Open to: UK fee eligible applicants only
Funding Providers: FOSTER, UKAEA + FSE
Subject Area: Nuclear Fusion
Project Start Dates: January 2027 **
* (Please see the note below regarding potential later start dates.)
**In exceptional circumstances, and subject to the discretion of the University and/or the relevant funding body, a deferral of offer may be granted to the next available enrolment period. Such deferral will typically not exceed a duration of three calendar months from the originally stipulated commencement date. Please note that only one deferral may be considered, and any such deferral is not guaranteed.
Supervisors:
- Professor Perumal Nithiarasu
- Dr Alberto Coccarelli
- Andy Davies (UKAEA)
Aligned programme of study: Mechanical Engineering, PhD
Mode of study: Full-time
Place of study: Swansea University (Bay Campus)
Project description:
In future fusion power plants operating with a closed-loop fuel cycle, breeding a sufficient quantity of tritium is essential for sustained power generation. A key performance metric is the Tritium Breeding Ratio (TBR), which depends on several tightly coupled factors, including breeder blanket design, plasma-facing surface area, neutron transport, material composition, and cooling performance. Since cooling is also intrinsically linked to heat extraction, structural integrity, and irradiation-induced material damage, TBR optimisation represents a highly coupled neutronic–thermomechanical challenge.
This PhD project will investigate this coupled problem within the context of UK-specific tokamak reactor designs, working collaboratively with other researchers and doctoral students across related areas. The primary focus will be on neutronics and optimisation of TBR within realistic fusion operating conditions.
Initially, the research will employ Monte Carlo neutronics methods using tools such as OpenMC (or equivalent) to model neutron transport and tritium breeding behaviour within breeder blanket configurations. The project will then extend toward accelerated predictive methodologies using machine learning and AI approaches, including surrogate modelling and large language model (LLM)-assisted information extraction from openly available international fusion datasets and literature.
Applications may be submitted in Welsh and any application submitted in Welsh will be treated no less favourably than an application submitted in English. Please refer to the University’s Welsh Language Policy on Awarding Grants
