Multi-objective optimization of parallel hydraulic flushing circuit based on NSGA-II

Yan Cao; Bing Zhang; Jingding Tian; Jiawei Wu; Hongyu Lu; Hao Chen

doi:10.15282/ijame.23.3.2026.14.1050

Authors

Yan Cao School of Mechanical Engineering, Jiangsu University, 212013 Zhenjiang, Jiangsu, China , Jiangsu University https://orcid.org/0009-0003-0522-7087
Bing Zhang School of Mechanical Engineering, Jiangsu University, 212013 Zhenjiang, Jiangsu, China , Jiangsu University https://orcid.org/0000-0001-8331-9962
Jingding Tian School of Mechanical Engineering, Jiangsu University, 212013 Zhenjiang, Jiangsu, China , Jiangsu University https://orcid.org/0009-0008-4155-5404
Jiawei Wu School of Mechanical Engineering, Jiangsu University, 212013 Zhenjiang, Jiangsu, China , Jiangsu University https://orcid.org/0009-0008-5677-9865
Hongyu Lu School of Mechanical Engineering, Jiangsu University, 212013 Zhenjiang, Jiangsu, China , Jiangsu University https://orcid.org/0009-0001-5476-6031
Hao Chen School of Mechanical Engineering, Jiangsu University, 212013 Zhenjiang, Jiangsu, China , Jiangsu University https://orcid.org/0009-0001-7647-0709

DOI:

https://doi.org/10.15282/ijame.23.3.2026.14.1050

Keywords:

Large-scale hydraulic motor, Parallel flushing circuit, Motor test bench, Hydraulic circuit optimization, Multi-objective optimization, AMESim

Abstract

In the parallel flushing circuit for large-scale hydraulic motor testing, increasing the flushing flow rate increases the total pressure loss and casing pressure differential, whereas reducing the pipeline pressure loss alone increases the casing pressure. To solve this strongly conflicting engineering problem, this study proposes an optimization method that combines the Maximin Latin Hypercube Sampling, Kriging surrogate model, and NSGA-II algorithm. Specifically, five core design variables (pipeline length, elbow quantity, and pump flow) were systematically tuned to minimize the casing pressure difference, total pressure loss, and flushing flow. The TOPSIS method was further applied to derive optimal solutions under different design preferences. The method was verified via AMESim simulation and leave-one-out cross-validation, achieving 0.8%–10.1% optimization for each target, and provides a valuable engineering reference for multi-objective optimization of similar hydraulic circuits. In conclusion, the proposed multi-objective optimization framework successfully resolved the inherent physical contradiction between casing heat dissipation and mechanical seal safety. This proves that the combination of the MLHS, Kriging surrogate models, and NSGA-II is a highly robust approach for large-scale hydraulic test bench design.

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Multi-objective optimization of parallel hydraulic flushing circuit based on NSGA-II

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