Strengthened elitist genetic algorithm for aeroengine blade arrangement optimisation

IF 2.3 4区工程技术 Q2 ENGINEERING, MECHANICAL

Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering Pub Date : 2024-07-25 DOI:10.1177/09544089241262965

Wuguo Wei, Chao Wu, Junjie Zhang

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引用次数: 0

Abstract

Variations in mass moment of aeroengine blades can cause different residual unbalances, leading to exceeding vibration limit values. For this reason, an accurate and rapid selection of a suitable blade arrangement is essential for improving assembly quality and efficiency. This work innovatively applies the strengthened elitist genetic algorithm (SEGA) to the optimization of blade arrangement for aeroengine. This work investigates the effects of population size (Ps: 100–500), population crossover probability (Pc: 0.6–0.9), and population mutation probability (Pm: 0.6–0.9) on the convergence speed and accuracy of the algorithm, respectively. The obtained results indicate that the optimal parameters of the algorithm are 300 for Ps, 0.7 for Pc, and 0.9 for Pm, which can quickly search for high-precision solution. Compared to the elitist genetic algorithm (EGA), the accuracy value of SEGA is improved about 82%. In addition, this work conducted simulated vibration platform to verify SEGA, and the obtained results show that the vibration values are reduced by 4.19%, 10.19%, and 2.99% at stable speeds of 1000, 1500, and 2000 rpm, respectively, compared to that of group sorting. This work employs a novel SEGA that can effectively improve the accuracy value and reduce vibration values compared to EGA and group sorting, respectively. The above may reduce the residual unbalances of the aeroengine, improve the quality of assembly, and provide a new idea for the assembly of blades.

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用于优化航空发动机叶片布置的强化精英遗传算法

航空发动机叶片质量力矩的变化会造成不同的残余不平衡，从而导致超过振动极限值。因此，准确、快速地选择合适的叶片排列方式对于提高装配质量和效率至关重要。本研究创新性地将强化精英遗传算法（SEGA）应用于航空发动机叶片排列的优化。研究了种群规模（Ps：100-500）、种群交叉概率（Pc：0.6-0.9）和种群突变概率（Pm：0.6-0.9）分别对算法收敛速度和精度的影响。结果表明，该算法的最优参数为：Ps：300；Pc：0.7；Pm：0.9，可以快速搜索到高精度的解。与精英遗传算法（EGA）相比，SEGA 的精度值提高了约 82%。此外，这项工作还进行了模拟振动平台来验证 SEGA，结果表明，在稳定转速为 1000、1500 和 2000 rpm 时，与分组排序相比，振动值分别降低了 4.19%、10.19% 和 2.99%。与 EGA 和分组分拣相比，本研究采用的新型 SEGA 可分别有效提高精度值和降低振动值。上述方法可减少航空发动机的残余不平衡，提高装配质量，并为叶片装配提供了新思路。

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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来源期刊

Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 工程技术-工程：机械

CiteScore

3.80

自引率

16.70%

发文量

370

审稿时长

6 months

期刊介绍： The Journal of Process Mechanical Engineering publishes high-quality, peer-reviewed papers covering a broad area of mechanical engineering activities associated with the design and operation of process equipment.