The Research On Turbo-fan Aeroengine Adaptive Modeling

This has two parts. In the first part, the mathematic model of a certain type of turbofan areoengine is discussed. At first, a thermodynamic model for this engine is set up, and a six order nonlinear equations are derived from the thermodynamic model. The steady point or balance point can be obtained by solving these nonlinear equations. Then, thj real time aeroengine dynamic model is built up by "one-pass through algorithm". During the process of modeling, questions caused by shortness of exact component characteristics and correct data of some stations, such as pressure recovery coefficient and extracted power, were solved.In the second part, adaptive modeling is discussed. In this paper a new method based on neural networks is proposed for aeroengine adaptive real time modeling. It is thought that output data of aeroengine will bias their nominal values in any cases of non-nominal work. So the biases of output data can be used to represent the non-nominal work conditions of the aeroengine and constitute the augment state variables. Kalman filter is designed to estimate these biases. Then the unmeasured outputs of the onboard model of the areoengine can be modified by the estimation of the unmeasured biases which are obtained through the mapping relation constructed between the measured output biases and unmeasured ones and trained by the BP neural networks offline. After modification, outputs of onboard model are the same as those of the real aeroengine, and real time onboard model has the abilities of adaptation.