四川大学学报（自然科学版）2024，Vol.61Issue(6)：135-143,9.DOI:10.19907/j.0490-6756.2024.063004
基于混合神经网络的非定常流场预测方法
An unsteady flow-field prediction method based on hybrid neural network
摘要
Abstract
Solving partial differential equations based on mesh discretization is a critical and time-consuming task in the simulation of numerous complex physical systems.To overcome this challenge,this paper pro-poses an innovative hybrid neural network,called GRNet,which combines graph neural network(GNN)and recurrent neural network(RNN).The GNN model is trained to learn the law of physics between mesh nodes governed by the Navier-Stokes equations.The RNN network is trained to uncover the temporal depen-dence of mesh nodes.The proposed model can effectively utilize the multi-scale advantages of high-resolution meshes to quickly and accurately predict subsequent flow fields based on just a few starting frames.We exten-sively explore the performance of GRNet in several complex flow field prediction tasks,such as cylinders and airfoils.Compared with conventional numerical simulation results,our model not only maintains exceptional accuracy but also operates at an impressively high speed.In contrast to the baseline model(GN),GRNet ex-hibits a substantial advantage in minimizing cumulative prediction errors.关键词
混合神经网络/图神经网络/循环神经网络/流场预测/非定常Key words
Hybrid neural network/Graph neural network/Recurrent neural network/Flow-field predic-tion/Unsteady分类
信息技术与安全科学引用本文复制引用
孔德天,董义道,张来平,邓小刚..基于混合神经网络的非定常流场预测方法[J].四川大学学报（自然科学版）,2024,61(6):135-143,9.基金项目
国家重大专项(GJXM92579) （GJXM92579）
四川省科技计划资助(2023YFG0158) （2023YFG0158）
国防科技大学科研计划项目(ZK21-08) （ZK21-08）
