Jianyong Ao¹, Yanping Li¹, Shengqing Hu¹, Songyu Gao², Qi Yao^2,*

Energy Engineering, Vol.121, No.12, pp. 3825-3841, 2024, DOI:10.32604/ee.2024.055250 - 22 November 2024

Abstract Blades are essential components of wind turbines. Reducing their fatigue loads during operation helps to extend their lifespan, but it is difficult to quickly and accurately calculate the fatigue loads of blades. To solve this problem, this paper innovatively designs a data-driven blade load modeling method based on a deep learning framework through mechanism analysis, feature selection, and model construction. In the mechanism analysis part, the generation mechanism of blade loads and the load theoretical calculation method based on material damage theory are analyzed, and four measurable operating state parameters related to blade loads are… More >

Shuang Kang¹, Yinchao He^1,2, Wenwen Li^1,*, Sen Liu²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 933-949, 2024, DOI:10.32604/cmc.2024.056614 - 15 October 2024

Abstract To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades (WTB), this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm. First, mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image. The algorithm employs entropy as the objective function to guide the iteration process of image enhancement, selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations, effectively enhancing the detail features of defect… More >

Zhan Wang¹, Liang Li^2,*, Long Wang¹, Weidong Zhu³, Yinghui Li⁴, Echuan Yang⁵

Energy Engineering, Vol.121, No.10, pp. 2981-3000, 2024, DOI:10.32604/ee.2024.048161 - 11 September 2024

Abstract A dynamic pitch strategy is usually adopted to improve the aerodynamic performance of the blade of a wind turbine. The dynamic pitch motion will affect the linear vibration characteristics of the blade. However, these influences have not been studied in previous research. In this paper, the influences of the rigid pitch motion on the linear vibration characteristics of a wind turbine blade are studied. The blade is described as a rotating cantilever beam with an inherent coupled rigid-flexible vibration, where the rigid pitch motion introduces a parametrically excited vibration to the beam. Partial differential equations More > Graphic Abstract

Xin Guan^*, Yuqi Xie, Shuaijie Wang, Mingyang Li, Shiwei Wu

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.9, pp. 2045-2058, 2024, DOI:10.32604/fdmp.2024.049671 - 23 August 2024

Abstract The effects of the erosion present on the leading edge of a wind turbine airfoil (DU 96-W-180) on its aerodynamic performances have been investigated numerically in the framework of a SST k–ω turbulence model based on the Reynolds Averaged Navier-Stokes equations (RANS). The results indicate that when sand-induced holes and small pits are involved as leading edge wear features, they have a minimal influence on the lift and drag coefficients of the airfoil. However, if delamination occurs in the same airfoil region, it significantly impacts the lift and resistance characteristics of the airfoil. Specifically, as More >

Wanrun Li^1,2,3,*, Wenhai Zhao¹, Tongtong Wang¹, Yongfeng Du^1,2,3

Structural Durability & Health Monitoring, Vol.18, No.5, pp. 553-575, 2024, DOI:10.32604/sdhm.2024.050751 - 19 July 2024

Abstract The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage, impacting the aerodynamic performance of the blades. To address the challenge of detecting and quantifying surface defects on wind turbine blades, a blade surface defect detection and quantification method based on an improved Deeplabv3+ deep learning model is proposed. Firstly, an improved method for wind turbine blade surface defect detection, utilizing Mobilenetv2 as the backbone feature extraction network, is proposed based on an original Deeplabv3+ deep learning model to address the issue of limited robustness. Secondly, through integrating the concept of… More > Graphic Abstract

Yuanjun Dai^1,4,*, Dong Wang¹, Xiongfei Liu², Weimin Wu³

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.7, pp. 1665-1665, 2024, DOI:10.32604/fdmp.2024.053146 - 23 July 2024

Abstract This article has no abstract. More >

Yuanjun Dai^1,4,*, Dong Wang¹, Xiongfei Liu², Weimin Wu³

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.7, pp. 1647-1664, 2024, DOI:10.32604/fdmp.2024.046158 - 23 July 2024

Abstract To enhance the aerodynamic performance of wind turbine blades, this study proposes the adoption of a bionic airfoil inspired by the aerodynamic shape of an eagle. Based on the blade element theory, a non-uniform extraction method of blade elements is employed for the optimization design of the considered wind turbine blades. Moreover, Computational Fluid Dynamics (CFD) is used to determine the aerodynamic performances of the eagle airfoil and a NACA2412 airfoil, thereby demonstrating the superior aerodynamic performance of the former. Finally, a mathematical model for optimizing the design of wind turbine blades is introduced and More >

Jicai Guo^1,2, Xiaowen Song^1,2,*, Chang Liu^1,2, Yanfeng Zhang^1,2, Shijie Guo^1,2, Jianxin Wu^1,2, Chang Cai³, Qing’an Li^3,*

Energy Engineering, Vol.121, No.7, pp. 1739-1758, 2024, DOI:10.32604/ee.2024.048854 - 11 June 2024

Abstract In winter, wind turbines are susceptible to blade icing, which results in a series of energy losses and safe operation problems. Therefore, blade icing detection has become a top priority. Conventional methods primarily rely on sensor monitoring, which is expensive and has limited applications. Data-driven blade icing detection methods have become feasible with the development of artificial intelligence. However, the data-driven method is plagued by limited training samples and icing samples; therefore, this paper proposes an icing warning strategy based on the combination of feature selection (FS), eXtreme Gradient Boosting (XGBoost) algorithm, and exponentially weighted… More >

Haixia Kou^1,*, Bowen Yang¹, Xuyao Zhang², Xiaobo Yang¹, Haibo Zhao¹

Structural Durability & Health Monitoring, Vol.18, No.3, pp. 277-297, 2024, DOI:10.32604/sdhm.2024.045023 - 15 May 2024

Abstract Given the difficulty in accurately evaluating the fatigue performance of large composite wind turbine blades (referred to as blades), this paper takes the main beam structure of the blade with a rectangular cross-section as the simulation object and establishes a composite laminate rectangular beam structure that simultaneously includes the flange, web, and adhesive layer, referred to as the blade main beam sub-structure specimen, through the definition of blade sub-structures. This paper examines the progressive damage evolution law of the composite laminate rectangular beam utilizing an improved 3D Hashin failure criterion, cohesive zone model, B-K failure More > Graphic Abstract

Vasishta Bhargava Nukala^*, Chinmaya Prasad Padhy

Sound & Vibration, Vol.58, pp. 133-150, 2024, DOI:10.32604/sv.2024.047762 - 19 March 2024

Abstract A significant aerodynamic noise from wind turbines arises when the rotating blades interact with turbulent flows. Though the trailing edge of the blade is an important source of noise at high frequencies, the present work deals with the influence of turbulence distortion on leading edge noise from wind turbine blades which becomes significant in low-frequency regions. Four quasi-empirical methods are studied to verify the accuracy of turbulent inflow noise predicted at low frequencies for a 2 MW horizontal axis wind turbine. Results have shown that all methods exhibited a downward linear trend in noise spectra More >