundary. The modularity will be the key method to transform the building massing to follow the adaptive design ideas. Moreover, the solar panel will be also generated by following the building geometry boundary as it has been fully optimized by considering the solar irradiation and sunlight accessibility to the surroundings

The Tool added doors, windows, balconies and green spaces to the building.

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4

The Basic Building Configuration

4.2.16

Hybrid Building

HYBRID BUILDING GENERATION

Demonstrate The How The Design Tool generates The Hybrid Typology By Steps

STEP 9

5

STUDIO 3 × CPU[AI] 2021

Adding Energy Hub

INPUT DATA FROM PREVIOUS STEP

HYBRID TYPOLOGY GENERATION

Model With Solar Optimization

6

The tool adds energy hub to the building, it can store the excess electricity converted by solar panels, and it will power the home when the sun is insufficient.

7

The Updated Building Configuration

Adding Solar Panels

The tool will generate the hybrid building typology based on solar optimized building geometry boundary. The modularity will be the key method to transform the building massing to follow the adaptive design ideas. Moreover, the solar panel will be also generated by following the building geometry boundary as it has been fully optimized by considering the solar irradiation and sunlight accessibility to the surroundings

The tool generates solar panels on the facade and roof of the building by following the geometry boundary of optimized building massing. These panels provide daily electricity demand for each household and store the excess electricity in energy hub.

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8

The Final Configuration

2021 CPU[Ai] Studio 3

Solar panels absorb solar energy and convert it into electrical energy for household use.

4.2.17 HOW DOSE THE HYBRID TYPOLOGY WORKS

Manchester school of architecture

Solar Panels

Power Core The core tube in each building is also used as a power transmission device, which is linked to the energy hub.

Energy Hub The energy hub can store the excess electricity converted by solar panels, and it will power the home when the sun is insufficient.

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4.2.18 COMMERCIAL COMPLEX

HIGH-RISE OFFICE

COMMERCIAL

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OVERALL DEPTH (M)

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RESIDENTIAL

OVERALL WIDTH (M)

OVERALL DEPTH (M)

25

HIGH-RISE RESIDENTIAL

OFFICE

OVERALL WIDTH (M)

60

HYBRID TYPOLOGIES COLLECTION

14.9

22

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18

93 100

4.2.18 PUBLIC SERVICE

PARAMETER BLOCK

OVERALL WIDTH (M)

45

30

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35

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15

OFFICE

OVERALL WIDTH (M)

OVERALL DEPTH (M)

18

CO-WORKING OFFICE + RETAIL

RESIDENTIAL

PUBLIC SERVICE

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18

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45

CHAPTER 4.2 CONCLUSION

RESIDENTIAL Solar Irradiation(kWh/m2): 6480800 Building Area(m2): 25990 Residential Unit Number: 347

Annual Electricity Consumption

2

Potential Solar Panel Area(m ): 4480

OFFICE

3000700kWh

Annual Electricity Generation

3378700kWh

Solar Irradiation(kWh/m2): 4400100 Building Area(m2): 18911 Annual Electricity Consumption

1167700kWh

Annual Electricity Generation

1418325kWh

2021 CPU[Ai] Studio 3

Potential Solar Panel Area(m2): 3602

PUBLIC SERVICE Solar Irradiation(kWh/m2): 2151600 Building Area(m2): 7699 Annual Electricity Consumption

Annual Electricity Generation

900783kWh

474730kWh

2021 CPU[Ai] Studio 3

Potential Solar Panel Area(m2): 1366

RESIDENTIAL Solar Irradiation(kWh/m2): 2833400 Building Area(m2): 11282 COMMERCIAL

Residential Unit Number: 150

Solar Irradiation(kWh/m2): 2185800 Building Area(m ): 5655 Potential Solar Panel Area(m ): 1426

Potential Solar Panel Area(m ): 2568

OFFICE

2

2

2

Annual Electricity Consumption

469365kWh

Annual Electricity Generation

374430kWh

Solar Irradiation(kWh/m2): 2853900 Building Area(m2): 10195 Potential Solar Panel Area(m2): 2570

Annual Electricity Generation

Annual Electricity Consumption

764625kWh

528490kWh

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Annual Electricity Consumption

Annual Electricity Generation

1466660kWh

859140kWh

2021 CPU[Ai] Studio 3

Manchester school of architecture

THE GATE WAY TO ZERO CARBON CITY

Chapter 4.3

TOOL MECHANISM TEST AND EVALUATION

4.3.0 Workflow relates to generation result evaluation 4.3.1 How dose the evaluation system form the Generative design process 4.3.2 Researched Principle evaluation 4.3.3 Brief & Design Matrices evaluation 4.3.4 Simulation Evaluation 1

This chapter demonstrates four key criteria sets of two evaluation steps in the generative design process. The evaluation system aims to provide optimal iteration for the design solution, meanwhile allows users to select any other extreme iterations for further investigation.

4.3.5 Simulation Evaluation 2

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STEP 1

STEP 2

INPUT SITE INFO AND REFINE

RUN GREEN AMENITY DISTRIBUTION SIMULATION

STEP 3 RUN URBAN PATTERN GENERATION

STEP 4

CONSOLIDATE AMENITY INTO URBAN PATTERN

GENERATE LANDUSE SPATIAL STRATEGY

EVALUATION 1 STEP 5

STEP 6

STEP 7

ASSIGN INITIAL BUILDING TYPOLOGY

INITIAL MASSING POSITION & HIGHT OPTIMIZATION

ITERATION SELECTION AND DATA INVESTIGATION

PROGRAM ALLOCATION 2021 CPU[Ai] Studio 3

4.3.0

WORKFLOW RELATES TO GENERATION RESULT EVALUATION

EVALUATION 2 STEP 8 BUILDING MASSING SOLAR OPTIMIZATION

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STEP 9

STEP 10

HYBRID TYPOLOGY GENERATION

ITERATION SELECTION AND DATA INVESTIGATION

STEP 1

STEP 2

STEP 3

STEP 4

STEP 5

STEP 6

STEP 8

STEP 9

2021 CPU[Ai] Studio 3

4.3.1

HOW DOSE THE EVALUATION SYSTEM FORM THE GENERATIVE DESIGN PROCESS

Generated Iteration From Step 1 to 7

Adjustment (Feedback) on Variables of Key Steps to Generate next Iteration

STEP 7 STEP 10

ITERATION SELECTION AND DATA INVESTIGATION

ITERATION SELECTION AND DATA INVESTIGATION

Selected Iteration for Further Optimization

Feedback Loop 2

Within the Generative design process, the evaluation step is vital for achieving the multiple iterations generation and optimal outcomes selections according to desired targets.

The evaluation criteria can be set based on researched theory & principles, the brief required matrices, the simulation results, etc. The Generic Algorithm can help in formulating the feedback loop and adjust key variables in multiple generation steps.

Moreover, the recorded evaluation result will be the key information for users’ further investigation and selection.

Feedback Loop 1

2021 CPU[Ai] Studio 3

Key Variables

Adjustment (Feedback) on Variables

Generated Iteration Step 9

Selected Iteration as output

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4.3.2

STUDIO 3 × CPU[AI] 2021

RESEARCHED PRINCIPLE EVALUATION

The Tool Will Calculate/measure The In-use Energy-related Spatial Aspects To Evaluate The Performance Of The Iterations Regarding Low Energy Demands.

The tool will measure and calculate the spatial aspects , which have a direct impact on building energy usage, of each iteration. The researched correlations between spatial aspects value and building in-use energy demand will be set as the criteria to evaluate each iteration from previous stages. Moreover, the result will form the feedback loop with other evaluation steps to drive each stage’s adjustment of parameters

STEP 1

STEP 7

SPREAD MORPHOLOGY

DATAEVALUATION AND ITERATION SELECTION CO2

STEP 2

COMPACT MORPHOLOGY CO2

CO2

CO2 CO2

STEP 3

STEP 4

Critical set 1: 1. E n e r g y- r e l a t e d spatial aspects 2. Metrics of population, green amenity and land-use 3. Annual Solar irradiation of buildings 4. E l e c t r i c i t y C o n sumption & Generation

CO2 CO2 CO2

Energy

Energy

CO2 CO2

CO2

CO2

CO2

CO2

CO2

CO2

CO2

STEP 5

STEP 10 DATAEVALUATION AND ITERATION SELECTION Plot Ratio

STEP 6

STEP 8

STEP 9

Critical set 2: 1. Metrics of population, green amenity and land-use 2. Annual Solar irradiation of buildings 3. Electricity Consumption & Generation

S/V Ratio

Plot Ratio

Havg

H4 H3

S/V Ratio

Havg

H4 H1

H2

H2 H1

H1

Average Height

Building Coverage

Average Height

Building Coverage

W

W1 W2

H

Aspect Ratio

0.9 ≥ H/W

or H/W ≤ 1.1

N

MOS <1.15

H

Main Street Orientation

Source: 1. Blundell, S. (2019) ‘What do digital twins mean for the built environment?’ Planning, BIM & Construction Today. BIM News. 9th October. [Online] [Accessed on 22nd November 2020] https://www.pbctoday.co.uk/news/bim-news/digital-twin-4-0/64519/.

2. Mutani, G., Gamba, A. and Maio, S. (2016) ‘Space heating energy consumption and urban form. The case study of residential buildings in Turin (Italy) (SDEWES2016.0441).’ In. 3. Rode, P., Keim, C., Robazza, G., Viejo, P. and Schofield, J. (2014) ‘Cities and Energy: Urban Morphology and Residential Heat-Energy Demand:’ Environment and Planning B: Planning and Design. SAGE PublicationsSage UK: London, England, February

Aspect Ratio

76

0.9 < H/W < 1.1

N

Main Street Orientation

MOS >1.15

4.3.3

STUDIO 3 × CPU[AI] 2021

BRIEF DESIGN MATRICES EVALUATION

The Tool Will Calculate/measure Each Generation Outcome To Evaluate How It Meets The Metrics From Brief/design Requirements Regarding Land Use, Population, Green Amenity

STEP 7 DESIGN TARGETS / MINIMUM REQUIREMENT METRICS

DATAEVALUATION AND ITERATION SELECTION STEP 2

STEP 3

STEP 4

Critical set 1: 1. E n e r g y - r e l a t e d spatial aspects 2. Metrics of population, green amenity and land-use 3. Annual Solar irradiation of buildings 4. E l e c t r i c i t y C o n sumption & Generation

STEP 10 DATAEVALUATION AND ITERATION SELECTION STEP 6

STEP 9

Critical set 2: 1. Metrics of population, green amenity and land-use 2. Annual Solar irradiation of buildings 3. Electricity Consumption & Generation

1-2m2 Per Inhabitant

15,000 new homes in next 15-20 years (Population will increase by 45,000) According to the document conducted by the Manchester city council, the current population of the area is 35,000, and the Northern Gateway is projected to provide 15,000 new homes in the next 15-20 years.

We utilize this data to calculate the indicators of every specific block area. These metrics will be combined with the brief requirement to form the brief& planning strategy evaluation criteria. Again, the result of this step will be the vital feedback to adjust the variable of each iteration to ensure the design outcome effective and feasible.

Source: 1. Williams, K. (2009) ‘Space per person in the UK: A review of densities, trends, experiences and optimum levels.’ Land Use Policy, 26, December, pp. S83–S92.

