{"title":"量化城市几何细节对城市空中交通风险预测的影响","authors":"Akshay Patil, Clara García-Sánchez","doi":"10.1016/j.scs.2025.106750","DOIUrl":null,"url":null,"abstract":"<div><div>Wind flow predictions in realistic urban areas are sensitive to a wide range of governing parameters such as building resolution, wind incidence, urban morphology, and underlying topography, to name a few. In this study, we systematically study the impact of the geometric level of detail (LoD) of the urban built environment using a Reynolds Averaged Navier–Stokes (RANS) computational framework specifically tailored for urban air mobility. Using a wind-incidence angular resolution of <span><math><mrow><mn>1</mn><mo>°</mo></mrow></math></span>, we simulated a total of 1440 simulations for two distinct urban areas and developed a probabilistic risk metric (<span><math><msup><mrow><mi>P</mi></mrow><mrow><mi>r</mi></mrow></msup></math></span>) based on velocity and turbulence fields that allow us to compare the impact of LoD 1.2 (lower geometric detail) and LoD 2.2 (higher geometric detail). Comparing the wind-rose weighted average velocity and the risk map, we found that LoD 2.2 provides a more conservative prediction for high-risk areas than LoD 1.2, suggesting the need to adopt higher geometric details when applicable. Our results present a cautionary view on the impact of LoD and how automatic reconstruction can further the efficiency of current wind engineering practices.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106750"},"PeriodicalIF":12.0000,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantifying the impact of urban geometric detail for urban air mobility risk forecasting\",\"authors\":\"Akshay Patil, Clara García-Sánchez\",\"doi\":\"10.1016/j.scs.2025.106750\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Wind flow predictions in realistic urban areas are sensitive to a wide range of governing parameters such as building resolution, wind incidence, urban morphology, and underlying topography, to name a few. In this study, we systematically study the impact of the geometric level of detail (LoD) of the urban built environment using a Reynolds Averaged Navier–Stokes (RANS) computational framework specifically tailored for urban air mobility. Using a wind-incidence angular resolution of <span><math><mrow><mn>1</mn><mo>°</mo></mrow></math></span>, we simulated a total of 1440 simulations for two distinct urban areas and developed a probabilistic risk metric (<span><math><msup><mrow><mi>P</mi></mrow><mrow><mi>r</mi></mrow></msup></math></span>) based on velocity and turbulence fields that allow us to compare the impact of LoD 1.2 (lower geometric detail) and LoD 2.2 (higher geometric detail). Comparing the wind-rose weighted average velocity and the risk map, we found that LoD 2.2 provides a more conservative prediction for high-risk areas than LoD 1.2, suggesting the need to adopt higher geometric details when applicable. Our results present a cautionary view on the impact of LoD and how automatic reconstruction can further the efficiency of current wind engineering practices.</div></div>\",\"PeriodicalId\":48659,\"journal\":{\"name\":\"Sustainable Cities and Society\",\"volume\":\"132 \",\"pages\":\"Article 106750\"},\"PeriodicalIF\":12.0000,\"publicationDate\":\"2025-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Cities and Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210670725006249\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670725006249","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Quantifying the impact of urban geometric detail for urban air mobility risk forecasting
Wind flow predictions in realistic urban areas are sensitive to a wide range of governing parameters such as building resolution, wind incidence, urban morphology, and underlying topography, to name a few. In this study, we systematically study the impact of the geometric level of detail (LoD) of the urban built environment using a Reynolds Averaged Navier–Stokes (RANS) computational framework specifically tailored for urban air mobility. Using a wind-incidence angular resolution of , we simulated a total of 1440 simulations for two distinct urban areas and developed a probabilistic risk metric () based on velocity and turbulence fields that allow us to compare the impact of LoD 1.2 (lower geometric detail) and LoD 2.2 (higher geometric detail). Comparing the wind-rose weighted average velocity and the risk map, we found that LoD 2.2 provides a more conservative prediction for high-risk areas than LoD 1.2, suggesting the need to adopt higher geometric details when applicable. Our results present a cautionary view on the impact of LoD and how automatic reconstruction can further the efficiency of current wind engineering practices.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;