Multi-objective hydraulic optimization and analysis in a minipump

Minipump is widely used in microfluidics system, active cooling system, etc. But building a high efficiency minipump is still a challenging problem. In this paper, a systematic method was developed to design, characterize and optimize a particular mechanical minipump. The optimization work was conducted to cope with the conflict between pressure head and hydraulic efficiency by an improved back-propagation neural network (BPNN) with the non-dominated sorting genetic algorithm-II (NSGA-II). The improved BPNN was utilized to predicate hydraulic performance and, moreover, was modified to improve the prediction accuracy. The NSGA-II was processed for minipump multi-objective optimization which is dominated by four impeller dimensions. During hydraulic optimization, the processing feasibility was also taken into consideration. Experiments were conducted to validate the above optimization methods. It was proved that the optimized minipump was improved by about 24 % in pressure head and 4.75 % in hydraulic efficiency compared to the original designed prototype. Meanwhile, the sensitivity test was used to analyze the influence of the four impeller dimensions. It was found that the blade outlet angle β2 and the impeller inlet diameter D0 significantly influence the pressure head H and the hydraulic efficiency η, respectively. Detailed internal flow fields showed that the optimum model can relieve the impeller wake and improve both the pressure distribution and flow orientation.摘要微泵在微流体系统,主动散热系统等领域应用广泛,然而制造高效能的微泵仍然存在诸多挑战。本文提出了一款独特的小型机械泵,并建立了系统的设计、表征、优化方法。结合前馈神经网络(BPNN)与非支配排序遗传算法-II(NSGA-II)对小型机械泵的扬程与水力效率进行了综合优化。其中,BPNN被用来预测估算泵的水力性能,并通过模拟退火算法(SA)提高了预测精度。NSGA-II的采用则是针对叶轮的四个尺寸参数展开多目标优化。依据优化结果,加工了两台测试样泵并进行了水力测试验证。利用灵敏度分析方法表征了叶轮的各结构参数对水力性能的影响。通过计算流体力学(CFD)分析了泵的流场特性,结果证明,经过优化的样泵其扬程和水力效率分别提升了24 %,4.75 %。叶轮的出口角和进口直径分别对泵的扬程和效率影响显著。优化后的样泵其流场具备更为理想的压力分布和液流导向。