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

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

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

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

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

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

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 >

Qifa Lang, Yuqiao Zheng^*, Tiancai Cui, Chenglong Shi, Heyu Zhang

Energy Engineering, Vol.121, No.2, pp. 483-498, 2024, DOI:10.32604/ee.2023.042437

Abstract This work presents a novel approach to achieve nonlinear vibration response based on the Hamilton principle. We chose the 5-MW reference wind turbine which was established by the National Renewable Energy Laboratory (NREL), to research the effects of the nonlinear flap-wise vibration characteristics. The turbine wheel is simplified by treating the blade of a wind turbine as an Euler-Bernoulli beam, and the nonlinear flap-wise vibration characteristics of the wind turbine blades are discussed based on the simplification first. Then, the blade’s large-deflection flap-wise vibration governing equation is established by considering the nonlinear term involving the… More >

Yuanjun Dai^1,2,*, Xin Wei¹, Baohua Li¹, Cong Wang¹, Kunju Shi¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.2, pp. 337-348, 2024, DOI:10.32604/fdmp.2023.042434

Abstract To study the impact of the trailing-edge wear on the vibrational behavior of wind-turbine blades, unworn blades and trailing-edge worn blades have been assessed through relevant modal tests. According to these experiments, the natural frequencies of trailing-edge worn blades −1, −2, and −3 increase the most in the second to fourth order, the fifth order increases in the middle, and the first order increases the least. The damping ratio data indicate that, in general, the first five-order damping ratios of trailing-edge worn blades −1 and trailing-edge worn blades −2 are reduced, and the first five-order More >

Wenyang Tang^1,2, Cong Liu^1,*, Bo Zhang²

Energy Engineering, Vol.120, No.11, pp. 2667-2681, 2023, DOI:10.32604/ee.2023.040743

Abstract Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades. The cracks on the blades can endanger the shafting of the generator set, the tower and other components, and even cause the tower to collapse. To achieve high-precision wind blade crack detection, this paper proposes a crack fault-detection strategy that integrates Gated Residual Network (GRN), a fusion module and Transformer. Firstly, GRN can reduce unnecessary noisy inputs that could negatively impact performance while preserving the integrity of feature information. In addition, to gain… More >

Yi Zhang^*, Jiamin Guo, Huanghua Peng

Energy Engineering, Vol.120, No.11, pp. 2489-2502, 2023, DOI:10.32604/ee.2023.029496

Abstract The windy environment is the main cause affecting the efficiency of offshore wind turbine installation. In order to improve the stability and efficiency of single-blade installation of offshore wind turbines under high wind speed conditions, the Stewart platform is used as an auxiliary tool to help dock the wind turbine blade in this paper. In order to verify the effectiveness of the Stewart platform for blade docking, a blade docking simulation system consisting of the Stewart platform, wind turbine blade, and wind load calculation module was built based on Simulink/Simscape Multibody. At the same time, More >