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

机译:NSGA-Ⅱ算法和FSI的风力涡轮机叶片多目标结构优化

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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.
机译:目的,风力涡轮机是解决世界上越来越多的能源需求问题的最佳候选人之一。本文的目的是将多目标结构优化研究应用于国家可再生能源实验室生产的II期风力涡轮机叶片,以获得更有效的小型风力涡轮机。解决该结构优化问题的设计/方法/方法,进行了一种新的非主导分类遗传算法(NSGA-II)。在优化研究中,目标函数是最小化刀片的质量和成本,并且设计参数是复合材料类型和翼梁帽层数。设计约束是变形,应变,应力,自然频率和故障标准。 ANSYS复合预备(ACP)模块用于模拟刀片的复合材料。此外,ANSYS中的流体结构相互作用(FSI)模型用于对叶片进行流动和结构分析。结果,使用多目标结构优化研究设计了一种新的原始刀片,该研究已经适用于空气动力学优化,NSGA-II算法和FSI。使用碳复合材料的三种选定的优化叶片的质量分别降低了6.6%,11.9%和14.3%,而其成本增加23.1%,29.9%和38.3%。基于多目标结构优化的研究表明,可以改变叶片的复合结构以达到所需的重量和生产成本。 Originity / Value ACP模块是一种新型和高级复合建模技术。本研究是一种新的研究,以呈现NSGA-II算法,该研究已经适用于空气动力学优化以及FSI。与其他研究不同,通过使用ACP模块来定义复合复合叠层,光纤方向和层取向,并使用ACP和FSI模块在一起分析了空气动力学压力和结构设计。

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