Application Exploration of Flow Field Prediction in Free Jet Tests Based on Proper Orthogonal Decomposition Method

This study focuses on the demand for rapid prediction of the flow field of aero-engine inlet free jet tests and explores the application of radial basis function interpolation (RBF) and backpropagation neural network (BPNN) methods. The proper orthogonal decomposition (POD) method was used for model order reduction of the full-order flow field results from numerical simulations. Two rapid prediction methods, namely POD-RBF and POD-BPNN, were constructed by utilizing radial basis function interpolation and a backpropagation neural network. These methods successfully achieved rapid prediction of the test flow field under different Mach numbers and angles of attack. To verify the accuracy of the numerical simulation and rapid prediction methods, a free jet test of the jet inlet was conducted under the same conditions. The test results show good agreement with both the CFD calculation results and the rapid prediction results. The research results show that the ninth-order mode can accurately reconstruct the flow field structure with a reconstruction error of 1.83%. Both methods can quickly and accurately predict the flow field under different conditions, and the prediction results are in good agreement with the numerical simulation results. Generally speaking, the prediction error of the POD-BPNN method is smaller than that of the POD-RBF method.