Abstract
Double suction centrifugal pumps, which feature large flow and head, are applied in water utility and transportation sectors. The efficiency, sound, and vibration levels are key performance indexes for double suction centrifugal pumps. This study aims to improve the performance of double suction pumps using a multi-objective optimization method. The Latin hypercube sampling (LHS) method is used to randomly generate sample data considering five key geometric parameters of the impeller, and the agent model training samples are generated using numerical computation. Then, the multi-objective optimization design of the impeller, focusing on the head, efficiency, and pressure pulsation energy as the objectives, was carried out by combining the Gaussian process regression (GPR) and non-dominated sorting genetic algorithm II (NSGA-II) algorithms. Results show that the head is increased by 3.91 m, the efficiency is increased by 0.2 %, and the pressure pulsation energy is reduced by 24 % compared with the original model. Meanwhile, the detailed information of energy loss and pressure pulsation in the pump was analyzed to understand the influence of impeller geometry parameters. This study provides a certain reference for the optimized design of double-suction pumps.
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Abbreviations
- LHS :
-
Latin hypercube sampling
- GPR :
-
Gaussian process regression
- NSGA-II :
-
Non-dominated sorting genetic algorithm II
- MOEA/D :
-
Multiobjective evolutionary algorithm based on decomposition
- ANN :
-
Artificial neural network
- H :
-
Head
- η :
-
Efficiency
- RMS a :
-
Root mean square of pressure pulsation coefficient
- Q d :
-
Flow rate at rated operating condition
- H d :
-
Head at rated operating condition
- η d :
-
Rotation speed at rated operating condition
- LEP :
-
Local entropy production
- TEP :
-
Total entropy production
- f BPF :
-
Blade passing frequency
- f n :
-
Shaft frequency
References
W. Wang, Y. P. Li, M. K. Osman, S. Q. Yuan, B. Y. Zhang and J. Liu, Multi-condition optimization of cavitation performance on a double-suction centrifugal pump based on ANN and NSGA-II, Processes, 8(9) (2020) 1124.
Q. R. Si, S. Q. Yuan, J. P. Yuan, C. Wang and W. G. Lu, Multiobjective optimization of low-specific-speed multistage pumps by using matrix analysis and CFD method, Journal of Applied Mathematics, 10(4) (2013) 136195.
Y. Wang and X. Huo, Multiobjective optimization design and performance prediction of centrifugal pump based on orthogonal test, Advances in Materials Science and Engineering, 2018 (2018) 1–10.
L. Zhou, W. Shi and S. Wu, Performance optimization in a centrifugal pump impeller by orthogonal experiment and numerical simulation, Advances in Mechanical Engineering, 5 (2013) 385809.
Y. Zhang, S. B. Hu, J. I. Wu, Y. Q. Zhang and L. P. Chen, Multi-objective optimization of double suction centrifugal pump using Kriging metamodels, Advances in Engineering Software, 74 (2014) 16–26.
R. Tao, R. Xiao, D. Zhu and F. Wang, Multi-objective optimization of double suction centrifugal pump, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 232(6) (2018) 1108–1117.
D. Shahram and B. Mohamad, Investigation of an efficient shape optimization procedure for centrifugal pump impeller using eagle strategy algorithm and ANN (case study: slurry flow), Structural and Multidisciplinary Optimization, 58 (2018) 459–473.
J. S. Zhou, J. S. Zhang and P. Z. Mao, Performance optimization based on genetic algorithm of double suction centrifugal pump, Advanced Materials Research, 468–471 (2012) 2565–2568.
J. Pei, W. Wang, M. K. Osman and X. C. Gan, Multiparameter optimization for the nonlinear performance improvement of centrifugal pumps using a multilayer neural network, Journal of Mechanical Science and Technology, 33 (2019) 2681–2691.
L. Zhou, L. Bai, W. Li, W. D. Shi and C. Wang, PIV validation of different turbulence models used for numerical simulation of a centrifugal pump diffuser, Engineering Computations, 35(1) (2018) 2–17.
X. Deng, A mixed zero-equation and one-equation turbulence model in fluid-film thrust bearings, Journal of Tribology, 146(3) (2024) 034101.
H. L. Liu, M. M. Liu, Y. Bai and L. Dong, Effects of mesh style and grid convergence on numerical simulation accuracy of centrifugal pump, Journal of Central South University, 22(1) (2015) 368–376.
A. A. Alubokin, B. Gao, Z. Ning, L. L. Yan, Z. X. Jiang and E. K. Quaye, Numerical simulation of complex flow structures and pressure fluctuation at rotating stall conditions within a centrifugal pump, Energy Science & Engineering, 10(7) (2022) 2146–2169.
