Matty Warner
Postgraduate Researcher
University of Manchester
Co-hort year: 2024 entry
I am starting the CDT having graduated from an integrated Masters degree in Physics with Astrophysics at the University of Bath in 2023. My Masters project involved the development of new numerical methods, specifically constructing Hamiltonian variational integrators to find solutions to dissipative dynamical systems.
The challenge with identifying materials that can be used to construct plasma-facing components in a fusion reactor is that not only will they be subject to temperatures and pressures comparable to those found in the interior of stars, but they will be required to maintain their structural integrity for at least five years. It is therefore essential to be able to accurately model material deformations under reactor conditions while also reliably qualifying the risk of failure of such a material.
My project will take an integrated approach to risk assess materials, using a crystal plasticity (CP) model to simulate the effects of loading on a particular microstructure, before identifying how these effects will be affected by statistical variations of the original microstructure. The CP model will be used to produce the strength distribution of the material as a function of microstructure for each simulation. An uncertainty quantification (UQ) framework, such as a Markov chain Monte Carlo method, will then be used to provide confidence intervals on the results of the simulations and further statistical analysis will determine the material failure probabilities. This UQ framework will be method agnostic and so equally can provide results based on experimental data or CP modelling inputs. It is hoped that this process can be used to accelerate the design and qualification of fusion reactor components by allowing high fidelity simulations to inform the choice of materials targeted in future experiments, and to enable data from the subsequent experiments to be used to refine the models. More generally, this approach could have wider applications in materials research and other scientific fields where integrating simulation and experiment can dramatically increase the speed and quality of the research.