2. https://www.mentorworks.ca/blog/market-trends/01-downsizing-to-optimizeliving-space/ 3. https://www.nationmaster.com/country-info/stats/Geography/Area/Land/Per-capita

PUBLIC SERVICE

Total area approximate 45,000m2- 90,000m2

×45,000 Population

RESIDENTIAL

STEP 5

STEP 8

×45,000 Population

PUBLIC SERVICE

STEP 1

15-37m2 Per Inhabitant

RESIDENTIAL

Total area approximate 675,000m2- 1665,000m2

×45,000 Population

COMMERCIAL

2-4m2 Per Inhabitant

COMMERCIAL

Total area approximate 90,000m2- 180,000m2

×45,000 Population

GREEN SPACE

9m2 Per Inhabitant

GREEN SPACE

Total area approximate 405,000m2

77

4.3.4

SIMULATION EVALUATION 1

The Solar Irradiation Simulation On Each Iteration To Evaluate The Solar Potential And Provide Data For Electricity Generation Calculation

STEP 1

STEP 7 DATAEVALUATION AND ITERATION SELECTION

STUDIO 3 × CPU[AI] 2021

The Tool will simulate annual solar irradiation for each iteration.

This measurement is essential for evaluating whether the application of researched spatial aspect and building form optimization is successful. Moreover, the further calculation of annual electricity consumption and annual electricity generation will base on this simulation result.

STEP 2

STEP 3

STEP 4

Critical set 1: 1. E n e r g y - r e l a t e d spatial aspects 2. Metrics of population, green amenity and land-use 3. Annual Solar irradiation of buildings 4. E l e c t r i c i t y C o n sumption & Generation

Low

High

STEP 5

STEP 10 DATAEVALUATION AND ITERATION SELECTION STEP 6

STEP 8

STEP 9

Critical set 2: 1. Metrics of population, green amenity and land-use 2. A n n u a l S o l a r irradiation of buildings 3. Electricity Consumption & Generation

78

STUDIO 3 × CPU[AI] 2021

REASON FOR THIS EVALUATION

The Solar Irradiation Simulation On Each Iteration To Evaluate The Solar Potential And Provide Data For Electricity Generation Calculation

Consumption by Fuel 1970 to 2019 in UK 80

STEP 1

STEP 7 DATA EVALUATION AND ITERATION SELECTION STEP 2

STEP 3

STEP 4

Critical set 1: 1. E n e r g y - r e l a t e d spatial aspects 2. Metrics of population, green amenity and land-use 3. Annual Solar irradiation of buildings 4. Electricity Consumption & Generation

Annual Consumption (TWh)

70 60 50 40

Gas Electricity

30

In the past 20 years, Britain’s dependence on and consumption of electricity is approaching the use of gas. This also lays the foundation for the use of renewable energy to generate electricity and gradually achieve zero-emission energy generation.

20

Annual Electricity Generated by fuel in UK 100 90

Total

80

Generation (TWh)

4.3.5

SIMULATION EVALUATION 2

70 60 50

Renewable

30

Fossil Fuel

an important design factor for achieving

Nuclear

the onsite energy & electricity surplus.

10

Bio-energy & Waste

10

0

Coal and Other Solid

0

1980

1990

2000

2010

2019

Q1

EVALUATION OF ELECTRICITY CONSUMPTION

: 83 kwh/m2 *yr Average Annual Electricity Consumption per square meter

Iteration Total Floor Area

electricity generation simulation is

40

20

1970

In 2020, Electricity Generation from renewable energy has been exceeded the generation from fossil fuels and other resources. This trend also hints that the simulation of on-site

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

EVALUATION OF ELECTRICITY GENERATION

Potential Solar Panel area of each building (Various)

COMMERCIAL

STEP 5

STEP 10 DATA EVALUATION AND ITERATION SELECTION STEP 6

STEP 8

STEP 9

: 75 kwh/m2 *yr Average Annual Electricity Consumption per square meter

: 130 kwh/m2 *yr Critical set 2: 1. Metrics of population, green amenity and land-use 2. Annual Solar irradiation of buildings 3. Electricity Consumption & Generation

Source: 1.Department of Energy & Climate Change (2013) National Energy Efficiency DataFramework.

2. Experimental Statistics (2019) ‘Energy consumption in new domestic buildings 2015 – 2017 (England and Wales),’ December, p. 16.

3. National Statistics (2020) Energy Consumption in the UK (ECUK) 1970 to 2019, p. 2.

4. National Statistics (2021) Energy Trends, p. 12.

Average Annual Electricity Consumption per square meter

: 203 kwh/m2 *yr Average Annual Electricity Consumption per square meter

Iteration Total Floor Area

Iteration Total Floor Area

OFFICE

Potential Solar Panel area of each building (Various)

Potential Solar Irradiation to Electricity conversion rate

Current Scenario: 30%-40%

RESIDENTIAL

Iteration Total Floor Area

PUBLIC SERVICE

Total Annual Consumption

Total Annual Generation

79

Future Scenario: 60%-70%

Chapter 4.3

END & Export the Finalized 3D model STAGE 10

CONCLUSION

Evaluation based on performance Metric & Criteria Critical set 2: 1. Annual Solar irradiation of facade 2. Metrics of population, green amenity and land-use 3. Electricity Consumption & Generation

The set of evaluation processes aim to achieve the generative process of the entire computational system. Creating this iterative comparison will potentially achieve the optimal result to respond to ‘Zero Carbon Future City’ and other secondary design targets.

2021 CPU[Ai] Studio 3

As the chapter demonstrated, two main evaluation sets are being concerned within the generative process of our proposed computational method. Due to the evaluation workload and running time, we decided to split the evaluation aspect of the entire process into two stages. Step 7, as the key evaluation process, will test the model from Step 6 optimized massing model to evaluate whether the optimized model has achieved the zero-carbon design target informed by three main researched criteria. As the model is still at the massing level, the feedback will be sent back to multiple stages to adjust variables. The iterations of these generative processes will potentially achieve the optimal/ balanced solution for all the criteria. Meanwhile, it also allows user to select the extreme iterations for any one of the evaluation aspects.

The second evaluation process performances as part of Step 10. It mainly targets whether Step 8 (Solar Optimization) and Step 9 (Hybrid Typology Generation) can achieve better results corresponding to electricity generation and solar irradiation. Simultaneously, the process will also evaluate whether the building level optimization has a minimal negative impact on the other 2 evaluation criteria set (Energy-related Spatial Aspect & brief design matrices evaluation). As mentioned above, the feedback loop will only be formed to adjust the variables at step 9 due to the model’s detail level.

STAGE 9

Feedback loop 3

Run the Generation Hybrid Typology Application

STAGE 8 Variables set 7.1: Numbers of floor unit area to be Omitted

2021 CPU[Ai] Studio 3

Feedback loop 3

Variables set 7.2: Floor numbers for solar accessibility

Optimization of building form Selected 3D model

STAGE 7 STAGE 3

Critical set 1: 1. Energy-related spatial aspects 2. Annual Solar irradiation of facade 3. Metrics of population, green amenity and land-use 4. Electricity Consumption & Generation

Urban Pattern Generation (Super block subdivision) Variables set 2: 1. Super block boundaries 2. Number of entrances of the super block 3. Subdivided plot width & length 4. Main orientation of grid

New Green amenity & patch consolidation Variables set 3: 1. Orientation of consolidated green patch (North, South, West, East) STAGE 4 Land-use spatial arrangement strategy

Evaluation based on performance Metric & Criteria

Feedback loop 2

Variables set 6: 1. Location factor (variation factor)

Run the Optimization

Optimization of building position within plot STAGE 6

Variables set 5: 1. Building density (further subdivision of plot) 2. Height constraints of building massing 3. Building orientation

Land-use Allocation Assign building typologies Variables set 4: 1. Single use / mixed use cluster 2. Ratio of different land-use programs

STAGE 5

COMPUTATIONAL LOGIC OF EVALUATION PROCESS

80

2021 CPU[Ai] Studio 3 2021 CPU[Ai] Studio 3

THE ENERGY HUB WILL NOT ONLY PROVIDES THE POWER BUT ALSO BE THE PART OF THE VERTICAL GREEN AMENITY

81

2021 CPU[Ai] Studio 3

Manchester school of architecture

Chapter 5

5.0 DESIGN TOOL OUTCOME EVALUATION DATA GRAPH

RESULT ANALYSIS

5.1 OVERVIEW OF OUTCOME DATA 5.2 ITERATION COLLECTION

THE GATE WAY TO ZERO CARBON CITY

5.3.1 TO 5.3.9 SELECTED ITERATION DATA ANALYSIS

This chapter demonstrates the data analysis of all the design outcomes (30 Iterations) from the proposed design tool generative process. It maps out the data graph meanwhile clarifies the iteration selection mechanism. Optimal iterations data will be fully analysed and compared to others. Moreover, the key parameters of each selection will also be indicated.

5.4.1 FURTHER COMPARISON 1: BETWEEN TWO OPTIMAL ITERATION 5.4.2 FURTHER COMPARISON 2: WITH UNSELECTED ITERATION 5.4.3 FURTHER COMPARISON 3: WITH UNSELECTED ITERATION 5.4.4 FURTHER COMPARISON 4: WITH UNSELECTED ITERATION

82

5.0

DESIGN TOOL OUTCOME EVALUATION DATA GRAPH

Annual Electricity Consumption (GWh)

Annual Electricity Generation (GWh)

Solar Irradiation (GWh/m2)

Potential Solar Panel Area(m2)

Plot Coverage

FAR

H/W (Aspect Ratio)

Green Amenity Area (m2)

Residential Unit Number

0.316 245

720,000

0.314

3.1

1.0

316 240

215

2021 CPU[Ai] Studio 3

210 235

314 312

710,000

700,000

230

200

308

396,000

19,000

70,000

560,000 550,000

60,000

540,000 394,000

18,500

0.4 2.8

500,000 530,000

50,000

480,000

392,000 18,000

0.2 0.304

560,000 540,000

0.6

680,000

Urban Form & Building Solar Potential Optimization Related Criteria

19,500

398,000

0.8

2.9

0.306

Public Service Total Area (m2)

520,000

0.310

690,000

306

3.0

0.308

310 205

0.312

Office & Co-working Total Area (m2)

570,000

318 220

Commercial Total Area (m2)

520,000 460,000 510,000

2.7

Building In-use Energy Demand / Efficiency Related Criteria

Brief Requirement & Future Projection Related Criteria

Towards Zero Carbon Future City

83

40,000

2021 CPU[Ai] Studio 3

By implementing a generative design process, the design tool will adjust key variables in each step for different iterations. The Evaluation process will test and record the data according to the key criteria metrics as demonstrated below. The Data Mapping demonstrates The Evaluation Result of Generated Iterations. The Listed Metrics Categories Closely Link To Criteria Of Low Carbon Urban Form And Brief Requirements

5.1

OVERVIEW OF OUTCOME DATA

For An Overview Of The Result Data Generated, And Explain The Tool’s Screening Mechanism For The Results

DATA OVERVIEW

510,000

520,000 18,000 0.2

Iteration i

If the Annual Electricity Generation / Consumption>=90% Yes

No

2.7

......

0.304

0.306 680,000

306

Select the iterations that have best performance in every each criteria metrics

7 iterations have lower ratio of Electricity Generation / Consumption which is < 90%.

If any iteration perform as the best in any other criteria metrics

No

H oweve r, th ey do perform well in some criteria categories. Representative iterations will be further investigated

Yes

Iteration i

Potential iterations for user to Select

230

200

205

308

690,000 310

210 235

2021 CPU[Ai] Studio 3

40,000

The tool has 23 iterations that have the potential to achieve electricity surplus in the coming future by following our hybrid typology strategy (current ratio of Electricity Generation / Consumption ≥ 90% ).

However, the iterations performance in other criteria metrics is various that still need to be evaluated and selected by tool and users.

460,000

50,000 2.8

2.9 0.308

0.310 700,000 312

Generative Process

392,000

18,500 394,000 0.6

0.4

396,000 0.8 3.0 0.312 710,000 314 215 240

316

480,000 530,000

500,000

60,000 540,000

19,000

550,000

540,000

520,000

70,000 560,000 560,000

570,000

19,500 398,000 1.0 3.1 0.314 720,000

0.316 318

220 245

2021 CPU[Ai] Studio 3

ITERATION SELECTION MECHANISM

84

Potential comparable iterations

Store the data

The Tool Will Run 30 Iterations To Prompt The Iteration Closer To The Optimal Solution For The Design Targets.

