Affiliation:
1. JiangSu Province Key Laboratory of Aerospace Power System , Nanjing University of Aeronautics and Astronautics , NO. 29 Yudao Street , Nanjing 210016 , China
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.
Funder
National Science and Technology Major Project of China
Innovation Centre for Advanced Aviation Power , China
The Fund of Prospective Layout of Scientific Research for NUAA(Nanjing University of Aeronautics and Astronautics), China
Project funded by China Postdoctoral Science Foundation, China
Jiangsu Funding Program for Excellent Postdoctoral Talent, China