Research on the Method of Automatic Generation and Multi-Objective Optimization of Block Spatial Form Based on Thermal Comfort Demand
Urban thermal environment challenges in China have made outdoor thermal comfort a key factor in evaluating spatial quality and livability. Building layout not only affects internal performance but also shapes the microclimate of surrounding outdoor spaces. The climatic characteristics of temperate m...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/12/2098 |
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| Summary: | Urban thermal environment challenges in China have made outdoor thermal comfort a key factor in evaluating spatial quality and livability. Building layout not only affects internal performance but also shapes the microclimate of surrounding outdoor spaces. The climatic characteristics of temperate monsoon climate regions significantly impact residents’ outdoor activities. Most existing studies focus solely on either the external thermal environment or the buildings themselves in isolation. This study focuses on Beijing, a representative city in the temperate monsoon climate zone, and explores block-scale spatial optimization using computational typology. The objective is to balance architectural performance with outdoor thermal comfort in both winter and summer. Optimization targets include the Universal Thermal Climate Index (UTCI), winter sunshine duration, and summer solar radiation. Results show winter UTCI can be optimized to −6.13 °C to −1.18 °C and summer UTCI to 28.19 °C to 29.17 °C, with greater optimization potential in winter (23.5% higher). Synergistic relationships are observed between winter comfort and sunshine duration (coefficient: 0.777) and between summer comfort and solar radiation (coefficient: 0.947). However, trade-offs exist between seasonal comfort indicators, with strong conflicts between winter and summer objectives. Two distinct form types—“low-south-high-north enclosed” for winter and “high-rise point-type low-density” for summer—are identified as effective for seasonal adaptation. The study proposes an integrated method combining data-driven generation, multi-objective optimization, and clustering-based decision-making. This approach moves beyond traditional empirical design, offering a quantitative and adaptable strategy for climate-responsive urban block planning and supporting low-carbon urban transformation. |
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| ISSN: | 2075-5309 |