Mechanical engineering, fusion, digital: An AI enhanced modelling of coupled tritium breeding and heat exchange for fusion breeder blankets
About the Project
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.
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