Yajuan Lu^*, Yongtao Hu, Jie Li, Jinping Zhang, Jingjing Si

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.071110 - 31 March 2026

Abstract To address challenges in wind turbine blade defect detection models, primarily due to insufficient feature extraction capabilities and the difficulty of deploying models on drone-type edge devices, this study proposes a wind turbine blade defect detection model, WtCS-YOLO11, that incorporates multiscale feature extraction and an attention mechanism. Firstly, the cross-stage partial with two kernels and a wavelet convolution module (C3k2_WTConv) is proposed by introducing wavelet convolution into the module. The cross-stage partial with two kernels (C3k2) module in the necking network is replaced with the C3k2_WTConv module to increase the model’s receptive field, enable multiscale… More >

Chaoyu Wei¹, Haipeng Zhang¹, Weidong Shi^1,*, Yongfei Yang^1,*, Linwei Tan¹, Xianglong Wu², Yurui Dai¹

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.2, 2026, DOI:10.32604/fdmp.2026.074543 - 04 March 2026

Abstract During high-speed operation, mixed-flow pumps are susceptible to cavitation, which destabilizes the internal flow, increases energy losses, and degrades hydraulic efficiency. To assess the effectiveness of blade perforation as a cavitation-mitigation strategy, in this study several mixed-flow pump models incorporating perforations were developed. Numerical simulations were performed for configurations with circular holes positioned at different locations along the blade leading edge, and the computational results were validated against experimental measurements. The findings indicate that the location of the perforations plays a decisive role in cavitation suppression. Moving from the blade rim toward the hub along More >

Mubarak Alanazi^1,*, Junaid Rashid²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077956 - 26 February 2026

Abstract Wind turbine blade defect detection faces persistent challenges in separating small, low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints. Conventional image-processing pipelines struggle with scalability and robustness, and recent deep learning methods remain sensitive to class imbalance and acquisition variability. This paper introduces TurbineBladeDetNet, a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types. Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability. The model achieves 97.14% accuracy, 98.65% More >

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 >

Yan Wang^1,2,*, Fuqiang Zhang¹, Long An¹, Bo Wang¹, Xueya Yang¹, Jie Jin^3,4

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.11, pp. 2651-2671, 2025, DOI:10.32604/fdmp.2025.070122 - 01 December 2025

Abstract A downburst is a strong downdraft generated by intense thunderstorm clouds, producing radially divergent and highly destructive winds near the ground. Its characteristic scales are expressed through random variations in jet height, velocity, and diameter during an event. In this study, a reduced-scale parked wind turbine is exposed to downburst wind fields to investigate the resulting extreme wind loads. The analysis emphasizes both the flow structure of downbursts and the variations of surface wind pressure on turbine blades under different jet parameters. Results show that increasing jet velocity markedly enhances the maximum horizontal wind speed,… More > Graphic Abstract

Yongsheng Wang^1,2, Xiangwu Lu¹, Wei Yuan^1,*, Lei Zhang¹

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.6, pp. 1505-1528, 2025, DOI:10.32604/fdmp.2025.061052 - 30 June 2025

Abstract This study explores the influence of rotor blade angle on stall inception in an axial fan by means of numerical simulations grounded in the Reynolds-Averaged Navier-Stokes (RANS) equations and the Realizable k-ε turbulence model. By analyzing the temporal behavior of the outlet static pressure, along with the propagation velocity of stall inception, the research identifies distinct patterns in the development of stall. The results reveal that stall inception originates in the second rotor impeller. At a blade angle of 27°, the stall inception follows a modal wave pattern, while in all other cases, it assumes 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

Tianpeng Li^1,*, Yujun Duan², Qianghu Ji³

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.3, pp. 623-644, 2025, DOI:10.32604/fdmp.2024.053862 - 01 April 2025

Abstract A centrifugal pump with a specific speed n_s = 67 is considered in this study to investigate the impact of blade cutting (at the outlet edge) on the fluid-induced noise, while keeping all the other geometric parameters unchanged. The required unsteady numerical calculations are conducted by applying the RNG k-ε turbulence model with the volute dipole being used as the sound source. The results indicate that the internal pressure energy of the centrifugal pump essentially depends on the blade passing frequency and its low-frequency harmonic frequency. Moreover, the pressure pulsation distribution directly affects the noise caused More >

Tingrui Liu¹, Qinghu Cui^1,2, Dan Xu^1,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.1, pp. 1-36, 2025, DOI:10.32604/fdmp.2024.058444 - 24 January 2025

Abstract With the gradual increase in the size and flexibility of composite blades in large wind turbines, problems related to aeroelastic instability and blade vibration are becoming increasingly more important. Given their impact on the lifespan of wind turbines, these subjects have become important topics in turbine blade design. In this article, first aspects related to the aeroelastic (structural and aerodynamic) modeling of large wind turbine blades are summarized. Then, two main methods for blade vibration control are outlined (passive control and active control), including the case of composite blades. Some improvement schemes are proposed More > Graphic Abstract