G. Yang, X. T. Zhao, D. S. Zhang, L. L. Geng, X. Q. Yang and X. F. Gao, Hydraulic components’ matching optimization design and entropy production analysis in a large vertical centrifugal pump, Journal of Mechanical Science and Technology, 35(11) (2021) 5033–5048.
M. D. Mckay, R. J. Beckman and W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 42(1) (2000) 55–61.
A. Zeng, H. Ho and Y. Yu, Prediction of building electricity usage using Gaussian process regression, Journal of Building Engineering, 28 (2020) 101054.
J. N. Fuhg, M. Marino and N. Bouklas, Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks, Computer Methods in Applied Mechanics and Engineering, 388 (2022) 114217.
E. Schulz, M. Speekenbrink and A. Krause, A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions, Journal of Mathematical Psychology, 85 (2018) 1–16.
S. Petchrompo, D. W. Coit, A. Brintrup, A. Wannakrairot and A. K. Parlikad, A review of Pareto pruning methods for multi-objective optimization, Computers & Industrial Engineering, 167 (2022) 108022.
Y. F. Cui, Z. Q. Geng, Q. X. Zhu and Y. M. Han, Review: multi-objective optimization methods and application in energy saving, Energy, 125 (2017) 681–704.
T. X. Wu, D. H. Wu, Y. Ren, Y. Song, Y. Q. Gu and J. G. Mou, Multi-objective optimization on diffuser of multistage centrifugal pump base on ANN- GA, Structural and Multidisciplinary Optimization, 65 (2022) 182.
B. Ghadimi, A. Nejat, S. A. Nourbakhsh and N. Naderi, Multi-objective genetic algorithm assisted by an artificial neural network metamodel for shape optimization of a centrifugal blood pump, Artificial Organs, 43(5) (2019) 76–93.
S. Verma, M. Pant and V. Snasel, A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems, IEEE Access, 9 (2021) 57757–57791.
S. Chakraborty, TOPSIS and modified TOPSIS: A comparative analysis, Decision Analytics Journal, 2 (2022) 100021.
M. H. Ahmadi, H. Hosseinzade, H. Sayyaadi, A. H. Mohammadi and F. Kimiaghalam, Application of the multi-objective optimization method for designing a powered stirling heat engine: Design with maximized power, thermal efficiency and minimized pressure loss, Renewable Energy, 60 (2013) 313–322.
F. Zhang, D. Appiah, F. Hong, J. F. Zhang, S. Q. Yuan, K. A. Adu-Poku and X. Y. Wei, Energy loss evaluation in a side channel pump under different wrapping angles using entropy production method, International Communications in Heat and Mass Transfer, 113 (2020) 104526.
D. H. Wu, Z. B. Zhu, Y. Ren, Y. Q. Gu and P. J. Zhou, Influence of blade profile on energy loss of sewage selfpriming pump, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41(10) (2019) 470.
H. C. Hou, Y. X. Zhang, X. Zhou, Z. T. Zuo and H. S. Chen, Optimal hydraulic design of an ultra-low specific speed centrifugal pump based on the local entropy production theory, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 233(6) (2019) 715–726.
H. Y. Guan, W Jiang, J. G. Yang, Y. C. Wang, X. H. Zhao, and J. X. Wang, Energy loss analysis of the double-suction centrifugal pump under different flow rates based on entropy production theory, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 234(20) (2020) 4009–4023.
T. X. Wu, D. H. Wu, S. Y. Gao, Y. Song, Y. Ren and J. G. Mou, Multi-objective optimization and loss analysis of multistage centrifugal pumps, Energy, 284 (2023) 128638.
Acknowledgments
This work was supported by the Natural Science Foundation of Zhejiang Province (No. LGG21E090002, LY21E060004), and Zhejiang Province Key Research and Development Program (No. 2021C01052).
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Yu Song is a postgraduate of the College of Metrology and Measurement Engineering, China Jiliang University and Zhejiang Engineering Research Center of Fluid Equipment and Measurement and Control Technology. His research interests include intelligent multi-objective optimization of centrifugal pumps.
Denghao Wu, Ph.D, is a Professor. He is the Head Deputy Director of Zhejiang Research Center of Intelligent Fluid Equipment and Digital Measurement and Control Technology at the College of Metrology and Measurement Engineering, China Jiliang University. He has been engaged in teaching and conducted research on control and detection of fluid machinery, optimal design of fluid machinery, and fault diagnosis and analysis of fluid machinery.
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Song, Y., Wu, D., Gu, Y. et al. Multi-objective optimal design of double-suction centrifugal pump impeller using agent-based models. J Mech Sci Technol 38, 4175–4186 (2024). https://doi.org/10.1007/s12206-024-0715-7
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DOI: https://doi.org/10.1007/s12206-024-0715-7