高功率密度斜向支柱锥齿轮辐板结构设计优化
Optimal Design of a Tilting Prop Bevel Gear Spoke Plate Structure with High Power Density
郝文康 1李坚 2文长龙 2孟卫华 2闫成1
作者信息
- 1. 厦门大学航空航天学院,厦门 361102,中国
- 2. 中国航发湖南航空动力研究所直升机传动技术国家级重点实验室,株洲 412002,中国
- 折叠
摘要
为满足直升机传动系统对复杂工况下齿轮轻量化设计的迫切需求,提出了一种高功率密度斜向支柱锥齿轮辐板结构的设计优化方法.基于考虑应力约束的变密度法,对锥齿轮辐板进行了拓扑优化与重构,得到一种新颖的突破传统构型的高功率密度斜向支柱锥齿轮辐板结构.在传统粒子群优化(Particle swarm optimization,PSO)算法的基础上,引入马尔可夫链和进化因子,自适应地选择跳变策略和延迟信息,进而发展了智能先进的时滞跳变粒子群优化(Switching delayed PSO,SDPSO)算法,并将其应用于斜向支柱锥齿轮辐板结构的尺寸优化.优化后,锥齿轮辐板质量共减小了19.24%,所有工况的最大von Mises应力共减小了7.27%,各工况应力分布更加均匀,证明了所设计的锥齿轮辐板的结构优势和所提出的设计优化方法的先进性.
Abstract
To meet the urgent need for the lightweight design of gears under complex conditions in helicopter transmission systems,a design optimization method is proposed for a tilting prop bevel gear spoke plate structure with a high power density.Based on the variable density method considering stress constraints,topology optimization and reconstruction of the bevel gear spoke plate structure are performed,which yield a novel tilting prop bevel gear spoke plate structure with a high power density,differing from the traditional configuration.Introducing an evolution factor and the Markov chain based on the traditional particle swarm optimization(PSO)algorithm,an intelligent and advanced switching delayed PSO(SDPSO)algorithm is developed.The SDPSO algorithm can adaptively select switching strategies and delay information,and it is employed for the size optimization of a tilting prop bevel gear spoke plate structure.After optimization,the mass of the bevel gear spoke plate is reduced by 19.24%,and the maximum von Mises stress of all the operating conditions is reduced by 7.27%.Additionally,the stress distribution of each operating condition becomes more uniform,which demonstrates the structural advantages of the designed bevel gear spoke plate and the superiority of the proposed optimization method.
关键词
粒子群优化算法/拓扑优化/尺寸优化/锥齿轮辐板Key words
particle swarm optimization(PSO)algorithm/topology optimization/size optimization/bevel gear spoke plate引用本文复制引用
基金项目
国家自然科学基金(52005421)
福建省自然科学基金(2020J05020)
国家科技重大专项(J2019-I-00130013)
中央高校基本科研业务费专项(20720210090)
China Postdoctoral Science Foundations(2020M682584)
China Postdoctoral Science Foundations(2021T140634)
出版年
2024
国家科技期刊平台
NSTL
万方数据