Husam AlShannaq, Aly Mousaad Aly^*

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 707-737, 2024, DOI:10.32604/sdhm.2024.054731 - 20 September 2024

Abstract Wind turbines have emerged as a prominent renewable energy source globally. Efficient monitoring and detection methods are crucial to enhance their operational effectiveness, particularly in identifying fatigue-related issues. This review focuses on leveraging artificial neural networks (ANNs) for wind turbine monitoring and fatigue detection, aiming to provide a valuable reference for researchers in this domain and related areas. Employing various ANN techniques, including General Regression Neural Network (GRNN), Support Vector Machine (SVM), Cuckoo Search Neural Network (CSNN), Backpropagation Neural Network (BPNN), Particle Swarm Optimization Artificial Neural Network (PSO-ANN), Convolutional Neural Network (CNN), and nonlinear autoregressive… 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 >

Xin Guan^1,2,*, Mingyang Li¹, Shiwei Wu¹, Yuqi Xie¹, Yongpeng Sun¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.9, pp. 2029-2043, 2024, DOI:10.32604/fdmp.2024.049499 - 23 August 2024

Abstract The focus of this research was on the equivalent particle roughness height correction required to account for the presence of ice when determining the performances of wind turbines. In particular, two icing processes (frost ice and clear ice) were examined by combining the FENSAP-ICE and FLUENT analysis tools. The ice type on the blade surfaces was predicted by using a multi-time step method. Accordingly, the influence of variations in icing shape and ice surface roughness on the aerodynamic performance of blades during frost ice formation or clear ice formation was investigated. The results indicate that 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^1,2,3,*, Zetao Deng¹, Baohua Li², Lei Zhong¹, Jianping Wang¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.7, pp. 1517-1537, 2024, DOI:10.32604/fdmp.2024.046828 - 23 July 2024

Abstract A combined experimental and numerical research study is conducted to investigate the complex relationship between the structure and the aerodynamic performances of an Archimedes spiral wind turbine (ASWT). Two ASWTs are considered, a prototypical version and an improved version. It is shown that the latter achieves the best aerodynamic performance when the spread angles at the three sets of blades are α = 30°, α = 55°, α = 60°, respectively and the blade thickness is 4 mm. For a velocity V = 10 m/s, a tip speed ratio (TSR) = 1.58 and 2, the maximum C values More > Graphic Abstract

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 >

Hongjiang Wang¹, Qingze Shen^2,*, Qin Dai¹, Yingcai Gao², Jing Gao², Tian Zhang^3,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 625-642, 2024, DOI:10.32604/cmc.2024.051757 - 18 July 2024

Abstract Deep learning has emerged in many practical applications, such as image classification, fault diagnosis, and object detection. More recently, convolutional neural networks (CNNs), representative models of deep learning, have been used to solve fault detection. However, the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error. For this reason, an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection. YOLOv8 is a CNN-backed object detection model. Specifically, to reduce… More >

Shaokun Liu¹, Zhiying Gao^1,2,*, Rina Su^1,2, Mengmeng Yan¹, Jianwen Wang^1,2

Energy Engineering, Vol.121, No.8, pp. 2213-2229, 2024, DOI:10.32604/ee.2024.049616 - 19 July 2024

Abstract Although the aerodynamic loading of wind turbine blades under various conditions has been widely studied, the radial distribution of load along the blade under various yaw conditions and with blade flapping phenomena is poorly understood. This study aims to investigate the effects of second-order flapwise vibration on the mean and fluctuation characteristics of the torque and axial thrust of wind turbines under yaw conditions using computational fluid dynamics (CFD). In the CFD model, the blades are segmented radially to comprehensively analyze the distribution patterns of torque, axial load, and tangential load. The following results are… More >