尺寸限制下的多翼离心风机蜗壳型线设计

Squirrel cage fan volute profile design under size limitation

为解决切割蜗壳型线造成多翼离心风机气动性能下降的问题,采用七个控制点构成的二次非均匀B样条(NUQBS)曲线表征蜗壳型线的扩张规律,并布置两个相近的控制点满足风机尺寸要求.为减少数值模拟的计算量,利用最优拉丁超立方试验设计方法,对控制点的3个设计变量进行空间采样.用径向基函数(RBF)神经网络模型建立设计变量与优化目标之间的响应关系,使用第二代非劣排序遗传算法(NSGA-Ⅱ)对其进行多目标优化.研究结果表明:RBF神经网络模型能准确预测设计变量与风机流量和效率之间的关系;优化后的蜗壳改善了风机内部流动状态,扩大了叶轮的做功范围;与原型机相比,风机的最大流量增大1.12 m^3/min,效率提高4.4%,气动噪声降低1.72 dB.

A non-uniform quadratic B-spline(NUQBS)curve composed of seven control points was used to characterize the expansion law of volute profile to solve the problem of aerodynamic performance degradation of squirrel cage fan caused by cutting volute profile.Two similar control points were arranged skillfully to meet the requirements of fan size.To reduce the computational complexity in the numerical simulation process,the optimal Latin hypercube experimental design method was used to sample the three design variables of the control points.A radial basis function(RBF)neural network model was used to establish the response relationship between design variables and optimization objectives.The second non-dominated sorting genetic algorithm(NSGA-Ⅱ)was used to optimize the design variables.The results show that the RBF neural network model can accurately predict the relationship between the design variables and the flow rate and efficiency of the fan.The optimized volute improves the internal flow state of the fan and enlarges the working range of the impeller.Compared with the prototype,the maximum volume flow rate of the fan increases by 1.12 m^3/min,the efficiency increases by 4.4%,and the aerodynamic noise decreases by 1.72 dB.