Fast prediction of urban wind distribution with deep learning-based surrogate models

Urban wind environment assessment is crucial for enhancing pedestrian wind comfort and managing pollutant dispersion at the planning and design stage. Deep learning-based models have shown potential to replace computationally intensive Computational Fluid Dynamics (CFD) simulations for accelerated assessment. However, the effectiveness and characteristics of the models in predicting wind distribution for urban microclimate under different building configurations remain unclear.

Moreover, there is a lack of knowledge about the influence of the training dataset composition on the model performance. This study aims to comprehensively evaluate a deep learning-based surrogate model for fast prediction of urban wind distribution. To this end, a substantial training dataset comprising 4,000 CFD simulations was created. A surrogate model based on U-net architecture was then developed and trained to predict wind distribution for urban microclimate.

The trained model achieved mean absolute percentage errors (MAPE) ranging from 1.74% to 12.49% for unseen configurations with 1 to 4 buildings, offering a speed-up of 3–4 orders of magnitude over traditional CFD methods thus enabling near real-time wind distribution assessments. The model exhibited limited domain transferability, as it can learn transferable wind flow patterns across different building configurations. From the model development perspective, integrating diverse building configurations into the training dataset proved effective in improving the model’s robustness and generalization capabilities, with cases including multiple buildings yielding more substantial improvements in predictive performance.

This study shows that the deep learning-based surrogate model has demonstrated significant potential for accelerating the assessment of wind distribution for urban microclimate, thus greatly benefitting the early stages of urban planning and design.