Research on modeling method of aeroengine on-board model based on flight data

Abstract

In order to build an aeroengine on-board model with full envelope, full state, high accuracy and high real-time, a modeling method based on flight data is proposed. This method builds state variable model based on component level model. Considering the influence of Reynolds number, power extraction, air bleed and other factors, the steady state model of the on-board model is modified based on regression analysis using flight data to reduce the modeling error caused by individual engine differences. At the same time, in order to compensate the residual steady-state error, a steady-state error model based on Gaussian Mixture Model Neural Network (GMM-NN) is established. Considering the need to reconstruct the speed sensor, the speed signal cannot be used as the scheduling variable to build a new scheduling variable, which has less dynamic error compared to taking fuel as the scheduling variable. Compared with the traditional model, the input of this model is only control variables and flight conditions, and it can reconstruct the signals of speed, pressure, temperature and other sensors. At the same time, it has the advantages of simple structure, no iterative calculation and high accuracy. Compared with flight data, the maximum dynamic error of compressor outlet total pressure of the new scheduling variable model is 3.564%, which is 4.13 times higher than the maximum relative error of 14.735% of the fuel scheduling model. In the verification of multi flight data, the average errors of LP rotor speed, HP rotor speed, compressor outlet total pressure and LP turbine outlet total temperature are 0.52%, 0.39%, 0.53% and 0.9% respectively, meeting the accuracy requirements of the project.