基于改进粒子群算法的航空发动机性能综合评价 |
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引用本文: | 赵凯,李本威,李冬,李海宁.基于改进粒子群算法的航空发动机性能综合评价[J].航空发动机,2014,40(6):13-17. |
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作者姓名: | 赵凯 李本威 李冬 李海宁 |
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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. 大连理工大学船舶工程学院CAD工程中心,辽宁大连,116024 |
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基金项目: | 国家自然科学基金(青年基金)(61102167)资助 |
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摘 要: | 针对基于单参数评估发动机性能能力不足的问题,研究了利用多参数综合评估发动机性能的方法;通过对某型发动机台架试车数据分析,确定了使用综合加权法评估发动机性能比算术加权平均法更具合理性;分别利用改进的遗传算法和粒子群优化算法计算多参数的权值,对比结果表明:使用改进的粒子群算法在计算精度和速度上均优于遗传算法。同时还计算了各翻修次数下发动机的性能指标。
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关 键 词: | 多参数权值 综合评判 遗传算法 粒子群算法 航空发动机 |
Comprehensive Evaluation of Aeroengine Performance Based on Improved PSO |
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Authors: | ZHAO Kai LI Ben-wei LI Dong LI Hai-ning |
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Institution: | 1. Department of Airborne Vehicle Engineering, NAAU, Yantai Shandong 264001, China; 2. Institute of Marine Engineering,Dalian University of Technology, Dalian Liaoning 116024, China |
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Abstract: | To solve the lack of ability problem for evaluate engine performance based on single parameter, the method of evaluating engine performance based on multiple parameters was studied. By analyzing the bench test data for an aeroengine, the results show that the synthetic weighted method was more rational than arithmetic weighted mean method for evaluating engine performance. Weights of multiple
parameters were calculated by the improved genetic algorithm and particle swarm optimization algorithm. The comparative study shows that the improved particle swarm algorithm is superior to the improved genetic algorithm in the calculation result. Finally, the engine performance index of each overhaul was calculated. |
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Keywords: | multi-parameter weights comprehensive evaluation genetic algorithm particle swarm optimization aeroengine |
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