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 >

Tao Zhang^1,*, Hailun Wang¹, Tianyue Wang¹, Tian Tian²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072696 - 12 January 2026

Abstract The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation, mechanical wear, and airflow disturbances during prolonged operation. These conditions can lead to a series of issues, including mechanical faults, air path malfunctions, and combustion irregularities. Traditional model-based approaches face inherent limitations due to their inability to handle nonlinear problems, natural factors, measurement uncertainties, fault coupling, and implementation challenges. The development of artificial intelligence algorithms has provided an effective solution to these issues, sparking extensive research into data-driven fault diagnosis methodologies. The review mechanism involved searching IEEE Xplore, ScienceDirect,… 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

Erping Song^1,*, Zipin Yao²

Energy Engineering, Vol.122, No.12, pp. 5129-5147, 2025, DOI:10.32604/ee.2025.063827 - 27 November 2025

Abstract Wind farm layout optimization is a critical challenge in renewable energy development, especially in regions with complex terrain. Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm, where the wake effect, wind speed, types of wind turbines, etc., have an impact on the output power of the wind farm. To solve the optimization problem of wind farm layout under complex terrain conditions, this paper proposes wind turbine layout optimization using different types of wind turbines, the aim is to reduce the influence of the wake effect… More >

Feng Zhou^1,2, Qifei Li^1,*, Lu Xin¹, Shiang Zhang³, Yang Liu¹, Ming Guo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 411-429, 2025, DOI:10.32604/cmes.2025.069639 - 30 October 2025

Abstract Bulb-type hydro turbines are commonly used in small- to medium-scale hydropower stations due to their compact design and adaptability to low-head conditions. However, long-term operation often results in wear at the runner rim, increasing tip clearance and triggering leakage flow and cavitation. These effects reduce hydraulic efficiency and accelerate blade surface erosion, posing serious risks to unit safety and operational stability. This study investigates the influence of tip clearance on cavitation performance in a 24 MW prototype bulb turbine. A three-dimensional numerical model is developed to simulate various operating conditions with different tip clearance values… More >

Ahmad S. Azzahrani^*

Energy Engineering, Vol.122, No.11, pp. 4703-4713, 2025, DOI:10.32604/ee.2025.067398 - 27 October 2025

Abstract The Light Detection and Ranging (LiDAR) data analysis method has emerged as a powerful and versatile tool for characterizing atmospheric conditions and modeling light propagation through various media. In the context of renewable energy, particularly wind energy, LiDAR is increasingly utilized to analyze wind flow, turbine wake effects, and turbulence in complex terrains. This study focuses on advancing LiDAR data interpretation through the development and application of the LiDAR Statistical Barnes Objective Analysis (LiSBOA) method. LiSBOA enhances the capacity of scanning LiDAR systems by enabling more precise optimization of scan configurations and improving the retrieval… More >

Ali Abdulshaheed^1,*, Faizal Mustapha¹, Mohd Anuar²

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.9, pp. 2289-2304, 2025, DOI:10.32604/fdmp.2025.066983 - 30 September 2025

Abstract A pump operating as a turbine (PAT) is a type of hydraulic machine capable of functioning both as a pump and as a turbine by reversing the flow direction. The pump-as-turbine (PAT) approach presents an effective method of hydropower generation, particularly suitable for addressing the increasing global energy demands in rural and remote areas. In addition to its adaptability, PAT-based micro-hydropower systems typically incur lower operating costs than conventional hydrodynamic turbines, despite requiring higher initial investment. Recent research has focused on integrating PATs into pipe distribution systems to harness untapped hydraulic energy. This study presents… More >

Bassel Weiss¹, Segundo Esteban^2,*, Matilde Santos³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3387-3418, 2025, DOI:10.32604/cmes.2025.070070 - 30 September 2025

Abstract Anomaly detection in wind turbines involves emphasizing its ability to improve operational efficiency, reduce maintenance costs, extend their lifespan, and enhance reliability in the wind energy sector. This is particularly necessary in offshore wind, currently one of the most critical assets for achieving sustainable energy generation goals, due to the harsh marine environment and the difficulty of maintenance tasks. To address this problem, this work proposes a data-driven methodology for detecting power generation anomalies in offshore wind turbines, using normalized and linearized operational data. The proposed framework transforms heterogeneous wind speed and power measurements into… More > Graphic Abstract

Ahmad Fazlizan^1,*, Wan Khairul Muzammil², Najm Addin Al-Khawlani¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1371-1437, 2025, DOI:10.32604/cmes.2025.067854 - 31 August 2025

Abstract This review provides a comprehensive and systematic examination of Computational Fluid Dynamics (CFD) techniques and methodologies applied to the development of Vertical Axis Wind Turbines (VAWTs). Although VAWTs offer significant advantages for urban wind applications, such as omnidirectional wind capture and a compact, ground-accessible design, they face substantial aerodynamic challenges, including dynamic stall, blade–wake interactions, and continuously varying angles of attack throughout their rotation. The review critically evaluates how CFD has been leveraged to address these challenges, detailing the modelling frameworks, simulation setups, mesh strategies, turbulence models, and boundary condition treatments adopted in the literature.… More >