Introduction
The renewable energy sector is experiencing increased demand due to recent energy crises and ever-increasing consumption scenarios around the world.
Wind energy [1, 2], in particular, holds promise for reducing fossil fuel dependence and attracting global investments, with an annual growth rate of over 10%.
Wind turbines are energy generation systems that need to be optimized for different environments, which can be costly due to a lack of knowledge and uncertainties. Vibration and dynamic stability remain persistent issues for wind turbines, limiting the potential of this technology. This project aims to develop smart wind turbines for a sustainable energy supply system based on intelligent materials and active structures for optimal dynamic performance [3, 4] while maximizing the power output considering temporal fluctuations and uncertainty [5] in wind speed and energy demand. An explainable artificial intelligence-based digital twin will be developed for active vibration control and optimal power output through quantification and minimization of associated uncertainties with a component of intellectual oversight and human intelligence.
Based on the above background, the motivation for this study stems from several important challenges. First, while monitoring AI (micro) can eliminate information asymmetries in system, the high-emission firm is less incentivized to implement as benefit might be hurt. Second, forecasting AI (macro) can reduce uncertainties, but its effectiveness depends on model accuracy, which may incur a significant cost. Firms must balance this cost against the benefits of improved accuracy. Third, in competitive supply chains, rivals may adopt AI at different stages, creating strategic asymmetries that influence both market outcomes and environmental performance. These considerations highlight the need for a rigorous analytical study of AI adoption strategies in carbon markets.
Existing literature has studied carbon cap-and-trade in supply chains extensively (Kroes et al. 2012, Huang et al. 2021). There is also growing work on AI adoption in supply chains, primarily focusing on predictive analytics and operational efficiency (Mithas et al. 2022, Jazairy et al. 2025). However, the intersection of AI adoption and carbon market dynamics remains underexplored, particularly with regard to enhancing environmental sustainability in supply chains.
Modern wind turbines operate in highly dynamic and uncertain environments, where unmitigated vibration and fluctuating wind loads can reduce performance and shorten operational life.
To address this, the project will create a physics-informed digital twin that learns from real-time sensor data and updates predictions on structural behaviour, power output, and health status, ensuring that model predictions and decisions are interpretable and trustworthy. These methods not only support greater annual energy production and longer service life but also reduce the embodied and operational carbon of wind energy systems.
The right PhD researcher
The project will make the doctoral student an expert in multidisciplinary areas concerning renewable energy, sustainable system design, digital twins and artificial intelligence that can be explained with human insights, optimization under uncertainty and multi-scale design for optimal structural performance. Such unique and multi-disciplinary skills developed while contributing to an impactful and ambitious research project in collaboration with leading industry partners will prepare the doctoral candidate to be a global leader in tackling challenging research projects concerning energy sustainability through deep technological innovation.
This PhD project is suitable for ambitious PhD students who are willing to make impactful contributions towards sustainable energy generation exploiting the emerging concepts of artificial intelligence and advanced system designs. Students with background in any one of the following areas (and willing to learn new concepts) are suitable for this project: engineering, physics, materials and manufacturing, and artificial intelligence. If you have further queries, do not hesitate to contact: T.Mukhopadhyay@soton.ac.uk
References:
(1) Herbert, G. J., Iniyan, S., Sreevalsan, E., & Rajapandian, S. (2007). A review of wind energy technologies. Renewable and sustainable energy Reviews, 11(6), 1117-1145.
(2) Garcia Marquez, F. P., Peinado Gonzalo, A. (2022). A comprehensive review of artificial intelligence and wind energy. Archives of Computational Methods in Engineering, 29(5), 2935-2958
(3) Sinha, P., & Mukhopadhyay, T. (2023). Programmable multi-physical mechanics of mechanical metamaterials. Materials Science and Engineering: R: Reports, 155, 100745.
(4) Xie, F., Aly, A. M. (2020). Structural control and vibration issues in wind turbines: A review. Engineering Structures, 210, 110087.
(5) Dey, S., Mukhopadhyay, T., Adhikari, S. (2018). Uncertainty quantification in laminated composites: a meta-model based approach. CRC Press.