Purpose
Wind turbines are one of the best candidates to solve the problem of increasing energy demand in the world. The aim of this paper is to apply a multi-objective structural optimization study to a Phase II wind turbine blade produced by the National Renewable Energy Laboratory to obtain a more efficient small-scale wind turbine.
Design/methodology/approach
To solve this structural optimization problem, a new Non-Dominated Sorting Genetic Algorithm (NSGA-II) was performed. In the optimization study, the objective function was on minimization of mass and cost of the blade, and design parameters were composite material type and spar cap layer number. Design constraints were deformation, strain, stress, natural frequency and failure criteria. ANSYS Composite PrepPost (ACP) module was used to model the composite materials of the blade. Moreover, fluid–structure interaction (FSI) model in ANSYS was used to carry out flow and structural analysis on the blade.
Findings
As a result, a new original blade was designed using the multi-objective structural optimization study which has been adapted for aerodynamic optimization, the NSGA-II algorithm and FSI. The mass of three selected optimized blades using carbon composite decreased as much as 6.6%, 11.9% and 14.3%, respectively, while their costs increased by 23.1%, 29.9% and 38.3%. This multi-objective structural optimization-based study indicates that the composite configuration of the blade could be altered to reach the desired weight and cost for production.
Originality/value
ACP module is a novel and advanced composite modeling technique. This study is a novel study to present the NSGA-II algorithm, which has been adapted for aerodynamic optimization, together with the FSI. Unlike other studies, complex composite layup, fiber directions and layer orientations were defined by using the ACP module, and the composite blade analyzed both aerodynamic pressure and structural design using ACP and FSI modules together.
Aerodynamic Optimization of a Wind Turbine Blade Designed for Egypt's Saharan Environment Using a Genetic Algorithm
