采用遗传算法的离心叶轮多目标自动优化设计

Multi-Objective Automatic Optimization Design of Centrifugal Impeller Based on Genetic Algorithm

针对离心叶轮多目标自动优化设计,首先提出了一种离心叶轮参数化方法,通过对离心叶轮型线数据进行转换,分别建立了圆柱坐标系下叶轮控制参数与归一化长度参数之间的关系,采用非均匀有理B样条进行叶型重构,获得叶片和叶轮子午流道构型.将离心叶轮参数化方法、多目标遗传算法及商业化数值计算软件NUMECA相结合,建立起离心叶轮自动优化设计平台.以总压比和效率为设计目标,在设计工况下,采用该方法对Krain高速离心叶轮进行气动优化设计,获得一系列优化叶轮,并采用数值模拟方法对叶轮内部流动特性及其气动性能进行了比较分析.结果表明,优化叶轮的总体气动性能有不同程度的改善,在叶轮出口截面上流场分布更加均匀,总压比和等熵效率约提高了2.5%和1.0%,同时也验证了所发展的多目标离心叶轮自动优化设计方法的有效性.

A parameterization method is developed to perform the multi-objective optimization design of centrifugal impellers.The relationship of the impeller parameters and the dimensionless length in the cylindrical coordinate system is established by the transformation of impeller profile data.Non-uniform rational B-spline is used to reconstruct the impeller to obtain the profile of the blade and the meridional surface.An automatic optimization design platform for the centrifugal impellers is constructed by the multi-objective genetic algorithm combined with parameterization method and the commercial computational fluid dynamics software NUMECA.The Krain high-speed centrifugal impeller is optimized by using the present method at the design condition,and the aerodynamic performance and the flow characteristics are analyzed.The numerical results show that the aerodynamic performance of the optimal impellers is improved to some extent.The flow field distribution at the outlet of the optimal impellers is more uniform.The total pressure ratio and the isentropic efficiency increases by about 2.5% and 1.0% respectively.These findings confirm that the present multi-objective optimization design method for the centrifugal impellers is effective.