  Urbanization has underscored the importance of understanding the pedestrian
wind environment in urban and architectural design contexts. Pedestrian Wind
Comfort (PWC) focuses on the effects of wind on the safety and comfort of
pedestrians and cyclists, given the influence of urban structures on the local
microclimate. Traditional Computational Fluid Dynamics (CFD) methods used for
PWC analysis have limitations in computation, cost, and time. Deep-learning
models have the potential to significantly speed up this process. The
prevailing state-of-the-art methodologies largely rely on GAN-based models,
such as pix2pix, which have exhibited training instability issues. In contrast,
our work introduces a convolutional neural network (CNN) approach based on the
U-Net architecture, offering a more stable and streamlined solution. The
process of generating a wind flow prediction at pedestrian level is
reformulated from a 3D CFD simulation into a 2D image-to-image translation
task, using the projected building heights as input. Testing on standard
consumer hardware shows that our model can efficiently predict wind velocities
in urban settings in real time. Further tests on different configurations of
the model, combined with a Pareto front analysis, helped identify the trade-off
between accuracy and computational efficiency. This CNN-based approach provides
a fast and efficient method for PWC analysis, potentially aiding in more
efficient urban design processes.
