Modeling method of variable cycle engine based on QPSO hybrid algorithm

A new hybrid algorithm which is based on quantum particle swarm optimization (QPSO) algorithm and Broyden quasi-Newton algorithm was proposed to reduce the effect of initial value selection on convergence speed and accuracy in solving the variable cycle engine (VCE) model. Firstly, based on the analysis of the VCE geometrical characteristics and the analysis of the steady-state characteristics of the external duct through backpropagation(BP) neural network method, a component model was established which can reflect variable geometry property and mode switching and other states of the VCE. Secondly, based on the model performance calculation, a QPSO based Broyden quasi-Newton hybrid algorithm was used to solve the VCE model cooperating equations, which improved the convergence and calculation efficiency of the hybrid algorithm by introducing the divergence coefficient to combine the two single algorithms. The effectiveness, efficiency and accuracy of the algorithm were verified by the simulation of high-order nonlinear equations. Finally, the steady state and dynamic simulation of VCE component model were carried out. The results of VCE model show that, compared with the results of GasTurb performance calculation, the trends of velocity characteristics and altitude characteristics are basically the same with those of GasTurb, the error between VCE model and GasTurb is less than 2%. The hybrid algorithm based on QPSO and Broyden quasi-Newton algorithm can solve the VCE model efficiently and quickly. The established VCE model can be used for performance simulation and analysis.