Sweep Blade Design for an Axial Wind Turbine using a Surrogate-‎assisted Differential Evolution Algorithm

This paper presents an optimal design of a sweep blade for the axial wind turbine using a hybrid surrogate-assisted optimizer. The design problem is defined to maximize the ratio of the torque coefficient to the thrust coefficient of a turbine blade at a low wind velocity of 10 m/s. Pitch angle and leading-edge blade curve are considered as the design variables. For the aerodynamic analysis of the wind turbine blade, computational fluid dynamics has been used as a high-fidelity simulation. While the surrogate models including, the Kriging model (KG), the radial basis function model (RBF), and the proposed hybrid of KG and RBF (HyKG-RBF) models are applied for function approximation or low-fidelity simulation. In this study, to obtain a set of sampling points and surrogate models development, an optimal Latin Hypercube sampling (OLHS) technique is utilized in the design of the experiment (DOE). A differential evolutionary (DE) algorithm is used to solve the proposed design problem. The performance of the proposed hybrid surrogate assisted optimization method is contrasted with two conventional surrogate assisted optimization techniques. Results demonstrate that the proposed hybrid surrogate model viz. HyKG-RBF is the most efficient surrogate-assisted optimization method for solving the sweep blade optimization problem.