Turbofan Engine Performance Based On Genetic Algorithm Optimization Control

With the fast-developing aeronautical technology and fast-enhancing computing speed, the traditional design mode, airplane control system and engine control system designed respectively, would not meet the requirements of military aircrafts. Integrated Flight/Propulsion Control (IFPC) is a inevitable direction. Performance Seeking Control (PSC) is the vital technology of IFPC.This paper first takes an overview of the internal and abroad PSC systems, and then presents an evolutionary approach (Genetic Algorithm, short as GA) as a new optimization framework to design for optimal performance in terms of two criteria below: maximizing thrust while non-augmented, minimizing fuel consumption while maintaining thrust output. The PSC problem was converted into nonlinear programming problem. The nonlinear component level engine mathematical model was analyzed and supplied with the performance optimization part and the practical constraints part. The simulation shows that the modified model can be used in above PSC problem.Secondly, the composition, theory, characteristic and realization of GA was introduced. The optimization process was realized on the combination of turbo-fan model and GAlib, a C++ library developed by MIT. The simulation of non-augmented maximum thrust mode and cruise state while maintaining thrust output at ground design point shows that GA can be used in turbo-fan PSC problem. Furthermore, the parameters of GA are optimized. The results indicate the optimization could accelerate converge speed and save computing cost.