Performance enhancement of axial fan blade through multi-objective optimization techniques
Performance enhancement of axial fan blade through multi-objective optimization techniques
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DOI：
10.1007/s12206-010-0619-6
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发表时间：
2010-10-01
影响因子：
1.6
通讯作者：
Kim, Kwang-Yong
中科院分区：
文献类型：
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作者：
Kim, Jin-Hyuk;Choi, Jae-Ho;Kim, Kwang-Yong
本文提出了一种结合混合多目标进化算法（混合 MOEA）的轴流风扇叶片设计优化方法。在流动分析中，使用剪应力传递湍流模型求解雷诺平均纳维-斯托克斯 (RANS) 方程。通过与实验数据进行比较，验证了轴向和切向速度的数值结果。通过实验设计 (DOE) 的拉丁超立方采样选择与叶片倾斜角和叶片轮廓相关的六个设计变量，以在选定的设计空间内生成设计点。采用总效率和扭矩两个目标函数，进行多目标优化，以提高性能。基于在指定设计点获得的数值解，为每个目标函数构建了代理模型，即响应面近似 (RSA)。带有局部搜索的非支配遗传算法（NSGA-II）用于多目标优化。获得帕累托最优解，并考虑到设计和流程约束，在两个相互冲突的目标之间进行权衡分析。据观察，通过多目标优化过程，总效率提高，扭矩降低。通过分析帕累托最优解阐明了这些性能改进的机制。
This paper presents an axial fan blade design optimization method incorporating a hybrid multi-objective evolutionary algorithm (hybrid MOEA). In flow analyses, Reynolds-averaged Navier-Stokes (RANS) equations were solved using the shear stress transport turbulence model. The numerical results for the axial and tangential velocities were validated by comparing them with experimental data. Six design variables relating to the blade lean angle and the blade profile were selected through Latin hypercube sampling of design of experiments (DOE) to generate design points within the selected design space. Two objective functions, namely, total efficiency and torque, were employed, and multi-objective optimization was carried out, to enhance the performance. A surrogate model, Response Surface Approximation (RSA), was constructed for each objective function based on the numerical solutions obtained at the specified design points. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with local search was used for multi-objective optimization. The Pareto-optimal solutions were obtained, and a trade-off analysis was performed between the two conflicting objectives in view of the design and flow constraints. It was observed that, by the process of multi-objective optimization, the total efficiency was enhanced and the torque reduced. The mechanisms of these performance improvements were elucidated by analysis of the Pareto-optimal solutions.