Lifu He¹, Zhongchu Huang¹, Haidong Shao^2,*, Zhangbo Hu¹, Yuting Wang¹, Jie Mei¹, Xiaofei Zhang³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073227 - 12 January 2026

Abstract Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction. However, several challenges remain. First, signal noise collected during blade operation masks fault features, severely impairing the fault diagnosis performance of deep learning models. Second, current blade fault diagnosis often relies on single-sensor data, resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states. To address these issues, a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed. Specifically, a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to More >

Ramazan Özkan^1,2, Mustafa Serdar Genç^1,3,*, İlker Kayali^1,4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3349-3380, 2025, DOI:10.32604/cmes.2025.072519 - 23 December 2025

Abstract The optimization of turbine blades is crucial in improving the efficiency of wind energy systems and developing clean energy production models. This paper presented a novel approach to the structural design of small-scale turbine blades using the Artificial Bee Colony (ABC) Algorithm based on the stochastic method to optimize both mass and cost (objective functions). The study used computational fluid dynamics (CFD) and structural analysis to consider the fluid-structure interaction. The optimization algorithm defined several variables: structural constraints, the type of composite material, and the number of composite layers to form a mathematical model. The More >

Md. Mahbub Alam^*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.4, pp. 657-695, 2025, DOI:10.32604/fdmp.2025.060942 - 06 May 2025

Abstract Wind turbines play a vital role in renewable energy production. This review examines advancements in wind turbine blade morphing technologies aimed at enhancing power coefficients, reducing vibrations, and minimizing noise generation. Efficiency, vibration, and noise levels can be optimized through morphing techniques applied to the blade’s shape, leading edge, trailing edge, and surface. Leading-edge morphing is particularly effective in improving efficiency and reducing noise, as flow attachment and separation at the leading edge significantly influence lift and vortex generation. Morphing technologies often draw inspiration from bionic designs based on natural phenomena, highlighting the potential of More > Graphic Abstract

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 >