Fast estimation of airflow distribution in an urban model using generative adversarial networks with limited sensing data☆

Abstract

Fast estimation of instantaneous urban airflow distribution can protect pedestrians and objects from high-speed winds and promote high-efficiency natural ventilation. Statistical values based on instantaneous airflow distribution can also provide valuable data for wind environment assessments and urban construction. In this study, we evaluated the potential of generative adversarial networks (GANs), a deep-learning-based generative model that learns the probability distribution of training data and generates new data for estimating instantaneous flow fields in an urban model. It utilizes the velocity obtained from sensors as input, and we investigated the influence of constraints on the estimated results. We used two non-linear models based on GANs: Wasserstein GAN with gradient penalty (WGAN), which has no constraints, and conditional WGAN (CWGAN), which has constraints. We employed a flow field dataset obtained using a large-eddy simulation (LES) for model training and testing. The feasibility and accuracy of the flow field generated by the GANs were verified using the results of the LES and proper orthogonal decomposition–linear stochastic estimation (POD–LSE). For the estimated results, although the WGAN learned the probability distribution of the training data, it was unable to determine the temporal relationship among the estimated data. Conversely, the CWGAN successfully estimated the airflow distribution data for reconstruction and prediction, including the temporal relationship. Furthermore, the statistical quantities of the results from the CWGAN were closer to those of the LES than those of the POD–LSE.