Steady-State Performance Model Identification Method of Low Bypass Ratio Turbofan Engine Based on Evolutionary Algorithm

Due to the high test cost and unreliable measuring instruments or methods, it is often difficult to obtain test data of aero-engines, and it is a challenging problem to model engines based on the test data. In this paper, an identification and modeling method of the low bypass ratio mixed turbofan engine based on differential evolutionary algorithm (DE) is proposed. This algorithm is used to find the optimal solution of the simulation model parameters in each working condition, so that the model fits the actual engine. The whole model identification method is streamlined and modular, which can be added, deleted and adjusted according to the actual information and data. In this paper, the method is used to identify a turbofan engine. According to the intermediate state test data of 12 different working conditions under International Standard Atmosphere (ISA), the control schedule and component characteristics in intermediate states are identified. And the maximum state control schedule is obtained. The identification results are verified by using the intermediate state test data under hot day and the maximum state test data. The average error calculated by the identified overall performance simulation model is 0.395%, 0.531% and 0.498% in the standard intermediate state, hot day intermediate state and maximum state, respectively. This general method can also be directly applied to other types of engines by modifying the model mechanism.