QPSO算法在航空发动机性能优化中的应用

2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering Pub Date : 2011-09-01 DOI:10.1109/CCIENG.2011.6008146

Bao E-er-dun, Wang Xiao-ping, Xue Jian-ping, Liu Qin, Wang Fa-wei

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引用次数: 1

摘要

将量子粒子游动优化算法(QPSO)应用于某型涡轮风扇发动机的性能优化。本文通过与粒子群算法的比较，发现QPSO算法具有明显的优势。在不同的高度和速度下进行了仿真。结果表明，在最大推力模式下，推力比粒子游优化(PSO)算法提高了7% ~ 9%，提高了0.3% ~ 3.7%。同时，在最低油耗模式下，油耗可降低2% ~ 3%。减小了初始值对粒子群算法的影响，解决了粒子群算法容易陷入局部最优的问题。显然，该算法具有很大的应用价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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QPSO algorithm in aeroengine performance optimization of application

A novel and practical method which is Quantum-behaved Particle Swam Optimization (QPSO) algorithm is applied in one type of turbo fan engine performance optimization. In this paper, by comparison with PSO algorithm, QPSO algorithm have obvious advantages. The simulation is carried out under different altitudes and velocities. The result shows that thrust can be increased by 7% ∼ 9% under maximum thrust mode and improved by 0.3% ∼ 3.7% than that is optimized by Particle Swam Optimization (PSO) algorithm. Meanwhile, fuel consumption can be decreased by 2% ∼ 3% under the minimum fuel consumption mode. The influence of initial values on PSO algorithm is reduced and the problem of being easily trapped in local optimal values is solved as well. Apparently, the algorithm is of great application value.

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2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering

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