基于随机排序微分进化算法的多级轴流压气机优化技术

推进技术 ›› 2014, Vol. 35 ›› Issue (3): 328-334.

基于随机排序微分进化算法的多级轴流压气机优化技术

杨小东,刘波

西北工业大学动力与能源学院, 陕西西安 710072;西北工业大学动力与能源学院, 陕西西安 710072

发布日期:2021-08-15
作者简介:杨小东(1986—),男,博士生,研究领域为叶轮机参数化设计及优化技术。 E-mail:yangxiaodong1986@163.com
基金资助:
国家自然科学基金资助项目 (51236006)。

Optimization of Multi-Stage Axial-Flow Compressor Based on Stochastic Ranking Differential Evolution Algorithm

School of Power and Energy, Northwestern Polytechnical University, Xi’an 710072, China;School of Power and Energy, Northwestern Polytechnical University, Xi’an 710072, China

Published:2021-08-15

摘要/Abstract

摘要： 为了精确处理多级轴流压气机优化设计过程中的约束条件,提高优化设计可靠性,引入了随机排序微分进化算法。使用随机排序算法处理优化问题中普遍存在的约束条件,并将该算子与微分进化算法耦合。使用13个基准测试函数对算法进行了性能测试。测试结果表明该算法具有可观的精确性和稳定性。随后,对一弹用5级轴流压气机进行了优化设计,在满足约束的条件下,总压和等熵效率分别得到了1.979%和2.5%的效率提升。 

关键词: 微分进化;随机排序;约束优化;流线曲率法;轴流压气机 

Abstract: Stochastic Ranking Based Differential Algorithm is presented with the purpose of disposing constraints precisely and enhancing the reliability of the optimization of multi-stage axial compressor. Stochastic ranking is introduced to handle the complex constraints which are common emergence and then it is coupled with differential evolutionary algorithm. 13bench-mark test functions are used to check the algorithm performance and satisfied results are obtained. The algorithm is validated by optimizing a five-stage compressor. 1.979% and 2.5% improvement is obtained for total pressure and isentropic efficiency, respectively, while all the constraints are obeyed. 

Key words: Differential evolution; Stochastic ranking; Constrained optimization; Streamline curvature; Axial-flow compressor

杨小东,刘波. 基于随机排序微分进化算法的多级轴流压气机优化技术[J]. 推进技术, 2014, 35(3): 328-334.

YANG Xiao-dong, LIU Bo. Optimization of Multi-Stage Axial-Flow Compressor Based on Stochastic Ranking Differential Evolution Algorithm[J]. Journal of Propulsion Technology, 2014, 35(3): 328-334.

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