Reconstruction of RANS Model and Cross-Validation of Flow Field Based on Tensor Basis Neural Network

The solution of the Reynolds-averaged Navier-Stokes (RANS) equation has been widely used in engineering problems. However, this model does not provide satisfactory prediction accuracy. Because the widely used eddy viscosity model assumes a linear relationship between the Reynolds stress and the average strain rate tensor and these linear models cannot capture the anisotropic characteristics of the actual flow. In this paper, two kinds of flow field structures of two-dimensional cylindrical flow and circular tube jet are calculated by using the RANS model. Secondly, in order to improve the prediction accuracy of the RANS model, the Reynolds stress of the RANS model is reconstructed by the tensor basis neural network algorithm based on nonlinear eddy viscosity model. Finally, the model trained by neural network is cross-validated, and compare the cross-test results with the traditional RANS k-eps model. The results show that the multi-layer neural network method has achieved good results in turbulence model reconstruction.