A Finite Difference Informed Graph Network for Solving Steady-State Incompressible Flows on Block-Structured Grids

• URL: http://arxiv.org/abs/2406.10534v1
• Date: Sat, 15 Jun 2024 07:30:40 GMT
• Title: A Finite Difference Informed Graph Network for Solving Steady-State Incompressible Flows on Block-Structured Grids
• Authors: Yiye Zou, Tianyu Li, Shufan Zou, Jingyu Wang, Laiping Zhang, Xiaogang Deng,
• Abstract summary: We propose a graph convolution-based finite difference method (GC-FDM) to train GNs in a physics-constrained manner. Our goal is to solve steady incompressible Navier-Stokes equations for flows around a backward-facing step, a circular cylinder, and double cylinders. We demonstrate improved training efficiency and accuracy, achieving a minimum relative error of $10-3$ in velocity field prediction.
• Score: 12.402245124816359
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, advancements in deep learning have enabled physics-informed neural networks (PINNs) to solve partial differential equations (PDEs). Numerical differentiation (ND) using the finite difference (FD) method is efficient in physics-constrained designs, even in parameterized settings, often employing body-fitted block-structured grids for complex flow cases. However, convolution operators in CNNs for finite differences are typically limited to single-block grids. To address this, we use graphs and graph networks (GNs) to learn flow representations across multi-block structured grids. We propose a graph convolution-based finite difference method (GC-FDM) to train GNs in a physics-constrained manner, enabling differentiable finite difference operations on graph unstructured outputs. Our goal is to solve parametric steady incompressible Navier-Stokes equations for flows around a backward-facing step, a circular cylinder, and double cylinders, using multi-block structured grids. Comparing our method to a CFD solver under various boundary conditions, we demonstrate improved training efficiency and accuracy, achieving a minimum relative error of $10^{-3}$ in velocity field prediction and a 20\% reduction in training cost compared to PINNs.

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