Research on Model Correction of Turbofan Engine Based on Quantum-behaved Particle Swarm Optimization

Abstract

In order to solve the problem that the calculation result of mathematical model and the measured data of the whole machine deviate greatly due to the conditional simplification in the modeling process and engine component difference, an engine model correction method based on Quantum-behaved Particle Swarm Optimization(QPSO) is proposed. And the engine model calculation results are compared with the measured data of the whole machine performance. The results show that using the QPSO to modify the engine model can significantly improve the accuracy of the model. Before the correction, comparing the performance calculation result of the engine model with the measured data, the maximum error reaches 4.84%. After the correction, the accuracy of the model is greatly improved, and the maximum error is only 0.966%. The correction effect is good.