Polymer composite-based turbine in wind energy harvesting
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Eris Elianddy Supeni
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Azizan As’arry
Abstract
This study explores the development and optimization of polymer composite-based wind turbine blades, integrating glass fiber reinforced plastic (GFRP) with shape memory alloy (SMA) to enhance performance in wind energy harvesting. Advances in materials science, aerodynamics, computational modelling, and structural analysis have been leveraged to improve blade efficiency, durability, and self-adaptive capabilities. The research employs finite element analysis (FEA) and artificial neural networks (ANN) to evaluate the mechanical behaviour of composite blades under varying loads. A graded beam model was developed to assess the effects of ply drop-off and material distribution on structural integrity. Experimental validation confirmed that SMA integration enhances blade deformation recovery, mitigating stress accumulation and improving aerodynamic stability. The results demonstrate that GFRP-SMA blades achieve a performance coefficient approaching the Betz limit (0.5923), reducing deflections and improving load response. Despite these advancements, challenges remain in optimizing SMA wire placement, adhesion, and actuation efficiency. Future work should focus on refining material interfaces, developing adaptive control mechanisms, and validating the model in full-scale wind turbine applications. This study contributes to the next-generation smart wind turbine blade design, addressing structural limitations while enhancing energy efficiency and operational resilience.
Acknowledgments
The authors would like to thank the editors S.M. Sapuan, Mohd Roshdi Hassan, Eris Elianddy Supeni and Azizan As’arry for their guidance and review of this article before its publication.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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