Abstract
A multiobjective optimization for improving the turbine output and efficiency of a counterrotating type pump-turbine unit operated at turbine mode was carried out in this work. The blade geometry of both the runners was optimized using a hybrid multiobjective evolutionary algorithm coupled with a surrogate model. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model were discretized by finite volume approximations and solved on hexahedral grids to analyze the flow in the pump-turbine unit. As major hydrodynamic performance parameters, the turbine output and efficiency were selected as objective functions with two design variables related to the hub profiles of both the runner blades. These objectives were numerically assessed at twelve design points selected by Latin hypercube sampling in the design space. Response surface approximation models for the objectives were constructed based on the objective function values at the design points. A fast nondominated sorting genetic algorithm for the local search coupled with the response surface approximation models was applied to determine the global Pareto-optimal solutions. The trade-off between the two objectives was determined and described with respect to the Pareto-optimal solutions. The results of this work showed that the turbine outputs and efficiencies of optimized pump-turbine units were simultaneously improved in comparison to the reference unit.