Optimization Of Turbofan Engine Acceleration Control Law

Aero engine accelerating control is one of researches of engine control. This paper focuses on an turbofan engine as the research object and an advance engine dynamic model has been build to research the control law in the accelerating operation. Then, the accelerating process has been analyzed by the theory of engine operation and displays the characteristics of acceleration. According those conclusions, a new optimized model about the accelerating control has been developed and then applied the GA (General Algorithm) to realize the result.The whole engine model was constructed of components of engine. Refer to foreign documents, the simplified method of components has been used in the building of engine model and the model improved the efficiency of calculation.In this paper, analysis of the accelerating process is the key point. The process has been divided into three parts: The tip of acceleration, the Mid-Segment and the end. The paper showed the alteration about different control values and limits in each part as well as the influences. Based analyses from the divided parts, the different fitness functions have been set. Simultaneously, considered the limits about the condition parameters and the physical limits about the control components such as the surge margin, fore turbine temperature limit, maximum rotate speed of axes, limit of rich air in chamber, etc., the model was completed.At last, the model based on GA optimized the engine character of acceleration originally and introduce the crossover operator, encoding, selection and mutation, etc. The program has been developed and simulated the process.In the application of GA, the model compared the different values to each other. The result showed that the different crossover coefficients and mutation coefficients have influenced the model result enormously.The research results are presented that the GA applied in the aero engine simulation is viable and the optimized control law is valid. Compared to the traditional optimization algorithm, GA is more effective than them to improve the acceleration performance.