This study presents the hydrodynamic analysis and optimization for a single-channel submersible pump impeller. The numerical analysis involved the use of the shear stress transport turbulence model to solve three-dimensional Reynolds-averaged Navier-Stokes equations. The optimization process was based on a radial basis neural network model and was conducted by considering two design variables to control the area from the inlet to the outlet of the impeller. The hydrodynamic performance of the pump impeller was optimized by selecting the efficiency as the objective function. The objective function was numerically assessed via Latin hypercube sampling at design points selected in the design space, and the optimization yielded a maximum increase in efficiency of approximately 1% in terms of the design flow rate, as compared to the initial design. The performance curve for the efficiency was also improved in the high flow rate region. Further details of the internal flow fields between the reference and optimum designs are also analyzed and discussed in this work.

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