Advancing graph neural network architecture for fluid flow and heat transfer surrogate modeling: Variable boundary conditions and geometry

Abstract

Graph neural networks (GNNs) represent a promising instrument for surrogate modeling, capable of handling unstructured computational meshes naturally. We address a typical issue of the accuracy degradation for larger computational domains due to the limited receptive field of GNN models and long-range global interactions between nodes of the mesh. We propose a modification of the GNN architecture allowing to improve the accuracy by a factor of 3 without significant increase in computational costs. The validation tests of the model concentrate on the two-dimensional stationary fluid flow around a bluff body in a channel and corresponding heat transfer. The problem formulation includes bluff bodies of randomly generated shapes and various boundary conditions. The model shows a robust performance for the out-of-domain data, i.e., the flow over an airfoil for different angles of attack.