基于多岛遗传算法的翼型多目标优化设计
Multi-objective optimal design of airfoil based on multi-island genetic algorithm
张之阳 1刘旭 1吴林艳 1陈少华 1刘葳兴 2崔琳3
作者信息
- 1. 江苏海洋大学 海洋工程学院,江苏 连云港 222005
- 2. 江苏海洋大学 马卡洛夫海洋工程学院,江苏 连云港 222005
- 3. 国家海洋技术中心,天津 300112
- 折叠
摘要
[目的]针对大型水平轴水轮机叶片运行工况复杂的问题,提出一种多目标优化算法.[方法]基于多岛遗传算法建立翼型优化模型,采用类形函数变换(CST)法对翼型进行参数化拟合,整个优化过程集成于Isight平台,实现自动优化.[结果]采用上述方法,选用NACA 63813/63815/63816 翼型作为初始翼型进行多目标优化,利用Fluent转捩模型对得到的翼型进行CFD数值验证,选择翼型攻角 5°时的升阻比、升力等为优化目标参数,得到优化后的翼型升力系数分别增大了 14%,15%,20%,升阻比分别增大了 14%,16%,28%.[结论]数值验证结果表明,优化后的翼型在多个工况点下的升阻比均高于同厚度原始翼型,在具有良好的水动力动性能的同时还提高了叶片的结构强度,相比于传统的翼型更适用于大型潮流能水平轴水轮机.
Abstract
[Objectives]A multi-objective optimization algorithm is proposed to address the problem of the complex operating conditions of large horizontal axis hydraulic turbine blades.[Methods]An airfoil optim-ization model is established based on the multi-island genetic algorithm,the airfoil is parametrically fitted us-ing the class shape function transformation(CST)function method,and the whole optimization process is in-tegrated on the Isight platform to achieve automatic optimization.[Results]Using the above method,NACA 63813/63815/63816 airfoils are selected as the initial airfoils for multi-objective optimization,CFD nu-merical validation is carried out on the obtained airfoils using the Fluent turning model,and the lift-to-drag ra-tios and lifting forces at the airfoil attack angle of 5° are selected as the optimization objectives,resulting in the optimized airfoils gaining increased lift coefficients of 14%,15%and 20%,and increased lift-to-drag ratios of 14%,16%and 28%,respectively.[Conclusions]Numerical validation shows that the lift-to-drag ratios of the optimized airfoil is higher than those of the original airfoils with the same thickness under several operat-ing conditions,and the structural strength of the blade is improved while ensuring good aerodynamic perform-ance,making it more suitable than conventional airfoils for large-scale tidal current energy horizontal axis hydraulic turbines.
关键词
水轮机/多岛遗传算法/多目标优化/翼型/类形函数变换参数化方法Key words
hydraulic turbine/multi-island genetic algorithm/multi-objective optimization/airfoil/class shape function transformation(CST)parametric approach引用本文复制引用
基金项目
国家自然科学基金(52101356)
国家自然科学基金(52001138)
连云港市重点研发计划(CG2224)
连云港市"521工程"科研项目(第六期)(LYG06521202337)
出版年
2024
国家科技期刊平台
NSTL
万方数据