Simultaneous efficiency improvement of pump and turbine modes for a counter-rotating type pump-turbine

Abstract

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