BackgroundThe advent of the Artificial Intelligence (AI) era has led to the application of neural networks and deep learning in different disciplines.PurposeThis study aims to solve incompressible gas dispersion problem in an electric thruster discharge cavity using deep neural network-based approach with a physical information-driven neural network.MethodsFirst of all, the traditional Computational Fluid Dynamics (CFD) numerical calculations were facilitated and the classical problem of propellant passing into a thin gas cavity was used to replace the incompressible gas flow problem in vacuum to satisfy the continuum medium assumption of the traditional CFD method. Then, the Navier-Stokes equations were combined with Physics-Informed Neural Networks (PINN) to embed physical information into the networks to ensure that the results were more consistent with the laws of physics to enhance the modeling performance. Subsequently, based on the idea of domain decomposition, the XPINN method was proposed to improve the modeling effect of PINN. Finally, predicted results of PINN and XPINN were compared with those of FLUENT simulation.ResultsComparison results show that the method proposed in this paper has the following advantages: 1) Compared with the traditional CFD method, the PINN-based gas diffusion field modeling method has less error with the FLUENT solution results, and more accurate numerical results can be obtained; 2) The predictive accuracy and physical consistency of PINN-based gas diffusion field modeling approach outperform the purely data-driven neural network approach; 3) It can solve the Partial Differential Equations (PDEs) inverse problem; 4) The modeling performance of XPINN based on the idea of domain decomposition is stronger than that of the ordinary PINN method.ConclusionsThis study demonstrates that the results of PINN method are close to those of traditional CFD methods, while the eXtended Physics-Informed Neural Network (XPINN) method based on the idea of domain decomposition improves the solution of the ordinary PINN.
WU et al. (Sun,) studied this question.