Structural optimization of multistage centrifugal pump via computational fluid dynamics and machine learning method

To implement energy savings in multistage centrifugal pumps, a return channel is utilized to replace the origin inter-stage flow channel structure, and then a single-objective optimization work containing high-precision numerical simulation, design variable dimensionality reduction, and machine learning is conducted to obtain the optimal geometric parameters. The variable dimensionality reduction process is based on the Spearman correlation analysis method. The influence of 15 design variables of the impeller and return channel is investigated, and seven of them with high-impact factors are selected as the final optimization variables. Thereafter, a genetic algorithm-backpropagation neural network (GA-BPNN) model is used to create a surrogate model with a high-fitting performance by employing a GA to optimize the initial thresholds and weights of a BPNN. Finally, a multi-island genetic algorithm (MIGA) is employed to maximize hydraulic efficiency under the nominal condition. The findings demonstrate that the optimized model's efficiency is increased by 4.29% at 1.0Q(d), and the deterioration of the pump performance under overload conditions is effectively eliminated (the maximum efficiency increase is 14.72% at 1.3Q(d)). Furthermore, the internal flow analysis indicates that the optimization scheme can improve the turbulence kinetic energy distribution and reduce unstable flow structures in the multistage centrifugal pump.

Automated summary

To achieve energy savings in multistage centrifugal pumps, a return channel is used instead of the original inter-stage flow channel structure. A single-objective optimization work is conducted using high-precision numerical simulation, design variable dimensionality reduction, and machine learning to determine the optimal geometric parameters. The study investigates the influence of 15 design variables and selects seven with high-impact factors as the final optimization variables. The findings show that the optimized model increases efficiency by 4.29% at 1.0Q(d) and eliminates performance degradation under overload conditions (maximum efficiency increase of 14.72% at 1.3Q(d)). Internal flow analysis confirms that the optimization scheme improves turbulence kinetic energy distribution and reduces unstable flow structures in the multistage centrifugal pump.