Fast urban flow predictions through Convolutional Neural Networks
In session: MON 4.1 - Machine Learning II
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Having real-time and accurate numerical predictions of urban wind flow can be extremely useful for developing tools intended to improve citizens’ life quality and health. However, traditional methods such as Computational Fluid Dynamics (CFD) are unsuitable for fast prediction. This work proposes using Convolutional Neural Network (CNN) trained with a newly-created vast dataset to enable fast and accurate flow predictions for any urban geometry. The dataset has been generated through high-fidelity CFD simulations of 30 different European Urban areas and 90 meteorological conditions. The geometries were selected to have a wide variety of urban flow patterns and geometrical features allowing the Neural Network (NN) to learn a representative range of urban flow conditions. Then, a CNN was trained to reproduce the urban wind flow for any urban geometry and meteorological condition. The strategy allows for predicting accurate mean wind flow in urban areas that have not been seen in training time, showing good generalization properties.