ITERATION 1

ITERATION 3

ITERATION 4

ITERATION 5

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Residential Unit

Residential Unit

Residential Unit

Residential Unit

Residential Unit

ITERATION 6

2021 CPU[Ai] Studio 3

ITERATION 2

Selected Iterations for Further investigation

ITERATION 7

ITERATION 8

ITERATION 9

ITERATION 10

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Residential Unit

Residential Unit

Residential Unit

Residential Unit

Residential Unit

ITERATION 11

ITERATION 12

ITERATION 13

ITERATION 14

ITERATION 15

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Residential Unit

Residential Unit

Residential Unit

Residential Unit

Residential Unit

Iterations are Ordered by The benchmark of Annual Electricity Consumption

85

2021 CPU[Ai] Studio 3

5.2

ITERATIONS COLLECTION

The Tool Will Run 30 Iterations To Prompt The Iteration Closer To The Optimal Solution For The Design Targets.

ITERATION 16

ITERATION 17

ITERATION 18

ITERATION 19

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Residential Unit

Residential Unit

Residential Unit

Residential Unit

Residential Unit

ITERATION 21

2021 CPU[Ai] Studio 3

Selected Iterations for Further investigation

ITERATION 20

ITERATION 22

ITERATION 23

ITERATION 24

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Residential Unit

Residential Unit

Residential Unit

Residential Unit

Residential Unit

ITERATION 26

ITERATION 25

ITERATION 27

ITERATION 28

ITERATION 29

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Consumption

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Electricity Generation

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Green Amenity Area

Residential Unit

Residential Unit

Residential Unit

Residential Unit

Residential Unit

ITERATION 30

Iterations are Ordered by The benchmark of Annual Electricity Consumption

86

2021 CPU[Ai] Studio 3

5.2

ITERATIONS COLLECTION

5.3.1

SELECTED ITERATION: NO.25 DATA ANALYSIS

In-depth Data Analysis Of Iteration 25 That Has The Best Performance In Plot Coverage Among Selected Options

0.316 220

720,000

3.1

0.314

1.0

215

210 235

314 312

710,000

700,000

230

200

Annual Electricity Consumption (GWh)

Annual Electricity Generation (GWh)

3.0

0.310

306

Solar Irradiation (GWh)

396,000

19,000

394,000

18,500

0.4 2.8

60,000

680,000

392,000 18,000

Plot Coverage

FAR

500,000

50,000

530,000 520,000

H/W (Aspect Ratio)

Green Amenity Area (m2)

Residential Unit Number

MODEL GENERATION

0.81

SOLAR IRRADIATION Office & Co-working Total Area (m2)

Commercial Total Area (m2)

Public Service Total Area (m2)

Solar Irradiation (GWh)

312GWh

IN-USE ELECTRICITY CONSUMPTION & GENERATION

MODEL OPTIMIZATION 1

PLOT COVERAGE

Annual Electricity Consumption (GWh)

230GWh

Annual Electricity Generation (GWh)

210GWh

Vertical Randomness Seed

Plot Width: 64

H/W (Aspect Ratio)

3.02

40,000

KEY PARAMETERS ADJUSTED BY THE TOOL

According to research, the higher Plot coverage, the low energy demand in building operation. However, it has negative impact on solar irradiation & Green Amenity walkability

0.317

The Higher value represents high compactness.

460,000 510,000

2.7

0.304

FAR

480,000

0.2

ITERATION 25

2021 CPU[Ai] Studio 3

550,000 540,000

2.9

0.306

Potential Solar Panel Area(m2)

Plot Coverage

540,000

0.8

0.6

690,000 308

70,000

560,000 560,000

520,000

0.308

310 205

0.312

19,500

398,000

316 240

KEY SPATIAL ASPECTS OF COMPACTNESS

570,000

318

79

128

Low

High

Surplus Ratio (Electricity Generation/ Consumption)

MODEL OPTIMIZATION 2

Plot Length:

91.3%

Horizontal Randomness Seed 64

125

180

Low

BRIEF AND DESIGN REQUIREMENTS

Building Orientation:

Landuse Location Seed:

76 % Mixed Distribution

High

Isolated Distribution

North

South

East

Residential Unit Number

West

MODEL OPTIMIZATION 3

Green Amenity Area (m2)

Voxel Units to transform: Typology Average Footprint (L * W): Low

Residential Commercial Office + Co-working Public

Min

Max

Percentage of 5min Green Amenity walkability

High

Floor numbers for solar accessibility : 0

5

13

87

17733 393200 79%

2021 CPU[Ai] Studio 3

245

5.3.2

SELECTED ITERATION: NO.9 DATA ANALYSIS

In-depth Data Analysis Of Iteration 9 That Has The Best Performance In Overall Floor Area Ratio (FAR) Among Selected Options

0.316 220

720,000

3.1

0.314

1.0

215

210 235

314 312

710,000

700,000

230

200

Annual Electricity Consumption (GWh)

Annual Electricity Generation (GWh)

3.0

0.310

306

Solar Irradiation (GWh)

396,000

19,000

394,000

18,500

0.4 2.8

60,000

680,000

392,000 18,000

Plot Coverage

FAR

500,000

50,000

530,000 520,000

H/W (Aspect Ratio)

Green Amenity Area (m2)

Residential Unit Number

MODEL GENERATION

0.79

SOLAR IRRADIATION Office & Co-working Total Area (m2)

Commercial Total Area (m2)

Public Service Total Area (m2)

Solar Irradiation (GWh)

315GWh

IN-USE ELECTRICITY CONSUMPTION & GENERATION

MODEL OPTIMIZATION 1

FAR

Annual Electricity Consumption (GWh)

237GWh

Annual Electricity Generation (GWh)

217GWh

Vertical Randomness Seed

Plot Width: 64

H/W (Aspect Ratio)

3.15

40,000

KEY PARAMETERS ADJUSTED BY THE TOOL

This Iteration has the best performance in selected iterations. The best version in FAR is Iteration 4; however, it can not achieve over 90% surplus ratio.

0.310

The Higher value represents high compactness.

460,000 510,000

2.7

0.304

FAR

480,000

0.2

ITERATION 9

2021 CPU[Ai] Studio 3

550,000 540,000

2.9

0.306

Potential Solar Panel Area(m2)

Plot Coverage

540,000

0.8

0.6

690,000 308

70,000

560,000 560,000

520,000

0.308

310 205

0.312

19,500

398,000

316 240

KEY SPATIAL ASPECTS OF COMPACTNESS

570,000

318

80

128

Low

High

Surplus Ratio (Electricity Generation/ Consumption)

MODEL OPTIMIZATION 2

Plot Length:

91.5%

Horizontal Randomness Seed 64

125

180

Low

BRIEF AND DESIGN REQUIREMENTS

Building Orientation:

Landuse Location Seed:

85 % Mixed Distribution

High

Isolated Distribution

North

South

East

Residential Unit Number

West

MODEL OPTIMIZATION 3

Green Amenity Area (m2)

Voxel Units to transform: Typology Average Footprint (L * W): Low

Residential Commercial Office + Co-working Public

Min

Max

Percentage of 5min Green Amenity walkability

High

Floor numbers for solar accessibility : 0

7

13

88

18267 395444 85%

2021 CPU[Ai] Studio 3

245

5.3.3

SELECTED ITERATION: NO.27 DATA ANALYSIS

In-depth Data Analysis Of Iteration 27 That Has The Spatial Ratio (H/W) Tending to 1 Among Selected Options

0.316 220

720,000

3.1

0.314

1.0

215

210 235

314 312

710,000

700,000

230

200

Annual Electricity Consumption (GWh)

Annual Electricity Generation (GWh)

3.0

0.310

306

Solar Irradiation (GWh)

396,000

19,000

394,000

18,500

0.4 2.8

60,000

680,000

392,000 18,000

Plot Coverage

FAR

500,000

50,000

530,000 520,000

H/W (Aspect Ratio)

Green Amenity Area (m2)

Residential Unit Number

MODEL GENERATION

64

1.04

SOLAR IRRADIATION Office & Co-working Total Area (m2)

Commercial Total Area (m2)

Public Service Total Area (m2)

Solar Irradiation (GWh)

309 GWh

IN-USE ELECTRICITY CONSUMPTION & GENERATION

MODEL OPTIMIZATION 1

Annual Electricity Consumption (GWh)

230GWh

Annual Electricity Generation (GWh)

206GWh

Vertical Randomness Seed

Plot Width:

Aspect Ratio (H/W) represents the ratio of average building height to Average building Distance. According to research, the urban form will have better performance in energy use when the value between 0.9 to 1.1

H/W (Aspect Ratio)

2.79

40,000

KEY PARAMETERS ADJUSTED BY THE TOOL

H/W RATIO TENDING TO 1

0.309

The Higher value represents high compactness.

460,000 510,000

2.7

0.304

FAR

480,000

0.2

ITERATION 27

2021 CPU[Ai] Studio 3

550,000 540,000

2.9

0.306

Potential Solar Panel Area(m2)

Plot Coverage

540,000

0.8

0.6

690,000 308

70,000

560,000 560,000

520,000

0.308

310 205

0.312

19,500

398,000

316 240

KEY SPATIAL ASPECTS OF COMPACTNESS

570,000

318

73

128

Low

High

Surplus Ratio (Electricity Generation/ Consumption)

MODEL OPTIMIZATION 2

Plot Length:

90%

Horizontal Randomness Seed 64

114

180

Low

BRIEF AND DESIGN REQUIREMENTS

Building Orientation:

Landuse Location Seed:

65 % Mixed Distribution

High

Isolated Distribution

North

South

East

Residential Unit Number

West

MODEL OPTIMIZATION 3

Green Amenity Area (m2)

Voxel Units to transform: Typology Average Footprint (L * W): Low

Residential Commercial Office + Co-working Public

Min

Max

percentage of 5min Green Amenity walkability

High

Floor numbers for solar accessibility : 0

7

13

89

18261 398036 92%

2021 CPU[Ai] Studio 3

245

5.3.4

SELECTED ITERATION: NO.2 DATA ANALYSIS

In-depth Data Analysis Of Iteration 2 That Has The Best Performance In Electricity Generation, Solar Irradiation and Residential Unit Among Selected Options

0.316 220

720,000

3.1

0.314

1.0

215

210 235

314 312

710,000

700,000

230

200

Annual Electricity Consumption (GWh)

Annual Electricity Generation (GWh)

3.0

0.310

306

Solar Irradiation (GWh)

550,000

60,000

394,000

18,500

392,000 18,000

FAR

50,000

520,000

H/W (Aspect Ratio)

40,000

Green Amenity Area (m2)

Residential Unit Number

SOLAR IRRADIATION Office & Co-working Total Area (m2)

Commercial Total Area (m2)

Public Service Total Area (m2)

KEY PARAMETERS ADJUSTED BY THE TOOL MODEL GENERATION

ANNUAL ELECTRICITY GENERATION

64

319.9GWh

Annual Electricity Consumption (GWh)

245GWh

Annual Electricity Generation (GWh)

224GWh

Vertical Randomness Seed

75

128

Low

High

Surplus Ratio (Electricity Generation/ Consumption)

MODEL OPTIMIZATION 2

Plot Length:

RESIDENTIAL UNIT

Solar Irradiation (GWh)

IN-USE ELECTRICITY CONSUMPTION & GENERATION

MODEL OPTIMIZATION 1

Plot Width:

SOLAR IRRADIATION

0.23

H/W (Aspect Ratio) The Higher value represents high compactness.

