Artificial intelligence approach for energy and entropy analyses of a double-suction centrifugal pump

Wengjie Wang, Hongyu Wang, Ji Pei, Jia Chen, Xingcheng Gan and Qin Sun

Energy, 2025, vol. 324, issue C

Abstract: As global energy and environmental concerns grow, reducing energy consumption and emissions has become crucial. Centrifugal pumps, widely used in various applications, are significant energy consumers, making energy loss reduction essential. This paper presents an optimization method for centrifugal pumps to minimize entropy generation losses while maintaining the same head. Using Computational Fluid Dynamics (CFD) and intelligent optimization algorithms, the study focuses on a double-suction centrifugal pump with a European specific speed of 43.8. The impeller blade profile is optimized to reduce entropy generation while maintaining head. Artificial Neural Network (ANN) and NSGA-II algorithm were combined to optimize the impeller blade Angle. The results show that the optimized design reduces the impeller's turbulent entropy generation by 19.3 %, and the volute's by 24.2 %. The optimized pump exhibits a more uniform pressure distribution, suppressing flow separation and vortices. The optimized design reduces entropy generation at the impeller outlet and shroud, while shrinking the low-pressure zone on the shroud. The total entropy generation loss decreases by 15.16 %, and the efficiency increases by 1.73 %, confirming the effectiveness of the optimization method in improving pump performance and energy efficiency.

Keywords: Double-suction centrifugal pump; Entropy generation loss; Multi-objective optimization; Artificial neural network; Optimization design (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225016883
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:324:y:2025:i:c:s0360544225016883

DOI: 10.1016/j.energy.2025.136046

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().