Application of Multi-Objective Climate Optimization on

M.Arch 2017

Ling Ban Liang

Application of Multi-Objective Climate Optimization on Residential in Singapore

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

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Table of Contents

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

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

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Necessity

>

Unnecessary


3m/s On the other hand, normalized score is biased towards surface normals which are parallel to wind direction. Fig 62. Normalised Score

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

Fig 61. Wind Score

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05 | Inputs of Optimization

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

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

Fig 63. Wind score test run

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05 | Inputs of Optimization

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

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

Fig 64. Wind score vs CFD result

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05 | Inputs of Optimization

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

Fig 65. Daylight Factor42

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

Fig 66. Daylight Autonomy43

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

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05 | Inputs of Optimization

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

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

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

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

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

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05 | Inputs of Optimization

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

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

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

Fig 69. Glare

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

05 | Inputs of Optimization

daylighting

massing volume

scoring

simulation

UDI

6 2 5

parametric model with variables

>300 Lux 300 lux and Population density.” Population density - Country Comparison. Accessed April 25, 2017. https://www.indexmundi.com/g/r. aspx?v=21000. 35. “Solar Radiation & Photosynthetically Active Radiation.” Environmental Measurement Systems. Accessed April 25, 2017. http://www.fondriest.com/ environmental-measurements/parameters/weather/photosynthetically-active-radiation/. 36. “Solar Photovoltaic Systems.” EMA : Solar Photovoltaic Systems. Accessed April 25, 2017. https://www.ema.gov.sg/Solar_Photovoltaic_Systems.aspx. 37. Robinson, Darren. “Irradiation modelling made simple: the cumulative sky approach and its applications” 38. “Climate of Singapore.” Climate of Singapore |. Accessed April 19, 2017. http://www.weather.gov.sg/climate-climate-of-singapore/ 39. Beaufort Wind Scale. Accessed April 25, 2017. http://www.spc.noaa.gov/faq/tornado/beaufort.html. 40. “Calculating the daylight factor”. Accessed April 20, 2017. https://faculty.unlv.edu/kroel/www%20731%20spring%202006/daylight%20factor.pdf 41. “Climate of Singapore.” Climate of Singapore |. Accessed April 19, 2017. http://www.weather.gov.sg/climate-climate-of-singapore/ 42-44. “Useful Daylight Illuminance.” Daylighting Pattern Guide. Accessed April 25, 2017. http://patternguide.advancedbuildings.net/using-this-guide/ analysis-methods/useful-daylight-illuminance. 45. “Recommended light levels.” Accessed April 25, 2017. https://www.noao.edu/education/QLTkit/ACTIVITY_Documents/Safety/LightLevels_outdoor+indoor.pdf 46. “Solar Radiation & Photosynthetically Active Radiation.” Environmental Measurement Systems. Accessed April 25, 2017. http://www.fondriest.com/ environmental-measurements/parameters/weather/photosynthetically-active-radiation/#PAR1. 47. “Measuring Daylight: Dynamic Daylighting Metrics & What They Mean for Designers.” Sefaira. March 11, 2014. Accessed April 25, 2017. http://sefaira.com/resources/measuring-daylight-dynamic-daylighting-metrics-what-they-mean-for-designers/.

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Appendix A

What company/department are you from?

DP SUSTAINABLE DESIGN

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

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

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

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

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

Appendix A

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

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

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