To overcome the problems of large calculation cost and high dependence on designers’ experience, an optimization design method based on multi-output Gaussian process regression (MOGPR) was proposed. The hydraulic design method of centrifugal pump based on the MOGPR model was constructed under Bayesian framework. Based on the available excellent hydraulic model, the complex relationship between the performance parameters such as head, flow rate and the geometric parameters of centrifugal pump impeller was trained. The hydraulic design of the impeller for M125-100 centrifugal pump was performed by the proposed MOGPR surrogate model design method. The initial MOGPR design was further optimized by using the proposed MOGPR and NSGA-II hybrid model. The initial sample set for NSGA-II was designed by Latin hypercube design based on the MOGPR initial design. The relationship between the impeller geometry and the CFD numerical results of the sample set was trained to construct the surrogate model for pump hydraulic performance prediction. The MOGPR surrogate model was used to evaluate the objective function value of the offspring samples in NSGA-II multi-objective optimization. The comparison of the pump hydraulic performance between the optimized designs and the initial design shows that the efficiency and the head of the tradeoff optimal design are increased by 2.5% and 2.6%, respectively. The efficiency of the optimal head constraint design is increased by 3.2%. The comparison of the inner flow field shows that turbulent kinetic energy decreases significantly and flow separation is effectively suppressed for the optimal head constraint design.

中文翻译：

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基于多输出高斯过程回归的离心泵叶轮优化设计

针对计算成本大、对设计人员经验依赖性高的问题，提出了一种基于多输出高斯过程回归（MOGPR）的优化设计方法。在贝叶斯框架下构建了基于MOGPR模型的离心泵水力设计方法。基于已有的优秀水力模型，训练了离心泵叶轮的扬程、流量等性能参数与几何参数之间的复杂关系。M125-100离心泵叶轮的水力设计采用提出的MOGPR替代模型设计方法。通过使用提出的 MOGPR 和 NSGA-II 混合模型，进一步优化了初始 MOGPR 设计。NSGA-II 的初始样本集是基于 MOGPR 初始设计的拉丁超立方设计。训练叶轮几何形状与样本集的 CFD 数值结果之间的关系，构建泵水力性能预测的替代模型。MOGPR代理模型用于评估NSGA-II多目标优化中后代样本的目标函数值。优化设计与初始设计的泵水力性能比较表明，折衷优化设计的效率和扬程分别提高了2.5%和2.6%。优化水头约束设计的效率提高了3.2%。