Research on Aerodynamic Performance Influence of Cascade Error Based on Deep Neural Network

The aerodynamic performance of the blade cascade is greatly affected by the change of the blade shape error. In order to study the influence of the small geometric error change on the compressor performance, statistical theory often requires a large number of example data to verify, and the process often needs too much time and cost. This paper proposes a method for predicting the steady flow field of incompressible laminar flow on variable cascades based on Deep Neural Network (DNN). This method is used to analyze the influence of the aerodynamic performance of the cascade on the refined error model. The method proposed in this paper can approximate the flow field as a function of the cascade deformation error and the angle of attack and the incoming flow Mach number during the work, without the need to solve the Navier-Stokes (N-S) equation used in the traditional method. The Latin hypercube combined with Monte Carlo method is used to generate a large number of geometric error parameters combined with flow field parameters as input, and the prediction is made through the trained DNN. The flow field prediction of a large number of samples can be completed in a short time, and the sensitivity relationship between the cascade error and the total pressure loss coefficient can be obtained by using the prediction results. © 2024 Science Press. All rights reserved.