摘要
针对变循环发动机非线性部件模型共同工作方程组求解时初值选取对收敛速度和精度的影响问题,提出一种基于量子粒子群优化(QPSO)算法与Broyden拟牛顿法混合的求解思路。首先,对变循环发动机(VCE)进行变几何特性分析以及反向传播(BP)神经网络下的外涵道稳态特性分析基础上,建立反映变几何特性以及模式切换等全状态部件模型。其次,以该模型性能计算为基准,提出了一种基于QPSO的Broyden拟牛顿混合算法来达到发动机共同工作平衡要求,通过发散系数实现混合算法的切换,以改善单一Broyden拟牛顿法对初值选取的依赖性同时提高QPSO算法的求解效率。通过高阶非线性方程组的仿真验证了算法的有效性、求解效率以及精度。最后,进行VCE部件模型稳态、动态仿真计算,结果表明:与Gas Turb性能计算结果对比可以看出发动机速度特性、高度特性等变化趋势与Gas Turb基本一致,且误差均小于2%;基于QPSO的Broyden拟牛顿混合算法可有效快速地完成VCE部件模型的求解;所建VCE部件模型能够有效实现该新型发动机的性能模拟分析。
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 Gas Turb performance calculation,the trends of velocity characteristics and altitude characteristics are basically the same with those of Gas Turb,the error between VCE model and Gas Turb 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.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2018年第2期305-315,共11页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家自然科学基金(51506176)
航空科学基金(6141B090302)
中央高校基本科研业务费专项资金(G2017KY0003)~~
