Generalization of Urban Wind Environment Using Fourier Neural Operator Across Different Wind Directions and Cities

TL;DR

This paper demonstrates that Fourier Neural Operator models can accurately predict urban wind environments across various city layouts and wind directions, significantly reducing computational costs and improving generalizability.

Contribution

The study introduces a novel FNO-based approach that effectively predicts urban wind conditions for different layouts and directions, with improved accuracy and efficiency.

Findings

FNO model reduces computational time by 99%.

Dividing wind fields into smaller blocks improves frequency feature capture.

Incorporating spatial building data enhances prediction realism.

Abstract

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.