460,000 510,000

2.7

Plot Coverage

500,000 530,000 480,000

0.2 0.304

3.13

FAR

540,000

2.8

ITERATION 2

2021 CPU[Ai] Studio 3

19,000

0.4

680,000

Potential Solar Panel Area(m2)

396,000

2.9

0.306

0.315

Plot Coverage

540,000

0.8

0.6

690,000 308

70,000

560,000 560,000

520,000

0.308

310 205

0.312

19,500

398,000

316 240

KEY SPATIAL ASPECTS OF COMPACTNESS

570,000

318

93%

Horizontal Randomness Seed 64

120

180

Low

65 % Mixed Distribution

BRIEF AND DESIGN REQUIREMENTS

Building Orientation:

Landuse Location Seed:

This Iteration only ranks at third place regarding Residential Unit Metrics. It was selected due to another two iterations c an not achieve over 90% surplus ratio.

High

Isolated Distribution

North

South

East

Residential Unit Number

West

MODEL OPTIMIZATION 3

Green Amenity Area (m2)

Voxel Units to transform: Typology Average Footprint (L * W): Low

Residential Commercial Office + Co-working Public

Min

Max

Percentage of 5min Green Amenity walkability

High

Floor numbers for solar accessibility : 0

8

13

90

19804 395035 86%

2021 CPU[Ai] Studio 3

245

5.3.5

SELECTED ITERATION: NO.21 DATA ANALYSIS

In-depth Data Analysis Of Iteration 21 That Has The Best Performance In Green Amenity Area & Walkability Among Selected Options

0.316 220

720,000

3.1

0.314

1.0

215

210 235

314 312

710,000

700,000

230

200

Annual Electricity Consumption (GWh)

Annual Electricity Generation (GWh)

3.0

0.310

306

Solar Irradiation (GWh)

19,000

394,000

2.8

18,500

60,000

392,000 18,000

FAR

Plot Coverage

500,000

50,000

530,000 520,000

H/W (Aspect Ratio)

40,000

Green Amenity Area (m2)

Residential Unit Number

SOLAR IRRADIATION Office & Co-working Total Area (m2)

Commercial Total Area (m2)

Public Service Total Area (m2)

KEY PARAMETERS ADJUSTED BY THE TOOL MODEL GENERATION

GREEN AMENITY AREA

64

Solar Irradiation (GWh)

308.8GWh

IN-USE ELECTRICITY CONSUMPTION & GENERATION

MODEL OPTIMIZATION 1

Annual Electricity Consumption (GWh)

231GWh

Annual Electricity Generation (GWh)

208GWh

Vertical Randomness Seed

Plot Width:

GREEN AMENITY WALKABILITY

0.95

H/W (Aspect Ratio) The Higher value represents high compactness.

460,000 510,000

2.7

0.304

3.07

FAR

480,000

0.2

ITERATION 21

2021 CPU[Ai] Studio 3

550,000 540,000

0.4

680,000

Potential Solar Panel Area(m2)

396,000

2.9

0.306

0.309

Plot Coverage

540,000

0.8

0.6

690,000 308

70,000

560,000 560,000

520,000

0.308

310 205

0.312

19,500

398,000

316 240

KEY SPATIAL ASPECTS OF COMPACTNESS

570,000

318

80

128

Low

High

Surplus Ratio (Electricity Generation/ Consumption)

MODEL OPTIMIZATION 2

Plot Length:

90%

Horizontal Randomness Seed 64

120

180

Low

BRIEF AND DESIGN REQUIREMENTS

Building Orientation:

Landuse Location Seed:

50 % Mixed Distribution

High

Isolated Distribution

North

South

East

Residential Unit Number

West

MODEL OPTIMIZATION 3

Green Amenity Area (m2)

Voxel Units to transform: Typology Average Footprint (L * W): Low

Residential Commercial Office + Co-working Public

Min

Max

percentage of 5min Green Amenity walkability

High

Floor numbers for solar accessibility : 0

5

13

91

17928 401005 100%

2021 CPU[Ai] Studio 3

245

5.3.6

SELECTED ITERATION: NO.23 DATA ANALYSIS

In-depth Data Analysis Of Iteration 23 That Has The Best Performance In Commercial Area Among Selected Options

0.316 220

720,000

3.1

0.314

1.0

215

210 235

314 312

710,000

700,000

230

200

Annual Electricity Consumption (GWh)

Annual Electricity Generation (GWh)

3.0

0.310

306

Solar Irradiation (GWh)

19,000

394,000

2.8

18,500

60,000

392,000 18,000

FAR

Plot Coverage

500,000

50,000

530,000 520,000

H/W (Aspect Ratio)

40,000

Green Amenity Area (m2)

Residential Unit Number

SOLAR IRRADIATION Office & Co-working Total Area (m2)

Commercial Total Area (m2)

Public Service Total Area (m2)

KEY PARAMETERS ADJUSTED BY THE TOOL MODEL GENERATION

COMMERCIAL AREA MAX

Solar Irradiation (GWh)

310.3GWh

IN-USE ELECTRICITY CONSUMPTION & GENERATION

MODEL OPTIMIZATION 1

Annual Electricity Consumption (GWh)

232GWh

Annual Electricity Generation (GWh)

214GWh

Vertical Randomness Seed

Plot Width: 64

0.1

H/W (Aspect Ratio) The Higher value represents high compactness.

460,000 510,000

2.7

0.304

2.8

FAR

480,000

0.2

ITERATION 23

2021 CPU[Ai] Studio 3

550,000 540,000

0.4

680,000

Potential Solar Panel Area(m2)

396,000

2.9

0.306

0.31

Plot Coverage

540,000

0.8

0.6

690,000 308

70,000

560,000 560,000

520,000

0.308

310 205

0.312

19,500

398,000

316 240

KEY SPATIAL ASPECTS OF COMPACTNESS

570,000

318

85

128

Low

High

Surplus Ratio (Electricity Generation/ Consumption)

MODEL OPTIMIZATION 2

Plot Length:

92%

Horizontal Randomness Seed 64

118

180

Low

BRIEF AND DESIGN REQUIREMENTS

Building Orientation:

Landuse Location Seed:

55 % Mixed Distribution

High

Isolated Distribution

North

South

East

Residential Unit Number

West

MODEL OPTIMIZATION 3

Green Amenity Area (m2)

Voxel Units to transform: Typology Average Footprint (L * W): Low

Residential Commercial Office + Co-working Public

Min

Max

percentage of 5min Green Amenity walkability

High

Floor numbers for solar accessibility : 0

4

Commercial Area (m2) 13

92

18130 392645 81% 566329

2021 CPU[Ai] Studio 3

245

5.3.7

SELECTED ITERATION: NO.28 DATA ANALYSIS

In-depth Data Analysis Of Iteration 28 That Has The Best Performance In Commercial Area Among Selected Options

0.316 220

720,000

3.1

0.314

1.0

215

210 235

314 312

710,000

700,000

230

200

Annual Electricity Consumption (GWh)

Annual Electricity Generation (GWh)

3.0

0.310

306

Solar Irradiation (GWh)

19,000

394,000

2.8

18,500

60,000

392,000 18,000

FAR

Plot Coverage

500,000

50,000

530,000 520,000

H/W (Aspect Ratio)

40,000

Green Amenity Area (m2)

Residential Unit Number

SOLAR IRRADIATION Office & Co-working Total Area (m2)

Commercial Total Area (m2)

Public Service Total Area (m2)

KEY PARAMETERS ADJUSTED BY THE TOOL MODEL GENERATION

OFFICE AREA MAX

Solar Irradiation (GWh)

308.5GWh

IN-USE ELECTRICITY CONSUMPTION & GENERATION

MODEL OPTIMIZATION 1

Annual Electricity Consumption (GWh)

230GWh

Annual Electricity Generation (GWh)

212GWh

Vertical Randomness Seed

Plot Width: 64

0.15

H/W (Aspect Ratio) The Higher value represents high compactness.

460,000 510,000

2.7

0.304

2.96

FAR

480,000

0.2

ITERATION 28

2021 CPU[Ai] Studio 3

550,000 540,000

0.4

680,000

Potential Solar Panel Area(m2)

396,000

2.9

0.306

0.307

Plot Coverage

540,000

0.8

0.6

690,000 308

70,000

560,000 560,000

520,000

0.308

310 205

0.312

19,500

398,000

316 240

KEY SPATIAL ASPECTS OF COMPACTNESS

570,000

318

81

128

Low

High

Surplus Ratio (Electricity Generation/ Consumption)

MODEL OPTIMIZATION 2

Plot Length:

92%

Horizontal Randomness Seed 64

118

180

Low

BRIEF AND DESIGN REQUIREMENTS

Building Orientation:

Landuse Location Seed:

60 % Mixed Distribution

High

Isolated Distribution

North

South

East

Residential Unit Number

West

MODEL OPTIMIZATION 3

Green Amenity Area (m2)

Voxel Units to transform: Typology Average Footprint (L * W): Low

Residential Commercial Office + Co-working Public

Min

Max

percentage of 5min Green Amenity walkability

High

Floor numbers for solar accessibility : 0

Office Area (m2) 7

13

93

17956 399529 81% 579993

2021 CPU[Ai] Studio 3

245

5.3.8

SELECTED ITERATION: NO.11 DATA ANALYSIS

In-depth Data Analysis Of Iteration 11 That Has The Best Performance In Commercial Area Among Selected Options

0.316 220

720,000

3.1

0.314

1.0

215

210 235

314 312

710,000

700,000

230

200

Annual Electricity Consumption (GWh)

Annual Electricity Generation (GWh)

3.0

0.310

306

Solar Irradiation (GWh)

19,000

394,000

2.8

18,500

392,000 18,000

60,000

FAR

Plot Coverage

500,000

50,000

530,000 520,000

2.99

H/W (Aspect Ratio)

Green Amenity Area (m2)

Residential Unit Number

MODEL GENERATION

0.2

SOLAR IRRADIATION Office & Co-working Total Area (m2)

Commercial Total Area (m2)

Public Service Total Area (m2)

Solar Irradiation (GWh)

313.1GWh

IN-USE ELECTRICITY CONSUMPTION & GENERATION

MODEL OPTIMIZATION 1

Annual Electricity Consumption (GWh)

237GWh

Annual Electricity Generation (GWh)

217GWh

Vertical Randomness Seed

Plot Width: 64

H/W (Aspect Ratio)

40,000

KEY PARAMETERS ADJUSTED BY THE TOOL

PUBLIC SERVICE MAX

FAR

The Higher value represents high compactness.

