Multi-objective structural optimization of a wind turbine blade using NSGA-II algorithm and FSI

Abstract

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.

The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

“…6(b). From the Figure, the root mean square error cumulative sum of GA, BP, and GA-BP method are all greater, which proves that the ASSGA-BP is highly accurate [22][23]. This is because in the ASSGA algorithm, the worst genetic individual obtained after selection is used for mutation operations.…”

A new nested-niche technique for optimization design of composite laminated structure

“…The evolutionary algorithms were widely used in the optimization of composite laminated structure because of their advantages of simple theory and strong adaptability. Özkan et al [2] performed a new Non-Dominated Sorting Genetic Algorithm (NSGA-II) to solve the structural optimization problem of a composite Phase II wind turbine blade; In the research of Dong et al [3], the particle swarm optimization algorithm was used to optimize the structural of ship composite materials; In reference [4], Particle swarm optimization algorithm (PSO), genetic algorithm (GA), and hunger games search optimization algorithm(HGS) were used to determine the best stacking angle value on the disc plate; Tran et al [5] presented a new approach as an integration of deep neural networks (DNN) into differential evolution (DE) for frequency optimization of laminated functionally graded carbon nanotube (FG-CNT)-reinforced composite quadrilateral plates. But the evolutionary algorithms have the problem of converging to an undesired local solution (premature convergence) when the objective function is multimodal, this restricts the application of these algorithms.…”

Research on the application of improved NSGA-II in the structure design of wind turbine blade spar cap

“…In 2019, With Annual Energy Production (AEP) and blade mass as objective functions, and aerodynamic parameters and structural parameters as design variables, Meng and Xie (2019) established a multi-objective optimization model of wind turbine blades based on the para-metric finite element model of blades and carried out optimization design using genetic algorithm, in which structural parameters took the thickness of spar cap as the main optimization variables. Later in 2021, Zkan and Gen (2021) applied the study of multi-objective structural optimization to the production of Phase II wind turbine blades in the NREL to obtain more efficient small wind turbines. In the optimization study, the objective function is the minimization of blade mass and cost, the design parameters are the composite material type and the number of spar cap layers, and the flow and structure analysis of the blade is performed using the fluidstructure coupling model in ANSYS.…”