Automating Microclimate Evaluation and Optimization during Urban Design: A Rhino–Grasshopper Workflow
Abstract
:1. Introduction
1.1. Background and General Context
1.2. State of the Art in Microclimate Evaluation
1.3. Research Gap
1.4. Objectives
- -
- Build a responsive evaluation workflow based on spatial indicators.
- -
- Increase computation speed.
- -
- Reduce the computation load for genetic optimization at middle- and large scale.
2. Methodology
2.1. Conceptual Framework
2.2. Workflow Structure and Functions
2.2.1. Model Preprocessing
- (1)
- CAD checking
- (2)
- 3D model generation:
- (3)
- Consistency verification:
- (4)
- Spatial information collection
2.2.2. Processing and Evaluation
- (1)
- Calculation grid generation
- (2)
- Spatial indicators and thermal comfort evaluation
- 1.
- Spatial indicators
- 2.
- Thermal comfort evaluation
- Sensitivity analysis and thermal comfort optimization
2.2.3. Output Visualization
3. Implementation Case
3.1. Area Location
3.2. Existing Stage
3.3. Spatial Descriptors and Thermal Comfort Outputs
3.4. Sensitivity Analysis and Optimization
3.4.1. Sensitivity Analysis
3.4.2. Optimization
3.5. Urban Model Optimization and Results
4. Discussions
4.1. Spatial Indicator Accuracy
4.2. Thermal Comfort Indicator Accuracy
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Validation of Thermal Comfort Estimation, Taking PET as an Example
References
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Index | Calculation Method |
---|---|
Floor Area Ratio (FAR) | |
Building Density (BD) | |
Height Deviation of Buildings (HD) | |
Sky View Factor (SVF) | In Rhino–Grasshopper, rays are emitted in all directions from the measurement point and the number of rays that are not blocked by buildings is counted. |
Wall Area Ratio (WAR) | |
Pavement Area Ratio (PAR) | |
Green Plot Ratio (GnPR) |
Ranking | Spatial Indicators | Sensitivity to PET | Sensitivity to UTCI |
---|---|---|---|
1 | PAR | 21.3368 | 14.1912 |
2 | BD | 17.2726 | 11.5060 |
3 | GNPR | 08.4517 | 05.7009 |
4 | SVF | 08.3681 | 04.6764 |
5 | FAR | 04.4758 | 02.8764 |
6 | WAR | 02.6979 | 01.6986 |
7 | HD | 01.0103 | 00.6343 |
Elements | Description |
---|---|
Hardware | Model: HP Pavilion Gaming Desktop TG01-1XXX Processor: Intel®/core (TM) i7-10700F CPU @ 2.9 GHz, 2.9 GHz, 8, 16 core System: Windows10 Family edition, x64 Memory: 16.0 GB |
Software | Rhino 7 vs. ArcGIS10.2 |
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Share and Cite
Bedra, K.B.; Zheng, J.; Li, J.; Sun, Z.; Zheng, B. Automating Microclimate Evaluation and Optimization during Urban Design: A Rhino–Grasshopper Workflow. Sustainability 2023, 15, 16613. https://doi.org/10.3390/su152416613
Bedra KB, Zheng J, Li J, Sun Z, Zheng B. Automating Microclimate Evaluation and Optimization during Urban Design: A Rhino–Grasshopper Workflow. Sustainability. 2023; 15(24):16613. https://doi.org/10.3390/su152416613
Chicago/Turabian StyleBedra, Komi Bernard, Jian Zheng, Jiayu Li, Zhaoqian Sun, and Bohong Zheng. 2023. "Automating Microclimate Evaluation and Optimization during Urban Design: A Rhino–Grasshopper Workflow" Sustainability 15, no. 24: 16613. https://doi.org/10.3390/su152416613