用AI模型99%提速风场模拟，跨城市跨风向通用
Generalization of Urban Wind Environment Using Fourier Neural Operator Across Different Wind Directions and Cities
- 用傅里叶神经算子分块训练，捕捉风频特征
- 计算时间减少99%，预测精度高
- 适合城市规划与环境模拟场景
城市风环境模拟对城市规划、污染控制和可再生能源利用至关重要。然而，高保真计算流体动力学（CFD）方法的计算成本过高，难以应用于真实城市。为解决这一问题，本研究探讨了傅里叶神经算子（FNO）模型在不同风向和城市布局下预测流场的有效性。通过使用大涡模拟数据训练速度场，评估模型在多种城市配置和风况下的表现。结果表明，FNO模型可实现高精度预测，同时将计算时间降低99%。创新性地将风场划分为小空间块进行训练，提升了模型捕捉风频特征的能力。建筑空间信息（SDF数据）增强了模型对物理边界的识别能力，生成更真实的预测结果。所提FNO方法显著提升了AI模型在不同风向和城市布局下的泛化能力。
原文摘要 · Abstract (English)
Simulation of urban wind environments is crucial for urban planning, pollution control, and renewable energy utilization. However, the computational requirements of high-fidelity computational fluid dynamics (CFD) methods make them impractical for real cities. To address these limitations, this study investigates the effectiveness of the Fourier Neural Operator (FNO) model in predicting flow fields under different wind directions and urban layouts. In this study, we investigate the effectiveness of the Fourier Neural Operator (FNO) model in predicting urban wind conditions under different wind directions and urban layouts. By training the model on velocity data from large eddy simulation data, we evaluate the performance of the model under different urban configurations and wind conditions. The results show that the FNO model can provide accurate predictions while significantly reducing the computational time by 99%. Our innovative approach of dividing the wind field into smaller spatial blocks for training improves the ability of the FNO model to capture wind frequency features effectively. The SDF data also provides important spatial building information, enhancing the model's ability to recognize physical boundaries and generate more realistic predictions. The proposed FNO approach enhances the AI model's generalizability for different wind directions and urban layouts.
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