Research on a high-precision real-time improvement method for aero-engine component-level model

Abstract

Abstract In order to improve the real-time performance of the aero-engine Component-Level Model (CLM) while ensuring accuracy, a method for the Calculation of Thermodynamic Parameters of Working Fluids (CTPWF) based on a Neural Network and Newton Raphson (NN-NR) is proposed. In this method, the enthalpy or entropy under different fuel-air ratio and humidity conditions is mapped to temperature by a neural network, and the mapping output is used as the initial solution of Newton Raphson (NR) iteration. Then, a high-precision solution can be obtained through a few iterations, which avoids the shortcoming that the traditional method uses a fixed initial solution that leads to too many iterative steps. This effectively reduces the number of iterative steps and improves the calculation efficiency. This method is applied to the aero-thermodynamic calculation of each component of an engine CLM, which improves the accuracy and real-time performance of the CLM. The simulation results show that, compared to the traditional method, the proposed method improves the accuracy of the CTPWF and can reduces the single aero-thermodynamic calculation time by 25 % when humidity is not considered and by 47 % when humidity is considered. This effectively improves the real-time performance of the CLM.