改进鲸鱼优化算法及涡轮盘结构优化

doi:10.3901/JME.2021.20.254

机械工程学报 ›› 2021, Vol. 57 ›› Issue (20): 254-265.doi: 10.3901/JME.2021.20.254

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改进鲸鱼优化算法及涡轮盘结构优化

曾念寅, 宋丹丹, 李寒, 闫成, 尤延铖

厦门大学航空航天学院厦门 361005

收稿日期:2021-01-05 修回日期:2021-07-31 出版日期:2021-10-20 发布日期:2021-12-15
通讯作者: 闫成(通信作者),男,1992年出生,博士,助理教授。主要研究方向为拓扑与多学科设计优化。E-mail:yanchengmail@xmu.edu.cn
作者简介:曾念寅,男,1986年出生,博士,副教授。主要研究方向为智能系统与计算,计算/人工智能及其应用。E-mail:zny@xmu.edu.cn;宋丹丹,女,1997年出生,硕士研究生。主要研究方向为智能优化算法,结构优化。E-mail:songdandan@stu.xmu.edu.cn;李寒,男,1996年出生,博士研究生。主要研究方向为智能优化算法,深度学习。E-mail:hanliy@stu.xmu.edu.cn;尤延铖,男,1981年出生,博士,教授,博士研究生导师。主要研究方向为高超声速空气动力学,推进系统概念和设计。E-mail:yancheng.you@xmu.edu.cn
基金资助:
国家自然科学基金（62073271，52005421）、中央高校基本科研业务费（20720190009）、福建省科技计划对外合作（2019I0003）和福建省自然科学基金（2020J05020）资助项目。

Improved Whale Optimization Algorithm and Turbine Disk Structure Optimization

ZENG Nianyin, SONG Dandan, LI Han, YAN Cheng, YOU Yancheng

School of Aerospace Engineering, Xiamen University, Xiamen 361005

Received:2021-01-05 Revised:2021-07-31 Online:2021-10-20 Published:2021-12-15

摘要/Abstract

摘要： 涡轮盘是航空发动机的核心零件，通过智能优化算法对涡轮盘结构进行优化，降低涡轮盘质量，有助于提升推重比。研究并改进了新型鲸鱼优化算法对涡轮盘截面进行结构尺寸优化。改进的鲸鱼优化算法中创新引入差分变异策略，交叉操作和常规变异策略以增强鲸鱼优化算法跳出局部最优解的能力。特别地，以改进的鲸鱼优化算法（Decomposition evolution based whale optimization algorithm，DEWOA）为例对Isight平台进行二次开发，与Isight平台中的五种算法和基本鲸鱼优化算法就八个单目标测试函数进行了对比，在均值和方差等指标验证了改进算法的稳定性和寻优性能。而且，将结合改进鲸鱼优化算法的Isight优化模块组件与有限元分析方法在Isight平台中集成为全自动化优化流程，对航空发动机涡轮盘进行结构优化，并与其他四种算法的涡轮盘结构优化结果进行对比。实验结果表明改进的鲸鱼优化算法对涡轮盘减重达26.09%，超过自适应模拟退火算法减重比2.24%，超过多岛遗传算法减重比5.29%，超过鲸鱼优化算法减重比1.94%，落后于指针自动优化算法减重比0.39%，但改进的鲸鱼优化算法收敛速度更快，达到最优解的代价更小，表明了改进鲸鱼优化算法在工程实际问题上的实用性和通用性。

关键词: 涡轮盘, 结构优化, Isight平台, 减重, 改进鲸鱼优化算法

Abstract: As one of the core parts of aero engine, the structure of turbine disk can be optimized through intelligent algorithms to improve thrust-weight ratio of the engine. An improved whale optimization algorithm (decomposition evolution based whale optimization algorithm, DEWOA) is proposed to optimize the structure of a turbine disk section. The proposed algorithm is innovatively integrated with differential mutation, crossover operation and conventional mutation to enhance the ability of basic whale optimization algorithm so as to jump out of the local optimum. In particular, the proposed algorithm is embedded in Isight platform, compared with five algorithms and basic whale optimization algorithm in the Isight on eight benchmark functions. The relevant results of mean and variance show the stability and the ability to find the optimal solution of DEWOA. Moreover, a fully automated optimization process is built in Isight on optimization module components chosen DEWOA and finite element analysis method to optimize the structure of a turbine disk. The results are compared with other algorithms as in the compared benchmark functions. Experimental results show that the improved whale optimization algorithm can reduce the weight of the turbine disk by 26.09%, which exceeds the weight reduction ratio of the adaptive simulated annealing algorithm by 2.24%, exceeds the weight reduction ratio of the multi-island genetic algorithm by 5.29%, and exceeds the weight reduction ratio of the whale optimization algorithm by 1.94%. It lags behind the pointer automatic optimization algorithm with a weight reduction ratio of 0.39%, but the improved whale optimization algorithm converges faster and the cost of reaching the optimal solution is lower, which shows the practicality and versatility of the improved whale optimization algorithm in practical engineering problems.

Key words: turbine disk, structure optimization, Isight, loss weight, improved whale optimization algorithm

中图分类号:

V232

曾念寅, 宋丹丹, 李寒, 闫成, 尤延铖. 改进鲸鱼优化算法及涡轮盘结构优化[J]. 机械工程学报, 2021, 57(20): 254-265.

ZENG Nianyin, SONG Dandan, LI Han, YAN Cheng, YOU Yancheng. Improved Whale Optimization Algorithm and Turbine Disk Structure Optimization[J]. Journal of Mechanical Engineering, 2021, 57(20): 254-265.

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改进鲸鱼优化算法及涡轮盘结构优化

Improved Whale Optimization Algorithm and Turbine Disk Structure Optimization

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