DOAJ Open Access 2025

Inlet Passage Hydraulic Performance Optimization of Coastal Drainage Pump System Based on Machine Learning Algorithms

Tao Jiang Weigang Lu Linguang Lu Lei Xu Wang Xi +2 lainnya

Abstrak

The axial-flow pump system has been widely applied to coastal drainage pump stations, but the hydraulic performance optimization based on the contraction angles of the inlet passage has not been studied. This paper combined the computational fluid dynamics (CFD) method, machine learning (ML) algorithms and genetic algorithm (GA) to find the optimal contraction angles of the inlet passage. The 125 sets of comprehensive objective function were obtained by the CFD method. Three contraction angles and comprehensive objective function values were regressed by three ML algorithms. After hyperparameter optimization, the Gaussian process regression (GPR) model had the highest <i>R</i><sup>2</sup> = 0.958 in the test set and had the strongest generalization ability among the three models. The impact degree of the three contraction angles on the objective function of the GPR model was investigated by the Sobol sensitivity analysis method; the results indicated that the order of impact degree from high to low was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi></mrow><mrow><mn>3</mn></mrow></msub><mo>></mo><msub><mrow><mi>θ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>></mo><msub><mrow><mi>θ</mi></mrow><mrow><mn>1</mn></mrow></msub></mrow></semantics></math></inline-formula>. The optimal objective function values of the GPR model and corresponding contraction angles were searched through GA; the maximum objective function value was 0.963 and corresponding contraction angles were <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><mn>13.34</mn><mo>°</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><mn>28.36</mn><mo>°</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi></mrow><mrow><mn>3</mn></mrow></msub><mo>=</mo><mn>3.64</mn><mo>°</mo></mrow></semantics></math></inline-formula>, respectively. The results of this study can provide reference for the optimization of inlet passages in coastal drainage pump systems.

Penulis (7)

T

Tao Jiang

W

Weigang Lu

L

Linguang Lu

L

Lei Xu

W

Wang Xi

J

Jianfeng Liu

Y

Ye Zhu

Format Sitasi

Jiang, T., Lu, W., Lu, L., Xu, L., Xi, W., Liu, J. et al. (2025). Inlet Passage Hydraulic Performance Optimization of Coastal Drainage Pump System Based on Machine Learning Algorithms. https://doi.org/10.3390/jmse13020274

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Informasi Jurnal
Tahun Terbit
2025
Sumber Database
DOAJ
DOI
10.3390/jmse13020274
Akses
Open Access ✓