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Multi-objective optimization of multiphase pump by using computational fluid dynamics and nondominated sorting genetic algorithm II

Author

Listed:
  • Wang, Jiaqiong
  • Hu, Chen
  • Du, Danyang
  • Agarwal, Ramesh
  • Zhou, Ling

Abstract

Multiphase pumps are a cost-effective technology for the transportation of gas-liquid mixture, which is widely used in the geothermal systems. To improve the energy performance of multiphase pump, we proposed an integrated approach within a multi-objective optimization framework by using Computational Fluid Dynamics (CFD) and Nondominated Sorting Genetic Algorithm II (NSGA-II). In this paper, 10 structural parameters are selected for sensitivity analysis of the multiphase pump, four main structural parameters affecting the performance are screened out. The efficiency and pressurisation are taken as the optimization objectives, and the efficiency extreme point model, the pressurisation extreme point model and the multi-objective optimization model are obtained. The results show that the hydraulic efficiency and pressurisation capacity of the optimized models are significantly improved compared with the original model under pure water and different inlet gas volume fractions (IGVFs) at the design flow rate. At IGVF = 50 %, the efficiency of each model after optimization is improved by 10.1 %, 9.68 % and 8.92 % compared with the original model; the pressurisation capacity is improved by 9.01 kPa, 6.68 kPa and 5.17 kPa, and the optimized internal flow field is also significantly improved. It is shown that this optimization method has great advantages for improving the performance of the multiphase pump at low cost and low energy consumption.

Suggested Citation

  • Wang, Jiaqiong & Hu, Chen & Du, Danyang & Agarwal, Ramesh & Zhou, Ling, 2025. "Multi-objective optimization of multiphase pump by using computational fluid dynamics and nondominated sorting genetic algorithm II," Renewable Energy, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:renene:v:254:y:2025:i:c:s0960148125013795
    DOI: 10.1016/j.renene.2025.123717
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    References listed on IDEAS

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