Surrogate-Based Optimization of a Biplane Wells Turbine
01 Jan 2019-pp 639-648
TL;DR: In this article, Radial Basis Neural Network (RBNN) method is used to create the surrogate and the objective function is taken as efficiency of the turbine rotor by considering the gap between the planes and offset angle between blades in two planes.
Abstract: Oscillating Water Column (OWC) is one of the most popular wave energy converters being used for the last two decades. The pneumatic energy from water waves inside the air chamber of OWC is converted into mechanical energy with the help of Wells turbine. Biplane Wells turbine has inherent advantage over the monoplane turbine in terms of starting characteristics and operating range. The main parameters affecting the performance of biplane Wells turbine are the gap between the planes and the offset angle between blades in two planes. Surrogate-based optimization represents the optimization methodologies that use surrogate modelling techniques to find out maxima or minima. Surrogate modelling techniques are very useful for design analysis that uses computationally expensive codes such as Computational Fluid Dynamics (CFD). In the present work, flow over a biplane Wells turbine is simulated using CFD and optimized using surrogate approach. Radial Basis Neural Network (RBNN) method is used to create the surrogate. Blade thickness and the offset angle defining the circumferential position of blades in two planes are considered as the two variables and the objective function is taken as efficiency of the turbine rotor. The comparison of performance between the reference blade and the optimized blade is presented in this article.
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