Fast simulation of high resolution urban wind fields at city scale
High resolution urban wind field simulations are limited in small simulation domain, short simulation period, or stable boundary conditions due to the high computational requirements. To address these limitations, non-intrusive reduced order models (NIROMs) are developed in this study and first real...
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| Vydáno v: | Urban climate Ročník 39; s. 100941 |
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| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.09.2021
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| Témata: | |
| ISSN: | 2212-0955, 2212-0955 |
| On-line přístup: | Získat plný text |
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| Abstract | High resolution urban wind field simulations are limited in small simulation domain, short simulation period, or stable boundary conditions due to the high computational requirements. To address these limitations, non-intrusive reduced order models (NIROMs) are developed in this study and first realized the prediction of high-resolution 3D wind field in a city-scale area (~150 km2) during a long simulation period (4 weeks). Within the NIROM, 3D convolutional autoencoder and eXtreme Gradient Boost regression models (XGBoost) are adopted. The urban airflow can be predicted rapidly by the XGBoost-decoder models with the input of boundary conditions via boundary conditions - latent variable - full-order wind fields. By comparing NIROM simulations to large-eddy simulations, we find that the NIROM with portable model size is capable for capturing the main spatial distribution characteristics of urban airflow. Moreover, the relationship between wind speed and building distribution indicates that the sparse layout with taller buildings can yield a 2-fold increase in the near-ground wind speed compared to the dense layout with lower buildings, which also benefits pedestrian thermal comfort and pollutant dissipation. Furthermore, this study can assist the optimization of urban ventilation and air quality in urban planning.
•Airflow is predicted via boundary condition - latent variable - wind field.•NIROMs are built to predict 3D urban airflow over a 150-km2 area for 4 weeks.•Sparser layout with taller buildings can increase near-ground wind speed by ~2 times. |
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| AbstractList | High resolution urban wind field simulations are limited in small simulation domain, short simulation period, or stable boundary conditions due to the high computational requirements. To address these limitations, non-intrusive reduced order models (NIROMs) are developed in this study and first realized the prediction of high-resolution 3D wind field in a city-scale area (~150 km2) during a long simulation period (4 weeks). Within the NIROM, 3D convolutional autoencoder and eXtreme Gradient Boost regression models (XGBoost) are adopted. The urban airflow can be predicted rapidly by the XGBoost-decoder models with the input of boundary conditions via boundary conditions - latent variable - full-order wind fields. By comparing NIROM simulations to large-eddy simulations, we find that the NIROM with portable model size is capable for capturing the main spatial distribution characteristics of urban airflow. Moreover, the relationship between wind speed and building distribution indicates that the sparse layout with taller buildings can yield a 2-fold increase in the near-ground wind speed compared to the dense layout with lower buildings, which also benefits pedestrian thermal comfort and pollutant dissipation. Furthermore, this study can assist the optimization of urban ventilation and air quality in urban planning.
•Airflow is predicted via boundary condition - latent variable - wind field.•NIROMs are built to predict 3D urban airflow over a 150-km2 area for 4 weeks.•Sparser layout with taller buildings can increase near-ground wind speed by ~2 times. |
| ArticleNumber | 100941 |
| Author | Zhang, Yizhou Ma, Jianmin Zheng, Leyi Yi, Kan Fu, Xiangwen Wang, Yuqing Liu, Junfeng Zhou, Jingcheng Xiang, Songlin Tao, Shu |
| Author_xml | – sequence: 1 givenname: Songlin surname: Xiang fullname: Xiang, Songlin organization: Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, China – sequence: 2 givenname: Jingcheng surname: Zhou fullname: Zhou, Jingcheng organization: Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, China – sequence: 3 givenname: Xiangwen surname: Fu fullname: Fu, Xiangwen organization: Princeton School of Public and International Affairs, Princeton University, USA – sequence: 4 givenname: Leyi surname: Zheng fullname: Zheng, Leyi organization: Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, China – sequence: 5 givenname: Yuqing surname: Wang fullname: Wang, Yuqing organization: Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, China – sequence: 6 givenname: Yizhou surname: Zhang fullname: Zhang, Yizhou organization: Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, China – sequence: 7 givenname: Kan surname: Yi fullname: Yi, Kan organization: Institute of Science and Technology, China Three Gorges Corporation, China – sequence: 8 givenname: Junfeng surname: Liu fullname: Liu, Junfeng email: jfliu@pku.edu.cn organization: Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, China – sequence: 9 givenname: Jianmin surname: Ma fullname: Ma, Jianmin organization: Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, China – sequence: 10 givenname: Shu surname: Tao fullname: Tao, Shu organization: Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, China |
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| Keywords | Urban airflow Non-intrusive reduced order model 3D convolutional autoencoder |
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