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Pedestrian wind flow prediction using spatial-frequency generative adversarial network
Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban conditions. Predicting pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical simulations. Deep learning, particularly generative adversarial networks (GAN), has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind flow. However, existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow images. This study proposes a novel approach termed SFGAN, which embeds spatial and frequency characteristics to enhance pedestrian wind flow prediction. In the spatial domain, Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges, which are used as the embedded spatial characteristics. Detailed information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency characteristics. These spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase, thereby leading to enhanced predictions. Experimental results demonstrate that SFGAN clearly improves wind flow prediction, reducing Wind_MAE, Wind_RMSE and the Fréchet Inception Distance (FID) score by 5.35%, 6.52% and 12.30%, compared to the previous best method, respectively. We also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind flow. SFGAN reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding buildings. The enhanced predictions provide a better understanding of performance variability, bringing insights at the early design stage to improve pedestrian wind comfort. The proposed spatial-frequency loss term is general and can be flexibly integrated with other generative models to enhance performance with only a slight computational cost.
Pedestrian wind flow prediction using spatial-frequency generative adversarial network
Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban conditions. Predicting pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical simulations. Deep learning, particularly generative adversarial networks (GAN), has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind flow. However, existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow images. This study proposes a novel approach termed SFGAN, which embeds spatial and frequency characteristics to enhance pedestrian wind flow prediction. In the spatial domain, Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges, which are used as the embedded spatial characteristics. Detailed information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency characteristics. These spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase, thereby leading to enhanced predictions. Experimental results demonstrate that SFGAN clearly improves wind flow prediction, reducing Wind_MAE, Wind_RMSE and the Fréchet Inception Distance (FID) score by 5.35%, 6.52% and 12.30%, compared to the previous best method, respectively. We also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind flow. SFGAN reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding buildings. The enhanced predictions provide a better understanding of performance variability, bringing insights at the early design stage to improve pedestrian wind comfort. The proposed spatial-frequency loss term is general and can be flexibly integrated with other generative models to enhance performance with only a slight computational cost.
Pedestrian wind flow prediction using spatial-frequency generative adversarial network
Build. Simul.
Wang, Pengyue (author) / Guo, Maozu (author) / Cao, Yingeng (author) / Hao, Shimeng (author) / Zhou, Xiaoping (author) / Zhao, Lingling (author)
Building Simulation ; 17 ; 319-334
2024-02-01
16 pages
Article (Journal)
Electronic Resource
English
pedestrian wind flow prediction , generative adversarial network , Gaussian kernel , wavelet transform , objective function Engineering , Building Construction and Design , Engineering Thermodynamics, Heat and Mass Transfer , Atmospheric Protection/Air Quality Control/Air Pollution , Monitoring/Environmental Analysis
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