Collaborative Optimization of Aerodynamics and Wind Turbine Blades

This paper explores the application of multidisciplinary design optimization to the blades in horizontal-axis wind turbines. The aerodynamics and structural performance of blades are considered in the optimization framework. In the aerodynamic discipline, class function/shape function transformation...

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Main Authors: Fushan He, Xingsheng Zheng, Weilin Luo, Jianfeng Zhong, Yunhua Huang, Aili Ye, Rongrong Qiu, Huafu Ma
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/15/2/834
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author Fushan He
Xingsheng Zheng
Weilin Luo
Jianfeng Zhong
Yunhua Huang
Aili Ye
Rongrong Qiu
Huafu Ma
author_facet Fushan He
Xingsheng Zheng
Weilin Luo
Jianfeng Zhong
Yunhua Huang
Aili Ye
Rongrong Qiu
Huafu Ma
author_sort Fushan He
collection DOAJ
description This paper explores the application of multidisciplinary design optimization to the blades in horizontal-axis wind turbines. The aerodynamics and structural performance of blades are considered in the optimization framework. In the aerodynamic discipline, class function/shape function transformation-based parameterized modeling is used to express the airfoil. The Wilson method is employed to obtain the aerodynamic shape of the blade. Computational fluid dynamics numerical simulation is performed to analyze the aerodynamics of the blade. In the structural discipline, the materials and ply lay-up design are studied. Finite element method-based modal analysis and static structural analysis are conducted to verify the structural design of the blade. A collaborative optimization framework is set up on the Isight platform, employing a genetic algorithm to find the optimal solution for the blade’s aerodynamics and structural properties. In the optimization framework, the design variables refer to the length of the blade chord, twist angle, and lay-up thickness. Additionally, Kriging surrogate models are constructed to reduce the numerical simulation time required during optimization. An optimal Latin hypercube sampling method-based experimental design is employed to determine the samples used in the surrogate models. The optimized blade exhibits improved performance in both the aerodynamic and the structural disciplines.
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institution Kabale University
issn 2076-3417
language English
publishDate 2025-01-01
publisher MDPI AG
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series Applied Sciences
spelling doaj-art-6f7cdd9ef4ba42038026e715cf0bc63a2025-01-24T13:21:00ZengMDPI AGApplied Sciences2076-34172025-01-0115283410.3390/app15020834Collaborative Optimization of Aerodynamics and Wind Turbine BladesFushan He0Xingsheng Zheng1Weilin Luo2Jianfeng Zhong3Yunhua Huang4Aili Ye5Rongrong Qiu6Huafu Ma7College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaSansteel Minguang Co., Ltd., Sanming 365000, ChinaSansteel Minguang Co., Ltd., Sanming 365000, ChinaSansteel Minguang Co., Ltd., Sanming 365000, ChinaSansteel Minguang Co., Ltd., Sanming 365000, ChinaThis paper explores the application of multidisciplinary design optimization to the blades in horizontal-axis wind turbines. The aerodynamics and structural performance of blades are considered in the optimization framework. In the aerodynamic discipline, class function/shape function transformation-based parameterized modeling is used to express the airfoil. The Wilson method is employed to obtain the aerodynamic shape of the blade. Computational fluid dynamics numerical simulation is performed to analyze the aerodynamics of the blade. In the structural discipline, the materials and ply lay-up design are studied. Finite element method-based modal analysis and static structural analysis are conducted to verify the structural design of the blade. A collaborative optimization framework is set up on the Isight platform, employing a genetic algorithm to find the optimal solution for the blade’s aerodynamics and structural properties. In the optimization framework, the design variables refer to the length of the blade chord, twist angle, and lay-up thickness. Additionally, Kriging surrogate models are constructed to reduce the numerical simulation time required during optimization. An optimal Latin hypercube sampling method-based experimental design is employed to determine the samples used in the surrogate models. The optimized blade exhibits improved performance in both the aerodynamic and the structural disciplines.https://www.mdpi.com/2076-3417/15/2/834wind turbine bladesaerodynamic characteristicsstructural characteristicsmultidisciplinary design optimizationnumerical simulation
spellingShingle Fushan He
Xingsheng Zheng
Weilin Luo
Jianfeng Zhong
Yunhua Huang
Aili Ye
Rongrong Qiu
Huafu Ma
Collaborative Optimization of Aerodynamics and Wind Turbine Blades
Applied Sciences
wind turbine blades
aerodynamic characteristics
structural characteristics
multidisciplinary design optimization
numerical simulation
title Collaborative Optimization of Aerodynamics and Wind Turbine Blades
title_full Collaborative Optimization of Aerodynamics and Wind Turbine Blades
title_fullStr Collaborative Optimization of Aerodynamics and Wind Turbine Blades
title_full_unstemmed Collaborative Optimization of Aerodynamics and Wind Turbine Blades
title_short Collaborative Optimization of Aerodynamics and Wind Turbine Blades
title_sort collaborative optimization of aerodynamics and wind turbine blades
topic wind turbine blades
aerodynamic characteristics
structural characteristics
multidisciplinary design optimization
numerical simulation
url https://www.mdpi.com/2076-3417/15/2/834
work_keys_str_mv AT fushanhe collaborativeoptimizationofaerodynamicsandwindturbineblades
AT xingshengzheng collaborativeoptimizationofaerodynamicsandwindturbineblades
AT weilinluo collaborativeoptimizationofaerodynamicsandwindturbineblades
AT jianfengzhong collaborativeoptimizationofaerodynamicsandwindturbineblades
AT yunhuahuang collaborativeoptimizationofaerodynamicsandwindturbineblades
AT ailiye collaborativeoptimizationofaerodynamicsandwindturbineblades
AT rongrongqiu collaborativeoptimizationofaerodynamicsandwindturbineblades
AT huafuma collaborativeoptimizationofaerodynamicsandwindturbineblades