考虑几何不确定性影响的离心泵叶轮稳健性优化设计

Robust Optimization Design of Centrifugal Pump Impeller Considering Geometric Uncertainty

摘要:

针对设计、加工制造及运行过程几何不确定性引起的离心泵叶轮性能随机波动问题，本文提出考虑几何不确定影响的离心泵稳健性优化设计。基于高斯过程-主成分分析方法构建叶片几何不确定性参数化模型，采用非侵入式多项式混沌(NIPC)方法量化叶片外形几何偏差对扬程与效率的影响;结合径向基函数(RBF)神经网络代理模型及NSGA-Ⅱ多目标优化算法，以进口安放角、出口安放角和叶片包角为设计变量开展离心泵稳健性优化设计。结果表明，原型叶轮在随机几何偏差±3%作用下，设计工况效率和扬程均出现下降，小流量工况下下降更为明显;稳健性优化后，扬程和效率较原型叶片泵得到较大提升，且扬程和效率方差大幅降低，提升水力性能的同时，显著提高了叶轮对几何随机偏差的稳健性，对叶片泵的稳定可靠运行具有一定指导意义。

Abstract:

Aiming to address the problem of random performance fluctuations of centrifugal pump impellers caused by geometric uncertainties during design, manufacturing, and operation, a robust optimization design method of centrifugal pumps considering geometric uncertainties was proposed. A parameterized model of blade geometric uncertainty was constructed based on the Gaussian process-principal component analysis (GP-PCA) method, and the influence of blade geometric deviations on head and efficiency was quantified by using the non-intrusive polynomial chaos (NIPC) method. Combined with the radial basis function (RBF) neural network surrogate model and the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ), a robust optimization design of the centrifugal pump was carried out by taking the inlet blade angle, outlet blade angle, and blade wrap angle as design variables. The results showed that under random geometric deviations of ±3%, both the efficiency and head of the prototype impeller were decreased under design conditions, with the decrease being more pronounced under low flow rate conditions. After robustness optimization, the head and efficiency were significantly improved compared with the prototype pump, and the head variance and efficiency variance were greatly reduced. This improved the impeller's robustness to random geometric deviations while enhancing its hydraulic performance, and had certain guiding significance for the stable and reliable operation of the pump.