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Multi-component optimization of a vertical inline pump based on multi-objective pso and artificial neural network
Journal of Mechanical Science and Technology ( IF 1.6 ) Pub Date : 2020-07-24 , DOI: 10.1007/s12206-020-2101-4
Xingcheng Gan , Ji Pei , Wenjie Wang , Shouqi Yuan , Yajing Tang

The vertical inline pump is a single-stage single-suction centrifugal pump with a curved inlet pipe before the impeller, which is widely used in where the constraint is installation space. In this paper, with the objective functions of efficiencies at 0.5Qd, 1.0Qd, and 1.5Qd, a multi-objective optimization on inlet pipe and impeller was proposed to broaden the efficient operating period of a vertical inline pump. Two 5th order Bézier curves were adopted to fit the shape of the mid curve of the inlet pipe and the trend of the blade angle of the impeller. Fourteen design variables were selected after the data-mining process. 300 sample cases were generated using Latin hypercube sampling (LHS), and they were solved by 3D RANS code to obtain the objective functions. The feed-forward artificial neural network with a hidden layer and an output layer was adopted to fit the two objective functions and the 14 design variables. The Pareto frontiers were generated for the three objectives using multi-objective particle swarm optimization (MOPSO), and three different configurations on the Pareto front are selected for detailed study by computational fluid dynamics (CFD). The results showed that the profile of the inlet pipe and the blade have a dramatic impact on the performance of the vertical inline pump. The Pareto frontiers reported that the performance under the overload condition usually keeps stable when the nominal efficiency is lower than 82 %, or the part-load efficiency is lower than 62 %, and it will decrease rapidly after that. After optimization, the improvement of efficiencies at the part-load condition and nominal condition of the picked case were 9.65 % and 7.95 %, respectively.



中文翻译:

基于多目标PSO和人工神经网络的立式管道泵的多部件优化

立式管道泵是单级单吸离心泵,叶轮前有弯曲的进气管,广泛用于安装空间受限的场合。本文以效率目标函数分别为0.5 Q d,1.0 Q d和1.5 Q d提出了进气管和叶轮的多目标优化方法,以扩大立式管道泵的有效运行时间。采用两个5阶Bézier曲线来拟合进气管中间曲线的形状和叶轮叶片角度的趋势。在数据挖掘过程之后,选择了14个设计变量。使用拉丁文超立方体抽样(LHS)生成了300个样本案例,并通过3D RANS代码对其进行求解以获得目标函数。采用具有隐藏层和输出层的前馈人工神经网络来拟合两个目标函数和14个设计变量。使用多目标粒子群优化(MOPSO)为这三个目标生成了帕累托边界,在帕累托前沿选择了三种不同的配置,以通过计算流体动力学(CFD)进行详细研究。结果表明,进气管和叶片的轮廓对立式管道泵的性能产生了巨大影响。帕累托边疆报告说,当额定效率低于82%或部分负载效率低于62%时,过载条件下的性能通常保持稳定,此后它将迅速下降。经过优化后,拣选箱的部分负载条件和标称条件下的效率分别提高了9.65%和7.95%。帕累托前沿报道,当额定效率低于82%或部分负载效率低于62%时,过载条件下的性能通常保持稳定,此后它将迅速下降。经过优化后,拣选箱的部分负载条件和标称条件下的效率分别提高了9.65%和7.95%。帕累托边疆报告说,当额定效率低于82%或部分负载效率低于62%时,过载条件下的性能通常保持稳定,此后它将迅速下降。经过优化后,拣选箱的部分负载条件和标称条件下的效率分别提高了9.65%和7.95%。

更新日期:2020-07-24
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