Energy efficient design of a jet pump by ensemble of surrogates and evolutionary approach

Afzal Husain*, Arihant Sonawat, Sarath Mohan, Abdus Samad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Energy systems working coherently in different conditions may not have a specific design which can provide optimal performance. A system working for a longer period at lower efficiency implies higher energy consumption. In this effort, a methodology demonstrated by a jet pump design and optimization via numerical modeling for fluid dynamics and implementation of an evolutionary algorithm for the optimization shows a reduction in computational costs. The jet pump inherently has a low efficiency because of improper mixing of primary and secondary fluids, and multiple momentum and energy transfer phenomena associated with it. The high fidelity solutions were obtained through a validated numerical model to construct an approximate function through surrogate analysis. Pareto-optimal solutions for two objective functions, i.e., secondary fluid pressure head and primary fluid pressure-drop, were generated through a multi-objective genetic algorithm. For the jet pump geometry, a design space of several design variables was discretized using the Latin hypercube sampling method for the optimization. The performance analysis of the surrogate models shows that the combined surrogates perform better than a single surrogate and the optimized jet pump shows a higher performance. The approach can be implemented in other energy systems to find a better design.

Original languageEnglish
Pages (from-to)265-276
Number of pages12
JournalInternational Journal of Fluid Machinery and Systems
Issue number3
Publication statusPublished - Jul 1 2016


  • Efficiency enhancement
  • Ensemble of surrogates
  • Evolutionary computation
  • Jet pump
  • Multi-objective optimization
  • Pareto-optimal designs

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


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