AI-assisted grading of end-of-life wind turbine composite materials for a circular economy

Project Description:

Applications are closed for this project.

This PhD scholarship is offered by the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience; a partnership between the Universities of Durham, Hull, Loughborough and Sheffield.  The successful applicant will undertake six-month of training with the rest of the CDT cohort at the University of Hull before continuing their PhD research at the University of Sheffield.

End-of-life blade waste is a pressing issue for the wind energy industry, both for existing blades and future blades. The vast majority of existing blades cannot be recycled and are currently destined for low-value, high-emission end-of-life routes (e.g., incineration or grinding for filler in cement).

The value of these robust composite materials can be retained via reuse in structural applications. For example, it has shown that existing blade materials have sufficient properties and dimensional tolerances to be segmented and used as basic construction elements. Thermoplastic composites blades, likely the blades of the future, have additional potential in that they can be thermoformed into new geometries, while also ‘healing’ the thermoplastic matrix. The challenge of reuse is that blade materials vary based on several factors including microstructure, macrostructure, and loading history.

This project directly aligns with the CDT’s aim to achieve a sustainable wind farm lifecycle by developing methods for high-value reuse of composite turbine blades. Machine learning and non-destructive evaluation techniques will be developed to efficiently grade the end-of-life material properties. These techniques are already in development at the University of Sheffield for assessing in operando material properties of offshore wind turbine blades. Successful re-development for end-of-life composites could enable reuse in other structural applications.

This PhD will investigate the development of hierarchical Bayesian algorithms to capture the variability of end-of-life composite materials. These algorithms will be combined with destructive and non-destructive test data, and then be used to develop predictive capabilities for grading. This work will establish a foundation for future automated grading systems, which will provide a cost-competitive solution for both the offshore wind energy sector and the wider composites industry. 

Training and development

You will benefit from a taught programme, giving you a broad understanding of the breadth and depth of current and emerging offshore wind sector needs. This begins with an intensive six-month programme at the University of Hull for the new student intake, drawing on the expertise and facilities of all four academic partners. It is supplemented by Continuing Professional Development (CPD), which is embedded throughout your 4-year research scholarship.

You will receive in-house training in statistical methods, composite manufacturing and machining, and mechanical testing, including various methods of destructive and non-destructive evaluation. Industrial visits will act as an additional sources of training in the current state-of-the-art in the project area.

If you have any queries about this project, please contact Dr James Maguire j.maguire@sheffield.ac.uk

You may also address queries about the CDT to auracdt@hull.ac.uk.

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References & further reading

[1] Jelle Joustra, Bas Flipsen, Ruud Balkenende, Structural reuse of wind turbine blades through segmentation, Composites Part C: Open Access, 2021, 5, 100137, DOI: https://doi.org/10.1016/j.jcomc.2021.100137

[2] Marcus Haywood-Alexander, Nikolaos Dervilis, Keith Worden, Robin S. Mills, Purim Ladpli, Timothy J. Rogers, A Bayesian Method for Material Identification of Composite Plates via Dispersion Curves, Sensors, 2023, 23(185), p1-22, DOI: https://doi.org/10.3390/s23010185

[3] Angela J. Nagle, Gerard Mullally, Paul G. Leahy, Niall P. Dunphy, Life cycle assessment of the use of decommissioned wind blades in second life applications, Journal of Environmental Management, 2022, 302, 113994, DOI: https://doi.org/10.1016/j.jenvman.2021.113994

[4] Fanran Meng, Jennifer L. Hawkin, Lukas Gast, Stella Job, Conchúr Ó Brádaigh, et al., End-of-life management for wind turbines, Nature Reviews Clean Technology, 2025, DOI: https://doi.org/10.1038/s44359-025-00097-3