This article presents a multi-objective optimization to improve the hydrodynamic performance of a counter-rotating type\npump-turbine operated in pump and turbine modes. The hub and tip blade angles of impellers/runners with four blades,\nwhich were extracted through a sensitivity test, were optimized using a hybrid multi-objective genetic algorithm with a\nsurrogate model based on Latin hypercube sampling. Three-dimensional steady incompressible Reynolds-averaged\nNavierââ?¬â??Stokes equations with the shear stress transport turbulence model were discretized via finite volume approximations\nand solved on a hexahedral grid to analyze the flow in the pump-turbine domain. For the major hydrodynamic performance\nparameters, the pump and turbine efficiencies were selected as the objective functions. Global Pareto-optimal\nsolutions were searched using the response surface approximation surrogate model with the non-dominated sorting\ngenetic algorithm, which is a multi-objective genetic algorithm. The trade-off between the two objective functions was\ndetermined and described with regard to the Pareto-optimal solutions. As a result, the pump and turbine efficiencies for\nthe arbitrarily selected optimum designs in the Pareto-optimal solutions were increased as compared with the reference\ndesign.
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