Sabangban, NumchoakPanagant, NateeBureerat, SujinWansasueb, KittinanKuma, SumitYıldız, Ali RizaPholdee, Nantiwat2024-11-082024-11-082023-04-120025-5300https://doi.org/10.1515/mt-2022-0308https://www.degruyter.com/document/doi/10.1515/mt-2022-0308/htmlhttps://hdl.handle.net/11452/47620This work presents a concurrent design and multi-objective optimisation framework of horizontal axis wind turbine blades, made of composite material, for low wind speed. The optimisation model aims to minimise the structural mass of the blade whilst simultaneously maximising the turbine power output, subjected to three constraints viz. blade tip deflection, and Tsai-Hill and von Mises criteria. The design variables are blade shape and details of the internal blade structure. The control points and polynomial interpolation technique were adopted to determine the blade shape while the airfoil types at blade sections remained fixed. The internal blade structure design variables include the thickness of ribs and spars and the carbon fibre thickness and orientations. The blade element momentum approach is utilised to calculate turbine power and structural loads, whereas a finite element method is employed for structural analysis. Twelve multi-objective metaheuristics algorithms are used to solve the proposed multi-objective optimisation problem while their performance is investigated. The results obtained show that the multi-objective cuckoo search algorithm is the most efficient method. This study is said to be the baseline for a future study on multi-objective optimisation which combines two design stages of the composite low-speed wind turbine blades.eninfo:eu-repo/semantics/closedAccessDesign optimizationShape optimizationSearchAlternative energy resourcesEvolutionary algorithmsLow-speed wind turbineMulti-objective optimisationWind turbine blades optimisationScience & technologyTechnologyMaterials science, characterization & testingMaterials scienceSimultaneous aerodynamic and structural optimisation of a low-speed horizontal-axis wind turbine blade using metaheuristic algorithmsArticle00097028410000169971465510.1515/mt-2022-03082195-8572