基于QPSO算法的压气机特性代理模型优化

推进技术 ›› 2014, Vol. 35 ›› Issue (11): 1537-1543.

基于QPSO算法的压气机特性代理模型优化

赵　勇¹,李本威²,朱飞翔¹,张　勇³

海军航空工程学院研究生管理大队，山东烟台 264001;海军航空工程学院飞行器工程系，山东烟台 264001;海军航空工程学院研究生管理大队，山东烟台 264001;海军航空工程学院科研部，山东烟台 264001

发布日期:2021-08-15
作者简介:赵　勇(1986—)，男，博士生，研究领域为航空发动机关键部件使用寿命监视。
基金资助:
国家自然科学基金(61174031)；海军航空工程学院研究生创新基金。

Surrogate Model Optimization of Compressor Characteristics Based on QPSO Algorithm

Graduate Students’ Brigade，Naval Aeronautical and Astronautical University，Yantai 264001，China;Department of Airborne Vehicle Engineering，Naval Aeronautical and Astronautical University，Yantai 264001，China;Graduate Students’ Brigade，Naval Aeronautical and Astronautical University，Yantai 264001，China;Department of Scientific Research，Naval Aeronautical and Astronautical University，Yantai 264001，China

Published:2021-08-15

摘要/Abstract

摘要： 考虑到小样本特性数据情况下进行部件特性数据的二维线性插值精度低，提出一种基于量子粒子群优化(QPSO)算法的压气机特性代理模型优化方法。针对原始Kriging模型对其相关模型参数的初始值极度敏感以及易限于局部最优解的缺陷，利用QPSO算法对Kriging的相关模型参数进行全局寻优，克服了基于梯度的模式搜索法对于参数初始值的依赖，经测试该方法具有较好的效率以及稳定性。将该优化模型扩展应用于低压压气机特性代理模型建立与重构。经验证，在小样本特性数据下，基于QPSO的压气机特性Kriging模型仍具有较高精度，应用前景可观。

关键词: 量子粒子群优化；压气机特性；代理模型；Kriging；优化；重构

Abstract: Inspired by the fact that two-dimension linear interpolations of engine component characteristics have a low precision with a small sample of characteristic data ，a method on surrogate model optimization of compressor characteristics based on quantum-behaved particle swarm optimization (QPSO) algorithm is proposed. Firstly，considering the limitations that the original Kriging model is sensitive to the initial value of parameters of its correlation model and extremely easy to fall into local optimal solution，the QPSO algorithm is introduced to search the global optimal solution to the parameters of the Kriging correlation model，and overcome the dependence on initial values of the pattern search method based on gradient information. The experimental results show that this method has good efficiency and stability. Secondly，the optimized Kriging model based on QPSO is applied to the establishment of surrogate model and reconstruction of a low-pressure compressor characteristics. It is proved that even with a small sample of characteristic data，surrogate model of the compressor characteristics based on QPSO still has a high accuracy，and its application prospect will be considerable.

Key words: QPSO；Compressor characteristics；Surrogate model；Kriging; Optimization；Reconstruction

赵　勇,李本威,朱飞翔,张　勇. 基于QPSO算法的压气机特性代理模型优化[J]. 推进技术, 2014, 35(11): 1537-1543.

ZHAO Yong¹,LI Ben-wei²,ZHU Fei-xiang¹,ZHANG Yong³. Surrogate Model Optimization of Compressor Characteristics Based on QPSO Algorithm[J]. Journal of Propulsion Technology, 2014, 35(11): 1537-1543.

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