To broaden the efficient operating zone and increase the energy efficiency of a multi-stage double-suction centrifugal pump, a multi-component and multi-condition optimization design method involving high-precision performance predictions, a flow loss visualization technique based on entropy production theory, and machine learning is proposed. First, the accuracy of the baseline pump numerical methodology is verified via a grid convergence analysis and experiments. Thereafter, nine design parameters of the impeller and double volute are selected as design variables. Subsequently, 150 designs are created according to the Latin hypercube sampling method (LHS) and numerically simulated using an automatic simulation program. A backpropagation neural network (BPNN) and a multi-objective genetic algorithm (MOGA) are adopted to maximize the efficiency at 0.6Q_d, 1.0Q_d, and 1.2Q_d. Finally, the optimal results are verified via numerical calculations and analyzed. The results indicate that the efficiency of the optimized pump is increased by 2.05%, 3.56%, and 5.36% at 0.6Q_d, 1.0Q_d, and 1.2Q_d, respectively. The comparative analysis of the energy characteristics reveals that the improved performance of the optimized pump can be attributed to the improved matching between the rotor and stator. This research further demonstrates the accuracy and reliability of the optimization method using an artificial neural network (ANN).

为拓宽多级双吸离心泵的有效工作区，提高能效，一种涉及高精度性能预测的多组分、多工况优化设计方法，一种基于熵产理论的流量损失可视化技术，并提出了机器学习。首先，通过网格收敛分析和实验验证了基线泵数值方法的准确性。此后，选择叶轮和双蜗壳的九个设计参数作为设计变量。随后，根据拉丁超立方抽样方法 (LHS) 创建了 150 个设计，并使用自动模拟程序进行了数值模拟。Q _d、1.0 Q _d和 1.2 Q _{d 。}最后通过数值计算验证了最优结果并进行了分析。结果表明，优化后的泵的效率在 0.6 Q _d、1.0 Q _d和 1.2 Q _d时分别提高了 2.05%、3.56% 和 5.36%。能量特性的对比分析表明，优化后的泵性能的提高可归因于转子和定子之间的匹配度提高。这项研究进一步证明了使用人工神经网络 (ANN) 的优化方法的准确性和可靠性。