Shape optimization of a bidirectional impulse turbine via surrogate models

Abstract

In this work, a bidirectional impulse turbine used in a wave energy device is simulated using a CFD technique and a shape optimization performed with multiple surrogates-assisted multi-objective evolutionary algorithm. The surrogates have been generated using the validated CFD technique. The objectives for the optimization are to maximize the shaft power and to minimize the pressure drop across the turbine. The hub and the tip thickness of the rotor blade profiles are modified and considered as the design variables. The Latin hypercube sampling technique generated design points are evaluated in the CFD solver, the objective responses are used to construct multiple surrogates, and a Pareto optimal front is generated through a multi-objective optimization approach. The turbine efficiency, which is the function of pressure drop and shaft power, is relatively increased by 10.4%. Abbreviations: BPS: best PRESS; GV: Guide vane; KRG: kriging; LE: leading edge; MOO: multi-objective optimization; OWC: oscillating water column; PRESS: predicted error sum of squares; PS: pressure side or pressure surface; RB: rotor blade; RBNN: radial basis neural network; Ref: reference; RMS: root mean square; RSA: response surface approximation; SS: suction side or suction surface; TE: trailing edge; WAS: weighted average surrogates.