FlowGAN: A Conditional Generative Adversarial Network for Flow Prediction in Various Conditions

Many flow-related design optimization problems like aircraft and automobile aerodynamic design are solved via computational fluid dynamics (CFD) simulations. However, CFD simulations are known to be resource-demanding and time-consuming. Deep learning (DL) is emerging as a viable means to accelerate CFD simulations by directly predicting the outcomes of multiple simulation iterations. While promising, existing DL-based models have to be re-trained whenever the flow condition changes, which incurs significant training overhead for real-life scenarios with a wide range of flow conditions. This paper presents FLOWGAN, a novel conditional generative adversarial network for accurate prediction of flow fields in various conditions. FlowGAN is designed to directly obtain the generation of solutions to flow fields in various conditions based on observations rather than re-training. As FlowGAN does not rely on knowledge of the underlying governing equations, it can quickly adapt to various flow conditions and avoid the need for expensive re-training. We evaluate FlowGAN by applying it to scenarios of simulating both the whole flow field and selected regions of interest (RoI). Compared to the state-of-the-art DL based methods, FlowGAN significantly reduces the prediction errors by 2.27% while exhibiting a better generalization ability.