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  Model-based Approach for Prognostics Health Management (PHM) for Fusion Reactors


   School of Physics, Engineering and Technology

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The University of York is at the forefront of transformative research in data-centric engineering, digital twins, and AI, bringing about a revolution in the design and operation of systems. As part of this ground-breaking initiative, we invite applications for PhD positions in the field of Model-based approach for PHM for Fusion Reactors.

Within this context, we are undertaking a pioneering PhD research project focused on addressing the critical need for proactive maintenance and decision-making in fusion reactors. The primary objective is to develop advanced mathematical models and algorithms for PHM, enabling accurate predictions of the health status and remaining useful life (RUL) of critical components within fusion reactor systems. By achieving this, we aim to optimize reactor operation, mitigate unexpected failures, and reduce operational costs.

Fusion reactors represent complex and demanding systems that necessitate continuous monitoring and maintenance to ensure their safe and efficient operation. The ability to predict the health status and RUL of critical components is paramount in maximizing reactor availability and minimizing operational expenses. Hence, our research focuses on utilizing a model-based approach to create mathematical models and algorithms that facilitate precise prognostics health management in fusion reactors.

The project entails the development of both physics-based models and data-driven models to capture the behaviour and performance of critical components within fusion reactor systems. Physics-based models will be grounded in fundamental principles, incorporating the pertinent physical laws and equations that govern reactor operations. Data-driven models, on the other hand, will leverage historical data to learn the intricate relationships between input parameters and component health.

To achieve accurate prognostics analysis, various advanced PHM algorithms will be investigated and developed. These algorithms may encompass statistical methods, machine learning techniques, or hybrid approaches that combine both paradigms. Regression models, time-series analysis, artificial neural networks, and particle filters are among the potential techniques to be explored, tailored to the unique challenges of fusion reactor PHM.

The University of York takes immense pride in being ranked among the top ten UK universities in the REF, underscoring our unwavering commitment to research excellence with a profound social impact. Our vision aligns with that of a University for the Public Good, forging robust partnerships to expand and disseminate knowledge for the benefit of local and global communities. This project's overarching ambition and potential impact lie in the development of an advanced PHM framework for fusion reactor operation and maintenance. Anticipated outcomes include precise predictions of component health status and RUL, empowering proactive maintenance strategies, optimized operational decision-making, and the prevention of unexpected failures. The research findings will significantly enhance the reliability, safety, and performance of fusion reactors, driving the realization of sustainable and clean energy generation.

We invite you to join us in shaping the future of clean energy generation by applying for a PhD at the University of York. Together, we will revolutionize the design and operation of fusion systems, making a substantial contribution to society as a whole.

Entry requirements:

Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Engineering, Physics, Computer Science, Mathematics or a closely-related subject.

How to apply:

Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.


Engineering (12)

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website for details about funding opportunities at York. View Website

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