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<p>This research investigates the dynamic response of a glass-filled nylon wind turbine blade subjected to ice shedding and turbulent wind conditions, with the aim of understanding and predicting structural behaviour under realistic operating scenarios. Experiments were conducted to capture the blade’s vibration response under controlled conditions, with the blade fixed at the root and measurements taken at the mid-span and tip. Ice shedding was modelled by attaching masses ranging from 100&#xa0;g to 1&#xa0;kg at various positions and release angles (− 5°, 0°, 5°, and 15°), allowing evaluation of how sudden mass loss influences dynamic behaviour; the first peak response was analysed as it represents the maximum deflection and highest risk of structural failure. A finite element model was developed and validated using the experimental results to accurately represent the blade’s structural dynamics and predict its natural frequencies and mode shapes, with frequency differences below 0.6% confirming model accuracy. Random vibration analysis using the Kaimal turbulence spectrum with frequency differences below 0.6% confirming model accuracy. In addition, neural network models such as Multi-Layer Perceptron, Levenberg-Marquardt (LM), and Scaled Conjugate Gradient were trained on 140 data points to predict trends in blade displacement amplitude as a function of ice mass, sensor location, and blade orientation under ice shedding and sudden mass loss scenarios, with Bayesian Regularization achieving the best performance (RMSE = 0.0032). While the blade response follows classical bending behaviour, the novelty of this study lies in the experimental methodology and integrated framework for evaluating ice-shedding effects. A noticeable change in the response is observed when the released mass becomes comparable to the blade mass, demonstrating the sensitivity of the method to mass variation. Overall, the approach provides a basis for design evaluation and structural monitoring of wind turbine blades under ice shedding and turbulent wind conditions.</p>

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Experimental, numerical, and data-driven analysis of impulsive ice-shedding-induced vibrations in wind turbine blades

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Iyad F. Al-Najjar,
  • Károly Jálics,
  • László E. Kollár

摘要

This research investigates the dynamic response of a glass-filled nylon wind turbine blade subjected to ice shedding and turbulent wind conditions, with the aim of understanding and predicting structural behaviour under realistic operating scenarios. Experiments were conducted to capture the blade’s vibration response under controlled conditions, with the blade fixed at the root and measurements taken at the mid-span and tip. Ice shedding was modelled by attaching masses ranging from 100 g to 1 kg at various positions and release angles (− 5°, 0°, 5°, and 15°), allowing evaluation of how sudden mass loss influences dynamic behaviour; the first peak response was analysed as it represents the maximum deflection and highest risk of structural failure. A finite element model was developed and validated using the experimental results to accurately represent the blade’s structural dynamics and predict its natural frequencies and mode shapes, with frequency differences below 0.6% confirming model accuracy. Random vibration analysis using the Kaimal turbulence spectrum with frequency differences below 0.6% confirming model accuracy. In addition, neural network models such as Multi-Layer Perceptron, Levenberg-Marquardt (LM), and Scaled Conjugate Gradient were trained on 140 data points to predict trends in blade displacement amplitude as a function of ice mass, sensor location, and blade orientation under ice shedding and sudden mass loss scenarios, with Bayesian Regularization achieving the best performance (RMSE = 0.0032). While the blade response follows classical bending behaviour, the novelty of this study lies in the experimental methodology and integrated framework for evaluating ice-shedding effects. A noticeable change in the response is observed when the released mass becomes comparable to the blade mass, demonstrating the sensitivity of the method to mass variation. Overall, the approach provides a basis for design evaluation and structural monitoring of wind turbine blades under ice shedding and turbulent wind conditions.