A key aspect of this scientific computing research is to address challenging problems within the nuclear energy sector, such as the development of multiphysics modelling and simulations (M&S) of thermal fatigue phenomena within nuclear power plants (NPPs) and the design of novel radiation detection technologies. Thermal fatigue phenomena are widespread within many areas of engineering and can limit the operational life of components, cause power outages, and require costly periodic inspections. Therefore, high-fidelity predictive models enable engineers to improve the reliability of engineering components and optimise maintenance scheduling. The development of novel radiation detection technologies is critical not only in the nuclear power sector but also for nuclear security and safeguard. Both these fields require the application of advanced high-performance computing (HPC) M&S along with uncertainty quantifications (UQ), surrogate modelling (SM), and machine learning (ML) algorithms. This research work aims to develop computationally efficient and improved M&S methods as well as advanced, cost-effective, radiation detector designs with benefits to both the engineering community and the nuclear energy sector.

I hold a Bachelor and Master’s degree in Nuclear Engineering from the University of Pisa in Italy and a PhD from the Nuclear Engineering Group of the Department of Mechanical Engineering at Imperial. My research work has been focused on developing high-fidelity multi-physics modelling and simulations and efficient data-driven machine learning surrogate models for critically important problems for nuclear power plant safety and control, such as thermal fatigue phenomena in T-junction pipes and design optimisation of neutron detectors within the reactor core.