Scalable super-resolution of flow conveyance systems through adaptive domain decomposition

Abstract Modern engineering design requires high-fidelity simulations, which can impose an enormous computational burden and slow the speed of design iteration. Data-driven up-sampling methods like physics-informed neural networks (PINNs) help reduce the computational resources required. However, machine learning model capacity and hardware limitations still pose challenges when evaluating large engineering simulations with complex physics dynamics. Recently, methods have been proposed to enforce the principle of locality in physical systems to neural network layers, allowing for concurrent inference on smaller subdomains with improved efficiency and accuracy. Based on such an idea, we extend the theory of domain decomposition to complex three-dimensional geometries using graph neural networks (GNNs). We developed a graph decomposition method to improve the training and inference efficiency of machine learning models. Super-resolution GNNs are then trained on individual subdomains distributed among GPU nodes. This approach significantly reduces computational overhead while maintaining simulation accuracy. We validate the method performance on two engineering applications: a variable inlet-angle mixing elbow junction and a low-pressure bleed duct from an Airbus A350 aircraft. These results demonstrate that our approach can effectively bridge the gap between computational efficiency and simulation fidelity across different scales of complex engineering design tasks.