基于TILS算法的汽轮机叶片排序方法
Steam turbine blade sequencing method based on TILS algorithm
刘谊 1郭闯强 2朱映远 1张庆利3
作者信息
- 1. 哈尔滨工业大学机电工程学院,哈尔滨 150001
- 2. 哈尔滨工业大学机电工程学院,哈尔滨 150001;哈尔滨工业大学芜湖机器人产业技术研究院,安徽芜湖 241060
- 3. 中国航天科技集团有限公司中国运载火箭技术研究院,北京 100076
- 折叠
摘要
通过优化汽轮机叶片的安装顺序,来减少安装后的残余不平衡量.对此提出一种阈值式迭代局部搜索(threshold iterative local search,TILS)算法,该算法在迭代局部搜索(iterative local search,ILS)算法基础上,采用阈值限定扰动与随机扰动相结合的方法来跳出局部最优解,减少了平均到达局部最优解所需的迭代步数.实验证明,该方法可以在短时间内找到一个近似最优叶片排序组合,相对于ILS算法,搜索效率提高了 20％以上.计算得到的合成质径积的近似最优解,相对于现有分组排序法、遗传算法、云自适应遗传算法(CAGA)等方法,分别减小到其最优解的0.33％～31％,且计算时间也大幅度减小.
Abstract
To reduce the residual unevenness after installation by optimizing the installation sequence of turbine blades,a threshold iterative local search algorithm(TILS)was proposed.Based on the iterative local search algorithm(ILS),this algorithm adopted the combination of threshold limited disturbance and random disturbance to escape from local optimum,which reduced the number of iteration steps required to reach the local optimal solution on average.Experiments demonstrated that this method can find the approximately optimal combination of blade sequences in a relatively short time,which improved the search efficiency by more than 20％compared with the ILS algorithm.Compared with existing group sorting methods,genetic algorithms,and cloud adaptive genetic algorithm(CAGA)algorithms,the approximately optimal solution of synthetic mass product calculated by this algorithm was reduced to 0.33％-31％,and the computation time was significantly shorter.
关键词
汽轮机/叶片排序/组合优化/迭代局部搜索(ILS)/阈值式迭代局部搜索(TILS)/局部搜索Key words
steam turbine/sequencing blade/combination optimization/iterative local search(ILS)/threshold iterative local search(TILS)/local search引用本文复制引用
基金项目
哈尔滨工业大学芜湖机器人产业技术研究院资助项目(HIT-CXY-CMP2-RVJDT-21-01)
出版年
2024
NETL
NSTL
万方数据