460,000 510,000

2.7

ITERATION 11

2021 CPU[Ai] Studio 3

550,000

480,000

0.2 0.304

0.31

540,000

0.4

680,000

Potential Solar Panel Area(m2)

396,000

2.9

0.306

Plot Coverage

540,000

0.8

0.6

690,000 308

70,000

560,000 560,000

520,000

0.308

310 205

0.312

19,500

398,000

316 240

KEY SPATIAL ASPECTS OF COMPACTNESS

570,000

318

78

128

Low

High

Surplus Ratio (Electricity Generation/ Consumption)

MODEL OPTIMIZATION 2

Plot Length:

91.5%

Horizontal Randomness Seed 64

120

180

Low

BRIEF AND DESIGN REQUIREMENTS

Building Orientation:

Landuse Location Seed:

54 % Mixed Distribution

High

Isolated Distribution

North

South

East

Residential Unit Number

West

MODEL OPTIMIZATION 3

Green Amenity Area (m2)

Voxel Units to transform: Typology Average Footprint (L * W): Low

Residential Commercial Office + Co-working Public

Min

Max

Percentage of 5min Green Amenity walkability

High

Floor numbers for solar accessibility : 0

Public Service (m2) 7

13

94

18519 390134 78% 74763

2021 CPU[Ai] Studio 3

245

5.3.9

SELECTED ITERATION: NO.8 DATA ANALYSIS

In-depth Data Analysis Of Iteration 8 That Has the best Balance Correlation Between Different Criteria Among Selected Options

0.316 220

720,000

3.1

0.314

1.0

215

210 235

314 312

710,000

700,000

230

200

Annual Electricity Consumption (GWh)

Annual Electricity Generation (GWh)

3.0

0.310

306

Solar Irradiation (GWh)

19,000

394,000

2.8

18,500

392,000 18,000

60,000

FAR

Plot Coverage

500,000

50,000

530,000 520,000

40,000

H/W (Aspect Ratio)

Green Amenity Area (m2)

Residential Unit Number

Office & Co-working Total Area (m2)

Commercial Total Area (m2)

Public Service Total Area (m2)

MODEL GENERATION

MODEL OPTIMIZATION 1 Vertical Randomness Seed

Plot Width:

This Balance Iteration demonstrate the potential of generating an urban form that can deal with low energy demand compact urban form; sufficient solar irradiation generated electricity and ideal green amenity distribution simultaneously

64

79

128

Low

High

117

180

Low

60 % Mixed Distribution

High

Isolated Distribution

North

South

East

MODEL OPTIMIZATION 3

Office + Co-working Public

Min

Max

High

Floor numbers for solar accessibility : 0

7

317.7GWh

Annual Electricity Consumption (GWh)

236GWh

Annual Electricity Generation (GWh)

222GWh

percentage of 5min Green Amenity walkability

Typology Average Footprint (L * W): Low

Solar Irradiation (GWh)

Green Amenity Area (m2)

West

Voxel Units to transform:

Residential

SOLAR IRRADIATION

Residential Unit Number

Building Orientation:

Landuse Location Seed:

0.51

94%

BRIEF AND DESIGN REQUIREMENTS

Horizontal Randomness Seed 64

H/W (Aspect Ratio)

Surplus Ratio (Electricity Generation/ Consumption)

MODEL OPTIMIZATION 2

Plot Length:

Commercial

3.02

IN-USE ELECTRICITY CONSUMPTION & GENERATION

KEY PARAMETERS ADJUSTED BY THE TOOL

BALANCE IN EVERY METRICS

FAR

The Higher value represents high compactness.

460,000 510,000

2.7

ITERATION 8

2021 CPU[Ai] Studio 3

550,000

480,000

0.2 0.304

0.31

540,000

0.4

680,000

Potential Solar Panel Area(m2)

396,000

2.9

0.306

Plot Coverage

540,000

0.8

0.6

690,000 308

70,000

560,000 560,000

520,000

0.308

310 205

0.312

19,500

398,000

316 240

KEY SPATIAL ASPECTS OF COMPACTNESS

570,000

318

18277 395489 91%

Commercial Area (m2)

548506

Office Area (m2)

558514

13

Public Service (m2)

95

54426

2021 CPU[Ai] Studio 3

245

5.4.1

FURTHER COMPARISON 1: BETWEEN TWO OPTIMAL ITERATION

A comparison between the Iteration 2 (Best in Electricity Generation & Solar Irradiation) and Iteration 8 (Balance Option in All criteria Metrics)

ITERATION 8

245

570,000

318 220

720,000

0.314

3.1

1.0

316 215

240

710,000

314 312

210 235

700,000

0.312

308

230

0.306

200

19,000

540,000 550,000

60,000

540,000 394,000

500,000

18,500

530,000

0.4 2.8

50,000

480,000

392,000

520,000

18,000

0.2 0.304

70,000

560,000

0.6

680,000

306

396,000

560,000

0.8

2.9

690,000

19,500

520,000

0.310 0.308

310 205

3.0

398,000

460,000

40,000

510,000

2.7

1. These two iterations can be recommended as the optimal option to achieve the ‘Zero Carbon Future‘ design target as they both have a near 100% electricity surplus ratio (Annual electricity generation / Annual electricity consumption).

0.316 245

SOLAR IRRADIATION

2021 CPU[Ai] Studio 3

RESIDENTIAL UNIT

720,000

0.314

3.1

1.0

316 215

240

710,000

314 312

210 235

700,000

0.312

308

230

200

396,000

19,000

Office + Co-working Public

Plot Coverage FAR H/W (Aspect Ratio)

0.315

Annual Electricity Consumption (GWh)

245GWh

3.13 0.23

Annual Electricity Generation (GWh)

224GWh

BRIEF AND DESIGN REQUIREMENTS Residential Unit Number

19804

Green Amenity Area (m2)

395035

percentage of 5min Green Amenity walkability

86%

The Higher value represents high compactness.

Commercial Area (m2)

SOLAR IRRADIATION Solar Irradiation (GWh)

319GWh

Surplus Ratio (Electricity Generation /Consumption)

93%

Office Area (m2) Public Service (m2)

60,000

394,000

500,000

18,500

530,000

0.4 2.8

50,000

480,000

392,000

520,000

18,000

460,000

40,000

510,000

2.7

BALANCE IN EVERY METRICS This Balance Iteration demonstrate the potential of generating an urban form that can deal with low energy demand compact urban form; sufficient solar irradiation generated electricity and ideal green amenity distribution simultaneously

Residential

Commercial

IN-USE ELECTRICITY CONSUMPTION & GENERATION

550,000 540,000

Residential

KEY SPATIAL ASPECTS OF COMPACTNESS

70,000

540,000

0.2 0.304

560,000 560,000

0.8

2.9

0.306

19,500

0.6

680,000

306

398,000

520,000

0.310

690,000

205

3.0

0.308

310

2. The difference of H/W ratio (Average Building Height / Average Building Distance) between two iterations dose reflects the researched principles: when the H/W ratio at the range of 0.9 to 1.1, The building has lower inuse energy demand.

Iteration 2 ----Average Building Annual Electricity consumption: 12912 KWh Iteration 8 ----Average Building Annual Electricity consumption: 12371 KWh

ELECTRICITY GENERATION

570,000

318 220

573470

485265 59128

3. Although the higher compactness value (FAR) dose contributes to generating more residential units and more overall solar irradiation in Iteration 2, it negatively impacts green amenity area and distribution. This can also be reflected by the higher Surplus ratio in Iteration 8, which could benefit from less overshadowing.

4. By considering the future scenario, Iteration 8 has more potential to adopt the hybrid typology strategy as it has better Average solar irradiation potential on building facade

Commercial Office + Co-working Public

KEY SPATIAL ASPECTS OF COMPACTNESS Plot Coverage

0.31

IN-USE ELECTRICITY CONSUMPTION & GENERATION Annual Electricity Consumption (GWh)

236GWh

3.02

FAR H/W (Aspect Ratio)

0.51

Annual Electricity Generation (GWh)

222GWh

BRIEF AND DESIGN REQUIREMENTS Residential Unit Number

18277

Green Amenity Area (m2)

395489

percentage of 5min Green Amenity walkability

91%

The Higher value represents high compactness.

Commercial Area (m2)

SOLAR IRRADIATION Solar Irradiation (GWh)

96

317.7GWh

Surplus Ratio (Electricity Generation /Consumption)

94%

548506

Office Area (m2)

558514

Public Service (m2)

54426

2021 CPU[Ai] Studio 3

ITERATION 2 0.316

5.4.2

FURTHER COMPARISON 2: WITH UNSELECTED ITERATION

A comparison between the Iteration 2 (Best in Electricity Generation & Solar Irradiation) and Unselected Iteration 1 (Best in Residential Unit & Worst in Electricity Consumption)

ITERATION 1

245

570,000

318 220

720,000

0.314

3.1

1.0

316 215

240

710,000

314 312

210 235

700,000

0.312

308

230

0.306

200

19,000

540,000 550,000

60,000

540,000 394,000

500,000

18,500

530,000

0.4 2.8

50,000

480,000

392,000

520,000

18,000

0.2 0.304

70,000

560,000

0.6

680,000

306

396,000

560,000

0.8

2.9

690,000

19,500

520,000

0.310 0.308

310 205

3.0

398,000

460,000

40,000

510,000

2.7

1. Iteration 1 is one of the extreme options regarding the Residential Unit number and the Electricity consumption. It has a similar FAR, Plot Coverage, Residential Unit with Iteration 2.

The lower Solar Irradiation and Electricity Generation causes its lower Surplus Ratio.

0.316 245

215

240

2021 CPU[Ai] Studio 3

FAR H/W (Aspect Ratio)

IN-USE ELECTRICITY CONSUMPTION & GENERATION Annual Electricity Consumption (GWh)

245GWh

3.13 0.23

319GWh

1.0

312

210

700,000

0.312

308

230

0.306

200

19,000

540,000 60,000

540,000 394,000

500,000

18,500

2.8

50,000

530,000

0.4

480,000

392,000

520,000

18,000

0.2 0.304

550,000

0.6

680,000

306

396,000

70,000

560,000 560,000

0.8

2.9

690,000

19,500

520,000

0.310 0.308

310 205

3.0

398,000

460,000

40,000

510,000

2.7

Annual Electricity Generation (GWh)

224GWh

ELECTRICITY CONSUMPTION UNIT MAX

Residential

Commercial

Commercial

Office + Co-working

Office + Co-working

Public

Public

BRIEF AND DESIGN REQUIREMENTS Residential Unit Number

19804

Green Amenity Area (m2)

395035

percentage of 5min Green Amenity walkability

86%

KEY SPATIAL ASPECTS OF COMPACTNESS Plot Coverage

0.315

IN-USE ELECTRICITY CONSUMPTION & GENERATION Annual Electricity Consumption (GWh)

249GWh

3.14

FAR H/W (Aspect Ratio)

0.60

Annual Electricity Generation (GWh)

219GWh

BRIEF AND DESIGN REQUIREMENTS Residential Unit Number

19844

Green Amenity Area (m2)

390134

percentage of 5min Green Amenity walkability

80%

The Higher value represents high compactness.

Commercial Area (m2)

Solar Irradiation (GWh)

3.1

Residential

The Higher value represents high compactness.

SOLAR IRRADIATION

710,000

314

235

2. By Comparing the two Iterations, the H/W ratio can lead to significant change both in Solar Irradiation and Green Amenity Area when the Iterations’ FAR and Plot Coverage are similar.

However, its impact on In-use energy demand is weakened

RESIDENTIAL UNIT

0.315

0.314

RESIDENTIAL UNIT MAX

SOLAR IRRADIATION

Plot Coverage

720,000 316

ELECTRICITY GENERATION

KEY SPATIAL ASPECTS OF COMPACTNESS

570,000

318 220

Surplus Ratio (Electricity Generation /Consumption)

573470

Commercial Area (m2)

SOLAR IRRADIATION 93%

Office Area (m2) Public Service (m2)

485265

Solar Irradiation (GWh)

59128

97

314.8GWh

Surplus Ratio (Electricity Generation /Consumption)

87.9%

533812

Office Area (m2)

515094

Public Service (m2)

65616

2021 CPU[Ai] Studio 3

ITERATION 2 0.316

5.4.3

FURTHER COMPARISON 3: WITH UNSELECTED ITERATION

A comparison between the Iteration 9 (Best FAR Among Selected Iterations) and Unselected Iteration 4 (Best FAR Among all generated outcome)

ITERATION 4

245

570,000

318 220

720,000

0.314

3.1

1.0

316 215

240

710,000

314 312

210 235

700,000

0.312

308

230

0.306

200

19,000

540,000 550,000

60,000

540,000 394,000

500,000

18,500

530,000

0.4 2.8

50,000

480,000

392,000

520,000

18,000

0.2 0.304

70,000

560,000

0.6

680,000

306

396,000

560,000

0.8

2.9

690,000

19,500

520,000

0.310 0.308

310 205

3.0

398,000

460,000

40,000

510,000

2.7

2021 CPU[Ai] Studio 3

Commercial

FAR H/W (Aspect Ratio)

0.310

IN-USE ELECTRICITY CONSUMPTION & GENERATION Annual Electricity Consumption (GWh)

237GWh

3.15 0.79

Annual Electricity Generation (GWh)

217GWh

315GWh

0.314

3.1

1.0

316 215

240

710,000

314 312

210 235

700,000

0.312

308

230

0.306

200

19,000

540,000 60,000

540,000 394,000

500,000

18,500

2.8

480,000

392,000

520,000

18,000

460,000

MAX FAR

Residential Commercial Public

Residential Unit Number

18267

Green Amenity Area (m2)

395444

percentage of 5min Green Amenity walkability

85%

40,000

510,000

2.7

Office + Co-working

BRIEF AND DESIGN REQUIREMENTS

50,000

530,000

0.4

0.2 0.304

550,000

0.6

680,000

306

396,000

70,000

560,000 560,000

0.8

2.9

690,000

19,500

520,000

0.310 0.308

310 205

3.0

398,000

Public

KEY SPATIAL ASPECTS OF COMPACTNESS Plot Coverage

0.31

IN-USE ELECTRICITY CONSUMPTION & GENERATION Annual Electricity Consumption (GWh)

242GWh

3.2

FAR H/W (Aspect Ratio)

0.39

Annual Electricity Generation (GWh)

215GWh

BRIEF AND DESIGN REQUIREMENTS Residential Unit Number

19031

Green Amenity Area (m2)

395028

percentage of 5min Green Amenity walkability

88%

The Higher value represents high compactness.

