Investigation of physics-informed deep learning for the prediction of parametric, three-dimensional flow based on boundary data

Abstract

The placement of temperature sensitive and safety-critical components is crucial in the automotive industry. It is therefore inevitable, even at the design stage of new vehicles, that these components are assessed for potential safety issues. However, with increasing number of design proposals, risk assessment using Computational Fluid Dynamics (CFD) quickly becomes expensive. We therefore present a parameterized surrogate model for the prediction of three-dimensional flow fields in aerothermal vehicle simulations. The proposed physics-informed neural network (PINN) design is aimed at learning families of flow solutions according to a geometric variation. The novelty of our method compared to existing works in the field of PINN lies in the extension of parametric flow prediction to three-dimensional space by applying a mini-batch based Quasi-Newton optimization. We contribute a parametric minibatch training algorithm which enables the utilization of the large datasets necessary for the three-dimensional flow modeling. Further, we introduce a continuous resampling algorithm that allows to represent domain variations, while operating on one static dataset of reduced size. In scope of this work, we could show that our nondimensional, multivariate scheme can be efficiently extended to predict the velocity and pressure distribution in three-dimensional space for different design scenarios and geometric scales. Every feature of our methodology is tested individually and verified against conventional CFD simulations. Finally, we apply our proposed method in context of an exemplary real-world automotive application.