| 基于改进量子粒子群的发动机部件特性修正 |
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| 引用本文： | 杨欣毅,王永华,贺孝涛,逢珊,沈伟.基于改进量子粒子群的发动机部件特性修正[J].航空计算技术,2012(2):4-8. |
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| 作者姓名： | 杨欣毅 王永华 贺孝涛 逢珊 沈伟 |
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| 作者单位： | [1]海军航空工程学院飞行器工程系,山东烟台264001 [2]海军驻西安地区航空军事代表室,陕西西安710021 [3]鲁东大学信息与电气工程学院,山东烟台264025 |
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| 基金项目： | 国家自然科学基金（青年基金）项目资助（61102167）; 航空科学基金与航空电子系统综合技术国防科技重点实验室基金项目联合资助（20095584006）; 山东省科技发展计划项目资助（2011YD04049） |
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| 摘 要： | 为提高发动机部件特性修正的精度,在分析修正因子法的求解条件以及目标方程的选取原则的基础上,利用部件特性删除法,直接以各部件特性参数作为被优化变量进行特性修正。对于目标函数,提出利用量子粒子群(QPSO)算法优化求解,并针对算法存在早熟收敛的问题进行。改进以涡扇发动机试车试验数据为依据,分别利用改进算法和其他典型算法进行部件特性修正计算。计算和试验结果对比表明,算法要明显优于其他被比较的算法。
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| 关 键 词： | 航空发动机 部件 特性 量子粒子群优化 |
Aeroengine Component Characteristic Correction Based on Enhanced Quantum-behaved Particle Swarm Optimization |
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| YANG Xin-yi,WANG Yong-hua,HE Xiao-tao,PANG Shan,SHEN Wei.Aeroengine Component Characteristic Correction Based on Enhanced Quantum-behaved Particle Swarm Optimization[J].Aeronautical Computer Technique,2012(2):4-8. |
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| Authors: | YANG Xin-yi WANG Yong-hua HE Xiao-tao PANG Shan SHEN Wei |
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| Institution: | 1.Department of Aerocraft Engineering,Naval Aeronautical and Astronautical University,Yantai 264001,China; 2.Navy Aeronautical Military Representative Office in Xi′an,Xi′an 710021; 3.School of Information and Electrical Engineering,Ludong University,Yantai 264001,China.) |
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| Abstract: | In order to improve the accuracy of engine component characteristic map correction,modification factors method for engine components characteristic correction is discussed in this paper.Solving requirements and selecting principle of target equations are analyzed in detail.Based on component characteristic delete method,characteristic parameters of all components were directly chosen as the to-be-adapted parameters.And the objective function is optimized using quantum-behaved particle swarm optimization（QPSO）.To solve the premature convergence problem of QPSO,an enhanced version of QPSO is proposed.Based on ground test data of an existing turbofan engine,the proposed algorithm and three other typical algorithms were applied in engine components characteristic correction.Comparison between calculated result and test data show the proposed algorithm shows better performance than the others. |
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| Keywords: | aeroengine component characteristics quantum-behaved particle swarm optimization |
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