Commercial Area (m2)

Solar Irradiation (GWh)

720,000

Office + Co-working

The Higher value represents high compactness.

SOLAR IRRADIATION

570,000

318 220

3. The main difference in this comparison is H/W Ratio. The higher H/W Ratio of Iteration 9 benefits its overall Solar Irradiation as the Urban form has a higher value in building average height.

Residential

Plot Coverage

245

2. As researched principles indicated, the positive impact of FAR on in-usage energy efficiency can be found by comparing Average building electricity consumption.

Iteration 4, with higher FAR, has a lower value (12700KWh) than Iteration 9 (12900KWh)

MAX FAR IN SELECTION

KEY SPATIAL ASPECTS OF COMPACTNESS

1. The Iteration 4 cannot be selected by tool due to its surplus ratio (Annual electricity generation / Annual electricity consumption) that is less than 90%. However, It has max FAR among all iterations.

0.316

Surplus Ratio (Electricity Generation /Consumption)

564179

Commercial Area (m2)

SOLAR IRRADIATION 91.5%

Office Area (m2) Public Service (m2)

571727

Solar Irradiation (GWh)

43919

98

312.4GWh

Surplus Ratio (Electricity Generation /Consumption)

88%

517622

Office Area (m2)

574047

Public Service (m2)

65244

2021 CPU[Ai] Studio 3

ITERATION 9 0.316

5.4.4

FURTHER COMPARISON 4: WITH UNSELECTED ITERATION

A comparison between the Iteration 27 (Spatial Ratio (H/W) Tending to 1) and Unselected Iteration 22 (Spatial Ratio (H/W) Tending to 1)

ITERATION 22

245

570,000

318 220

720,000

0.314

3.1

1.0

316 215

240

710,000

314 312

210 235

700,000

0.312

308

230

0.306

200

19,000

540,000 550,000

60,000

540,000 394,000

500,000

18,500

530,000

0.4 2.8

50,000

480,000

392,000

520,000

18,000

0.2 0.304

70,000

560,000

0.6

680,000

306

396,000

560,000

0.8

2.9

690,000

19,500

520,000

0.310 0.308

310 205

3.0

398,000

460,000

40,000

510,000

2.7

1. The difference these two iterations’ Surplus Ratio demonstrates that the FAR has contradictory impact on Building in-use Electricity consumption and Solar Irradiations when the H/W Ratio of two iterations is similar.

0.316 245

720,000

0.314

3.1

1.0

316 215

240

710,000

314 312

210 235

700,000

0.312

308

230

200

396,000

19,000

70,000

540,000 550,000

60,000

540,000 394,000

500,000

18,500

530,000

0.4 2.8

50,000

480,000

392,000

520,000

18,000

0.2 0.304

560,000 560,000

0.8

2.9

0.306

19,500

0.6

680,000

306

398,000

520,000

0.310

690,000

205

3.0

0.308

310

1.1. High Value of FAR has more obvious negative impact on Solar Irradiation than the positive impact on Building in-use Electricity consumption

H/W RATIO TENDING TO 1

570,000

318 220

460,000

40,000

510,000

2.7

H/W RATIO TENDING TO 1

2021 CPU[Ai] Studio 3

2. The Building Form Optimization (Position Optimization & Building Solar Envelope Optimization) also contribute to enhance the solar irradiation significantly when the H/W Ratio is similar

KEY SPATIAL ASPECTS OF COMPACTNESS Plot Coverage FAR H/W (Aspect Ratio)

0.309

IN-USE ELECTRICITY CONSUMPTION & GENERATION Annual Electricity Consumption (GWh)

230GWh

2.79 1.04

Annual Electricity Generation (GWh)

206GWh

Residential

Residential

Commercial

Commercial

Office + Co-working

Office + Co-working

Public

Public

BRIEF AND DESIGN REQUIREMENTS Residential Unit Number

18261

Green Amenity Area (m2)

398036

Percentage of 5min Green Amenity walkability

92%

The Higher value represents high compactness.

Solar Irradiation (GWh)

309GWh

Plot Coverage

0.30

IN-USE ELECTRICITY CONSUMPTION & GENERATION Annual Electricity Consumption (GWh)

233GWh

2.87

FAR H/W (Aspect Ratio)

0.99

Annual Electricity Generation (GWh)

201GWh

BRIEF AND DESIGN REQUIREMENTS Residential Unit Number

18442

Green Amenity Area (m2)

396381

percentage of 5min Green Amenity walkability

89%

The Higher value represents high compactness.

Commercial Area (m2)

SOLAR IRRADIATION

KEY SPATIAL ASPECTS OF COMPACTNESS

Surplus Ratio (Electricity Generation /Consumption)

563569

Commercial Area (m2)

SOLAR IRRADIATION 90%

Office Area (m2) Public Service (m2)

496727

Solar Irradiation (GWh)

55408

99

305.5GWh

Surplus Ratio (Electricity Generation /Consumption)

86%

538699

Office Area (m2)

493171

Public Service (m2)

55676

2021 CPU[Ai] Studio 3

ITERATION 27 0.316

Chapter 5

CONCLUSION ITERATION 2

So far, we have clarified the working mechanism of the design tool in the final step of design outcome recommendation through the screening and comparative analysis of the generated iteration. And further analyse the previously researched spatial aspects related to building in-use energy demand (such as FAR, Plot Coverage, H/W) to study the impact on the realization of the low carbon urban form. Moreover, key parameters of each optimal iterations were also recorded, which can be the reference for further design development

ELECTRICITY GENERATION

BALANCE IN EVERY METRICS

RESIDENTIAL UNIT

Residential

Residential

Commercial

Commercial

Office + Co-working

Office + Co-working

Public

Public

Optimal iteration for primarily achieving ‘Zero Carbon Future‘ design

ITERATION 25

ITERATION 9

PLOT COVERAGE

ITERATION 27

H/W RATIO TENDING TO 1

FAR

According to research, the higher Plot coverage, the low energy demand in building operation.

However, it has negative impact on solar irradiation & Green Amenity walkability

USER SELECTION

This Iteration has the best performance in selected iterations. The best version in FAR is Iteration 4; however, it can not achieve over 90% surplus ratio.

Aspect Ratio (H/W) represents the ratio of average building height to Average building Distance.

According to research, the urban form will have better performance in energy use when the value between 0.9 to 1.1

Residential

Residential

Residential

Commercial

Commercial

Commercial

Office + Co-working

Office + Co-working

Office + Co-working

Public

Public

Public

ITERATION 21

ITERATION 23

GREEN AMENITY AREA

ITERATION 28

COMMERCIAL AREA MAX

OFFICE AREA MAX

GREEN AMENITY WALKABILITY

Residential

Residential

Residential

Commercial

Commercial

Commercial

Office + Co-working

Office + Co-working

Office + Co-working

Public

Public

Public

Optimal iteration for other design target under ‘Zero Carbon Future‘ design condition

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SOLAR IRRADIATION

TOOL RECOMMENDATION

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Based on the analysis data, we first divide the iteration of the design tool generates into two categories (also the final classification and recommendation mechanism of the design tool): 1. Its annual electricity generation / annual electricity consumption ratio >= 90%. 2 The ratio is <90 %. The first category is considered to have greater potential to achieve energy surplus and the ‘zero carbon future’ design targets by combining with the hybrid typology strategy proposed in the previous chapter. We also selected 8 optimal iterations for in-depth analysis in this category based on the previously preset criteria metrics. For the unselected iterations, we also choose the one with extreme performance in the criteria metric to compare with the previously filtered iterations.

ITERATION 8

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Manchester school of architecture

THE GATE WAY TO ZERO CARBON CITY

Chapter 6

6.1.1 INTERFACE REVIEW - WORKFLOW

TOOL INTERFACE REVIEW & MANNUAL

6.2.1 INTERFACE REVIEW - INPUT 6.2.2 INTERFACE REVIEW - SETTING 6.2.3 INTERFACE REVIEW - ITERATION RESULT 6.2.4 INTERFACE REVIEW - OPTIMAL ITERATION 6.2.5 INTERFACE REVIEW - OUTCOMES

According to our research and working mechanism, we developed and designed a set of software, and designed a simple and easy-to-understand operation interface for it. In this chapter, we will introduce our software work flow and the use of Interface in detail.

101

6.2.1 6.1.1

INTERFACE REVIEW - WORKFLOW

Start

In this section, There is the Detail introduction of the interface workflow of the tool

Input Project File

User

Site Geometry

Building Road Green space Water area Railway

Public Transport Station -Location -Type Weather Information Site Location

Step-1

User setting

We have developed a software based on our working mechanism, and designed a simple Workflow to help our users operate this tool more easily.

Site Boundary Major Road Centreline Main Green Area Water area (on site) Railway(on site)

There are two main parts. The first part requires the user to manually set it up, and the second part is done entirely by our tool. There are 6 steps： Step 1:

Estimated Population Land Use Ratio Railway Setting Specifies Element

Set

Users will import relevant data and files of their own projects into our tool, including the preliminary design document of the project, basic information of the site and weather information.

Set Specifies Parameter

Step-2

Step 2:

Users need to set their own preferences as well as their desired target. For example, the estimated population, land use ratio, etc.

Step 3:

Our tool will start generating results automatically under our mechanism, based on the files provided by the user and the preferences set.

Step-3

The tool will automatically export the preliminary iteration results.

Step 5:

Generated Iteration Result

According to the rules we set, the tool will automatically filter the preliminary iteration results, and finally output 8 recommendation results.

Step-4

Output Iteration Result

Step 6:

When the user selects one of the Iteration Results, the interface will display the specific data of this Iteration result, which will produce the specific data of Urban, Block and Building level as well as the visual results.

Selected Iteration Result Detail of Selected Iteration Result

Select User

Step-5

Filter out Recommendation Result

Urban

Iteration Result Iteration Attributes Solar Irradiation Visualization Program Distribution Visualization Near Future Scenario Far Future Scenario

Automatic Generation

Auto-generation

By Tool

Step 4:

Block

102

Optimized Building Configuration: - Building Height - Building Position - Solar Irradiation

Building

Building Form of Hybrid Typology Building Data and Performance The Expected Performance of Building Performance (Under Future Scenario)

Outcomes

6.2.1

INTERFACE REVIEW - INPUT

This step will show you how to import data through interface and the specific data that needs to be imported.

Step-1

In this step, the user needs to import two types of data, namely, Project File and Site Geometry. Project File is part of the content designed by the user in advance, and we will process the information in site according to these contents.

In Site Geometry, users need to import some information surrounding site, as well as geographic and climate information, which is an important basis for our tool to analyse site. For example, climate information will be the key to the sunshine analysis of the buildings within Site.

