Unsteady Flow Prediction Based on Multi-task Graph Neural Network
Traditional computational fluid dynamics(CFD)solver encounters significant computational costs and struggles with sim-ulating fluids due to the inherently high-dimensional and nonlinear properties.To improve the computational efficiency,a framework combining multi-task learning and graph neural networks(GNN)was proposed here,which could predict unsteady flow quickly and ef-ficiently.Firstly,the fluid computational domain's spatial distribution was modeled by unstructured grids.Then,GNN was employed to extract multidimensional spatial features from this distribution.Finally,the aggregation and updating properties of the message passing mechanism were used to simulate the spatio-temporal variation patterns of the fluid.Considering that there were some correlations be-tween different physical fields,a multi-task learning strategy was adopted to learn the variations of multiple physical field parameters in parallel,which could improve the accuracy and generalization of the model.Validation experiments were carried out on a simulation dataset and compared with graph convolutional neural network.The results show that our model has the best prediction results,with a relative decrease of 7.2％in mean square error(MSE)at 100 steps and 29.9％at 200 steps.This model also has a large improvement in computational efficiency,and its prediction speed is improved by one to two orders of magnitude compared with the conventional CFD solver,which provides a good solution on real-time prediction.
万方数据
维普
