大功率机车用轴流冷却风机叶轮气动性能优化

摘要

This paper presents an optimization procedure based on a artificial neural network surrogate model for design of for axial-flow cooling fan impeller. Numerical analysis of air-flow in the impeller has been carried out by solving three-dimensional Reynolds-averaged Navier-Stokes equations with the Spalar-Allmaras turbulence model. The optimization processes has been conducted with three design variables defining the inlet angle, the outlet angle of medial camber line of blade and the setting angle of blade. The efficiency and the static pressure rise as aerodynamic performance parameters have been selected as the objective function for optimizations. The objective function values have been assessed through three-dimensional flow analysis at design points sampled by Random among Discrete Levels sampling in the design space. The optimization processes have been performed many times with the different ranges of design variables. Compared with the original model, the optimization design result shows that the efficiency has improved 1.5% and the static pressure rises 87 Pa respectively. The off-design performance has been also improved in all of the optimum shapes, which meets design requirements.%本文基于人工神经网络代理模型对某大功率机车用轴流冷却风机叶轮进行优化设计。采用S-A湍流模型和求解三维雷诺平均N-S方程分析叶轮内部流动，以叶片中弧线进口角、出口角和叶片的安装角为设计变量，优化目标函数选择效率和静压升。设计点采用随机离散层取样方式，在几何参数的设计范围内生成样本并进行三维流动分析，以得到目标函数的模拟值；取不同自由参数可变范围，多次优化。优化设计结果与原始模型相比提高效率1.5%，静压升提高87Pa，其非设计点性能也均有所提高，满足设计需要。