Input:

User setting

Project File

Step-2

2021 CPU[Ai] Studio 3

Site Boundary Major Road Centreline Main Green Area Water area (on site) Railway(on site) Site Geometry Building Road Green space Water area Railway

Public Transport Station -Location -Type Weather Information Site Location Project File Input

Step-3

2021 CPU[Ai] Studio 3

Automatic Generation

Before using the software, set the site design principle in advance, such as the location and shape of the main green space, the direction of the main road, site boundary, water area and railway, etc. This information is then imported in layers.

Site Geometry Input

Step-4

Input basic model of site location, and weather data in layers.

Step-5 Tool Interface Display_Step 1

Outcomes

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6.2.2

INTERFACE REVIEW - SETTING

In this step, users set some elements in Site according to their preferences and expectations.

In this interface, users will set the Settings according to their preferences and expectations according to several specific site factors. Like population expectations and preservation of key buildings and landmarks on the site.

In addition, users also need to set the location of the transportation hub specifically.

User setting

The gate way to ZERO carbon city Step-2 Setting:

Transportation Hub Setting After importing the file, mark the location on the map where you want to set the Transportation Hub.

Set Specifies Parameter Estimated Population Land Use Ratio Transportation Hub Railway Setting Specifies Element

Estimated Population Setting Users are required to provide expectations and estimates of the future population, which include three numbers representing three different time periods in the future.

Estimated Population Setting Planned population : Near future population :

Step-3 Automatic Generation

Far Future Population :

Land Use Ratio Setting

Land Use Ratio sETTING

2021 CPU[Ai] Studio 3

Automatic Generation

rESIDENTIAL aREA :

10%

cOMMERCIAL Area :

10%

Office Area : Public Service Area :

20% 5%

tRANSPORTATION huB Setting 71% 14%

70%

11%

45%

11%

Users can provide their expected land use Ratio plan, including the proportion and area of ratio to Residential area, Commercial area, Office area, Public Service area.

10%

Railway Setting Step-4

Public transportation HUb :

90%

Spacifies Element Setting

Elevated :

Water aREA :

Ground :

RAILWAY :

Under ground :

Historical Building :

Specifies Element Setting Select whether some elements in the site, such as water and railway, need to be retained. It provides reference for the subsequent iteration operation.

Generate

Railway Setting Users need to set their own preferences for the railway setting in Site, whether it is elevated, level with the ground or underground.

Step-5 Tool Interface Display_Step 2

Outcomes

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Step-1

6.2.3

INTERFACE REVIEW - ITERATION RESULT

The user will see some preliminary Iteration results at this step. However, these results will not be provided for users to choose, and will only be used as line charts for users to compare.

At this step, the tool will generate some preliminary iteration results.

Step-1

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User setting Step-2

Step-3 Automatic Generation Generated Iteration Line Chart

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Automatic Generation

The system will automatically generate preliminary results according to our mechanism, and the data of each result will be displayed in the form of line chart.

Step-4 Output: Iteration Result Generated Iteration Result

Step-5

Tool Interface Display_Step 2

Outcomes

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6.2.4

INTERFACE REVIEW -OPTIMAL ITERATION

Based on the preliminary generation of Iteration results, the tool will select several iteration results that perform well in all aspects.

The tool will select several specific iteration results according to our mechanism. These specific iteration results will be displayed in our interface for users to choose. The details of these iteration results will also be shown in our interface.

Iteration Result Selection

User setting

Iteration Result Information The tool displays a brief description of the Iteration Result that describes the Iteration feature.

Step-2

Step-3 Line chart of Iteration Result

2021 CPU[Ai] Studio 3

Automatic Generation

In the line chart showing the iteration result, we will highlight the broken line of this iteration. This will provide the user with a clearer comparison with other iteration results.

Step-4

Iteration Attribute The attributes of each iteration result are displayed in the float window in the lower right corner.

Step-5 Optimal Iteration:

Tool Interface Display_Step 3

Recommendation Result Selected Iteration Result Detail of Selected Iteration Result

Model Type Switch We offer three display modes to choose from: Normal, Solar Irradiation and Program. The three modes will display three different visual models respectively, to show the Solar Irradiation situation of the city and the distribution of the programs.

Outcomes

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We provide eight selected “Iteration Result” buttons. When the user clicks a different one, the user can view the Iteration Result model in the interface.

Step-1

6.2.5

INTERFACE REVIEW - OUTCOMES-1

In this step, we will provide more specific information about an iteration and outcome based on the user’s choice.

Our outcomes are divided into three levels. First, we will introduce the outcomes of urban level. This interface will show the specific information of iteration and other visual content provided by the user after selection. For example, the program model and the solar irradiation model.

The eight selected iteration results, the features of each iteration, and a brief description of the iteration results.

Step-2

Near Future Scenario

User setting

Step-1

For each Iteration feature, we provide a Near Future Scenario that shows that, with this Iteration Result, when the population continues to grow, with the help of hybrid Building, the city realizes the urban form of near future.

Far Future Scenario

Step-3

2021 CPU[Ai] Studio 3

Automatic Generation

In addition to a Near Future Scenario, we will also show a far Future Scenario, which shows the change of the urban form as the population continues to increase further into the Future.

Detail Attribute & Comparison Step-4

Step-5

Outcomes

The float window contains the specific data of this iteration result and the performance of energy consumption. Users can use the data according to their own requirements or compare other Iteration data.

Outcomes-1: Urban Level

Tool Interface Display_Outcomes 1

Iteration Result Iteration Attributes Solar Irradiation Visualization Program Distribution Visualization Near Future Scenario Far Future Scenario

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Iteration Result

6.2.5

INTERFACE REVIEW - OUTCOMES-1

In this step, we will provide more specific information about an iteration and outcome based on the user’s choice.

Our outcomes are divided into three levels. First, we will introduce the outcomes of urban level. This interface will show the specific information of iteration and other visual content provided by the user after selection. For example, the program model and the solar irradiation model.

Step-1

2021 CPU[Ai] Studio 3

User setting Step-2

Step-3

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Automatic Generation

Legend of Solar Irradiation

Step-4

Solar Irradiation Mode

Step-5

Outcomes

Outcomes-1: Urban Level

Tool Interface Display_Solar Irradiation

Iteration Result Iteration Attributes Solar Irradiation Visualization Program Distribution Visualization Near Future Scenario Far Future Scenario

108

When the user switches to Solar Irradiation Mode, the visual results of solar irradiation generated by the buildings in the site will be shown in the interface under current climate conditions.

6.2.5

INTERFACE REVIEW - OUTCOMES-1

In this step, we will provide more specific information about an iteration and outcome based on the user’s choice.

Our outcomes are divided into three levels. First, we will introduce the outcomes of urban level. This interface will show the specific information of iteration and other visual content provided by the user after selection. For example, the program model and the solar irradiation model.

Step-1

2021 CPU[Ai] Studio 3

User setting Step-2

Step-3

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Automatic Generation

Legend of Program

Step-4

Program Mode In the program display mode, users can see the distribution of all programs in Site in the main interface.

Step-5

Outcomes

Outcomes-1: Urban Level

Tool Interface Display_Program

Iteration Result Iteration Attributes Solar Irradiation Visualization Program Distribution Visualization Near Future Scenario Far Future Scenario

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6.2.5

INTERFACE REVIEW - OUTCOMES-2

The Use Of Modular Building Can Help Us Realize The Goal Of Resilience City In The Future From The Building Level

When the user selects the block view, we will display the details of the block. The interface will show the urban form optimized by us, as well as the presentation of the buildings generated in the city according to the architectural design principles of hybrid typology.

User setting

Block Information We are going to show an introduction to Block Level and an introduction to the basics at the Block Level.

Step-2

Block Level Scene At this point, the main interface will display our main interface, so that users can see the performance of the building in the block, as well as the urban form and detail presentation at the block level under the architectural design principle of Hybrid typology

Step-3

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Automatic Generation

Step-4

Step-5

Tool Interface Display_Outcomes 2

Outcomes-2: Block Level Optimized Building Configuration: - Building Height - Building Position - Solar Irradiation Outcomes

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Step-1

5.2.5

INTERFACE REVIEW - OUTCOMES-3

The Use Of Modular Building Can Help Us Realize The Goal Of Resilience City In The Future From The Building Level

When the user specifically clicks on a mobile building, the camera will focus on the building and display the specific information and energy consumption performance of the building. Meanwhile, according to the design principle of hybrid typology, we will put forward an expectation of the performance of the building in the future for users to make a comparison.

Building Form

User setting Step-2

Building Performance & Data The floating window on the side will show the building’s detailed data and energy performance.

Expected Performance

Step-3

2021 CPU[Ai] Studio 3

Automatic Generation

Beneath that data, we’ll show how the building will change as the population grows in the future, and how we expect it to perform.

Step-4

Step-5

Tool Interface Display_Outcomes 3

Outcomes-3: Building Level Building Form of Hybrid Typology Building Data and Performance The Expected Performance of Building Performance (Under Future Scenario) Outcomes

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When you click on Building, the main interface will display the presentation of the architectural form in the city generated according to our Hybrid Typology design principles.

Step-1

Access to the

2021 CPU[Ai] Studio 3

DESIGN TOOL The gate way to ZERO carbon city

Estimated Population Setting Planned population : Near future population : Far Future Population :

Land Use Ratio sETTING rESIDENTIAL aREA :

10%

cOMMERCIAL Area :

10%

Office Area : Public Service Area :

Railway Setting

20% 5%

tRANSPORTATION huB Setting 71% 14% 11%

11%

Public transportation HUb :

90% 70% 45% 10%

Spacifies Element Setting

Elevated :

Water aREA :

Ground :

RAILWAY :

Under ground :

Historical Building :

2021 CPU[Ai] Studio 3

Generate

To View the Tool Experience Video https://drive.google.com/drive/folders/147ketK1nxFEGHqHmObkM318L1GiVzMq2?usp=sharing

To Test the Tool Demo https://drive.google.com/drive/folders/147ketK1nxFEGHqHmObkM318L1GiVzMq2?usp=sharing

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Manchester school of architecture

THE GATE WAY TO ZERO CARBON CITY

Chapter 7

7.1 MASTERPLAN 7.2 MASTER PLAN CHARACTERISTICS

PROJECT DELIVERABLES

7.3 VISUALIZATION COLLECTION

This chapter aims to demonstrate the masterplan, project characteristic, computational coding overview and architecture visualization of the project.

113

7.1

Master Plan STUDIO 3 × CPU[AI] 2021

MASTER PLAN

Show The Basic Information Of The Master Plan Mixed Use Residential Area

Mixed Use Commercial Area

Mixed Use Commercial Area

Mixed Use Commercial Area Mixed Use Commercial Area

Mixed Use Commercial Area

Transportation Hub Railway Mixed Use Office Area

114

7.2

Walk-ability Access of New Green Space

MASTER PLAN CHARACTERISTICS

Buildings Energy Consumption

Solar Irradiation Evaluation The amount of solar radiation is low, and the self-sufficiency of electric energy cannot be achieved.

100m walk-able area

The amount of solar radiation is relatively high, which can achieve self-sufficiency in electrical energy.

50m walk-able area

Low

Medium

High

The high amount of solar radiation can realize self-sufficiency in electric energy and store surplus electric energy.

7.4

COMPUTATIONAL SCRIPT ILLUSTRATION

SIET DATA REFINE AND REFERENCE

URBAN PATTERN GENERATION

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TYPOLOGY ASSIGNMENT & HIGHT OPTIMIZATION

EVALUATION & TEST

GREEN AMENITY DISTRIBUTION & GENERATION

BLOCK GREEN PATCH CONSLIDATION DENSITY METRICS

BUILDING FORM OPTIMIZATION

2021 CPU[Ai] Studio 3

LAND-USE RATIO & SPATIAL STARTEGY

PLOT LAYOUT AND BUILIDNG POSITION OPTIMIZATION

FEEDBACK LOOP 2 FEEDBACK LOOP 2 FEEDBACK LOOP 2

Please check out the series of computational process video: https://youtu.be/frqqzA6o4h4

119

FEEDBACK LOOP 3

2021 CPU[Ai] Studio 3

Manchester school of architecture

Chapter 8

THE GATE WAY TO ZERO CARBON CITY

CONCLUSION 8.1 CONCLUSION 1: LESSONS LEARNED FROM DESIGN OUT COME ANALYSIS

The conclusion of this project will mainly focus on three aspects: 1. The conclusion and reflection on what we learn from the design outcome analysis. 2. Second part targets how we achieve the overall design challenge by applying specific theory and utilizing the specific computational method. 3.The Reflection on the limitation and prospect of this design tool development.

8.2 CONCLUSION 2: CONCLUSION & REFLECTION ON ENTIRE PROJECT PROCESS 8.3 CONCLUSION 3: DESIGN TOOL CONSTRAINTS AND PROSPECT

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8.1

CONCLUSION 1: LESSONS LEARNED FROM DESIGN OUTCOME ANALYSIS

By Utilizing Our Design Tool, The Correlation That We Can Explore Among Different Input Parameters Researched Spatial Aspects, And Outcome Performance In Different Criteria 245

720,000

0.314

3.1

1.0

316 240

215

210 235

314 312

710,000

0.312

230

200

308 306

0.306

396,000

560,000 550,000

19,000

Annual Electricity Generation (GWh)

Solar Irradiation (GWh/ m2)

60,000 520,000

540,000 394,000

500,000

18,500

530,000

0.4 2.8

680,000

Potential Solar Panel Area(m2)

Plot Coverage

1. These two iterations can be recommended as the optimal option to achieve the ‘Zero Carbon Future‘ design target. And they both have ideal surplus Ratio (Electricity Generation / Electricity Consumption Ratio). Therefore, their input parameter value can be regarded as an ideal design input for other similar computation process.

Vertical Randomness Seed

Plot Width:

50,000

480,000

392,000

460,000

128

H/W (Aspect Ratio)

Green Amenity Area (m2)

Residential Unit Number

Office & Co-working Total Area (m2)

Commercial Total Area (m2)

120

Low

High

Building Orientation: Isolated Distribution

Typology Average Footprint (L * W):

SOLAR IRRADIATION

2. By Comparing two iterations’ parameter: a. Two iterations both have similar Plot Width ( 75 to 79m) and Plot Length (117 to 120m). This value range also matches the previous research on plot dimension that can achieve efficient walkability and mixed-use program arrangement.

180

Landuse Location Seed:

Mixed Distribution

ELECTRICITY GENERATION

High

Horizontal Randomness Seed

40,000

Public Service Total Area (m2)

Low

MODEL OPTIMIZATION 2

510,000

2.7

FAR

75

Plot Length:

520,000

18,000

64

64 Annual Electricity Consumption (GWh)

MODEL OPTIMIZATION 1

70,000

540,000

0.2 0.304

560,000

19,500

0.8

2.9

690,000

398,000

0.6

0.308

310 205

3.0

0.310

700,000

MODEL GENERATION

570,000

318 220

CONCLUSION & PARAMETER SUGGESTION

KEY PARAMETERS ADJUSTED BY THE TOOL

65 %

North

South

East

West

b. Both Iterations have the Landuse Location Seed tending to Mixed Distribution, the can be concluded that the mixture of building function and typology at urban and block level helps on achieve energy efficient urban form.

MODEL OPTIMIZATION 3 Voxel Units to transform:

RESIDENTIAL UNIT

Min

Max

Low Floor numbers for solar accessibility : 0

Residential

Surplus Ratio (Electricity Generation /Consumption)

Commercial Office + Co-working

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Public

720,000

0.314

1.0

316 240

215

210 235

314 312

710,000

700,000

230

200

308

19,500

396,000

19,000

540,000 550,000

Annual Electricity Generation (GWh)

Solar Irradiation (GWh/ m2)

Potential Solar Panel Area(m2)

540,000 394,000

500,000

18,500

530,000

0.4 2.8

50,000

480,000

392,000

460,000

64

128

H/W (Aspect Ratio)

Green Amenity Area (m2)

Residential Unit Number

Commercial Total Area (m2)

Low

High

e. As iteration 1 indicates, the higher value in Building Form Solar Optimization does help receive more solar irradiation. On the other hand, this extraction optimization method will require a Higher value in FAR to meet the minimum floor area requirement.

MODEL OPTIMIZATION 2

40,000

510,000

2.7

FAR

79

Plot Length:

520,000

18,000

0.2

Plot Coverage

Vertical Randomness Seed

Plot Width: 60,000

Horizontal Randomness Seed

64 Annual Electricity Consumption (GWh)

MODEL OPTIMIZATION 1

70,000

560,000

0.6

680,000 0.304

560,000

0.8

2.9

0.306

398,000

d. However, the overshadowing should be considered here, requiring building form optimization both in the vertical and horizontal dimension. This also can be reflected in two iterations input parameters: high Randomness Seed both for Building Height Optimization and Building Position Optimization

520,000

0.310

690,000

306

3.0

0.308

310 205

0.312

MODEL GENERATION

570,000 3.1

13

KEY PARAMETERS ADJUSTED BY THE TOOL

318 220

8

c. Both Iterations have reached high Typology Average Footprint value; this can be linked to a high Plot Coverage ratio as the graph showing. Based on previous research and iterations performance, the higher Plot Coverage ( Larger Average Footprint) dose contributes to more efficient energy usage.

93%

ITERATION 8 0.316 245

High

Office & Co-working Total Area (m2)

Public Service Total Area (m2)

180 Low

Landuse Location Seed:

Mixed Distribution

BALANCE IN EVERY METRICS

117

High

3. The difference of H/W ratio (Average Building Height / Average Building Distance) between two iterations dose reflects the researched principles: when the H/W ratio at the range of 0.9 to 1.1, The building has lower in-use energy demand.

Iteration 2 ----Average Building Annual Electricity consumption: 12912 KWh Iteration 8 ----Average Building Annual Electricity consumption: 12371 KWh

Building Orientation:

Isolated Distribution

60 %

North

South

East

West

MODEL OPTIMIZATION 3 Typology Average Footprint (L * W):

Min

Max

Voxel Units to transform:

Low

High

4. Although the higher compactness value (FAR) dose contributes to generating more residential units and more overall solar irradiation in Iteration 2, it negatively impacts green amenity area and distribution. This can also be reflected by the higher Surplus ratio in Iteration 8, which could benefit from less overshadowing.

Floor numbers for solar accessibility : Residential Commercial Office + Co-working Public

Surplus Ratio (Electricity Generation /Consumption)

94%

0

7

13

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ITERATION 2 0.316

8.2

CONCLUSION 2: CONCLUSION & REFLECTION ON ENTIRE PROJECT PROCESS

We set the theoretical lens to understand the critical issues from resilience theory and complex adaptive system perspective to propose a bottom-up design approach, which is aimed to achieve the zero-carbon future city design. Framed by the two urban theories, we developed a deeper understanding of the defined contradictory correlations and a preliminary concept of achieving the design challenge, theoretically.

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The research-based principle study specifically helped us select spatial aspects that directly impact the correlations among building energy demand, morphological compactness, solar potential and greens amenity distribution whilst better revealing the previously mentioned contradictions. Due to the theoretical framework and the complexity of the variable, we decided to develop a generative design tool to efficiently deal with the complicated changes and contradictions of different spatial aspects in realizing the ‘Zero Carbon Future’ design. The main reason for choosing the computational generative design method is that the design problem cannot be resolved intuitively and manually. Meanwhile, to deal with multiple contradictory variables and achieve multiple design goals, it is necessary to compare and analyse the multiple potential results. By utilizing the generative design method, multiple iterations can be generated and evaluated. While the corresponding parameters also can be recorded for further investigation.

The generation process has ten steps. The controllable parameters and variables are translated by the researched key spatial aspects, namely, basic building typology information and site climate condition. This design tool aims to fulfil three design goals: low energy use urban form, hybrid building typology with ideal solar irradiation potential and walkable green amenity distribution. The generation sequence was set from urban level to block level, followed by a zoom-in into hybrid building typology. Each level contains a specific computational approach adopted to the design issues.

From step one to step five, the tool mainly generated the urban form at the urban level. The urban pattern and green amenity distribution were conducted by implementing the circle packing method. The TOD and P+R principle was applied in Land-use spatial strategy formulation. When zooming into block level, multiple program and typologies allocation outcome were benefited by the generative process. The diversity of iterations did help in achieving the ideal spatial aspect value that has been researched. The optimization of building form also based on the generative process. The simulation result can guide the optimization parameter tend to an optimal range.

As mentioned above, the design tool can conduct multiple iterations based on the feedback loop from the evaluation mechanism, which criteria were mainly based on researched spatial Aspect ratio, brief requirement and solar/electricity simulation. The evaluation process was formulated to achieve two main goals. Firstly, the design tool will evaluate the performance of each iteration in terms of electricity consumption, generation, green space distribution, and solar radiation. The surplus ratio (electricity consumption/electricity generation ratio) reaches 90% is used as an essential basis for screening urban forms that can realize the ’Zero Carbon Future‘ design target. Secondly, the performance of other criteria will also be recorded and ranked, allowing users to set and select more specific design solutions. This process’s outcome can also be interpreted as the response to the selected design challenge and brief requirements.

The iteration analysis shows that the optimal design output can exceed 90% of the surplus ratio (Annual Electricity Generation / Annual electricity consumption), demonstrating more potential than the general urban form and building typology to achieve the ‘Zero Carbon Future’ design. Similarly, the analysis results also reflect the contradictory value demands of spatial aspects to achieve morphological compactness, building solar irradiation potential and green amenity distribution. The changes of spatial aspects and the impact on the design results are often interrelated, and it is impossible to obtain the best value of an aspect directly. At the same time, the ideal value range of input parameters can only be inferred by selecting the relatively optimal iteration. This complicated interaction and contradictory relationship between multiple variables also proved the rationality and necessity of choosing the generative design method.

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The Gateway to Zero-carbon City conducted in-depth research on urban-related climate issues by targeting the zero-carbon future city design as the primary design challenge. We, as a group, extracted the on-site problem and context in the formulation of our design. The thesis is driven by one research question: how to design the urban morphology and hybrid building typology through a generative approach to deal with potentially contradictory correlation among morphological compactness, solar optimization and green amenity distribution, and thus to better achieve low building energy demand and energy regeneration?

LIMITATION & PROSPECT 1: Due to the calculation time consuming and coding logic, we were decided to split the evaluation process into two steps. The first step mainly evaluated the outcome from urban and block-level generation; the second evaluation was mainly set for building level optimization. It did help save the generation time ease the workload of the machine; however, this split evaluation mechanism has the potential to fall in generating more optimal iteration due to the mutual influence and complex relationship between parameters and evaluation criteria. The future work should aim to evaluate the entire generation process with one unified evaluation process and generate a more reliable outcome.

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LIMITATION & PROSPECT 2: Since we initially focused on block-level generation, the design tool allows user to reference basic site condition as the start point but only with limited parameter adjustment at the urban level. Most primary street and superblock land-used arrangement was only decided based on our researched principles or guidance. This limitation could constrain the user to customize their design outcome fully. Meanwhile, it did limit the diversity and possibility of generated iteration.

LIMITATION & PROSPECT 3: Although we applied the generative process to generate multiple allocation strategies by adjusting the random location seed at the block level program allocation, it still could miss any optimal iteration due to the number of iterations we can generate. In the future update, we should utilize the Agent-Based model to simulate this process and gain a more reasonable and optimal result.

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8.3

CONCLUSION 3: DESIGN TOOL LIMITATION AND PROSPECT

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GATEWAY TO FUTURE

Thanks for reading

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