Magnetic confinement of plasma in a tokamak device offers a promising route towards commercial fusion power. A significant challenge, however, is the high heat loading of the plasma-facing components (the blanket and divertor). High energy neutrons bombard these components, creating a non-uniform volumetric heat loading. This heat must be adequately transferred to prevent thermal damage. While numerical simulation can be employed as an effective design tool, the requisite scale of such simulations, when combined with the breadth and high dimensionality of parametric space, limits their practicality / efficiency in design space exploration.
In this project, the potential of physics aware deep learning surrogate models as candidates for full fluid flow PDE modelling will be investigated. Our motivation is efficient exploration of parameter space for fusion thermal hydraulics. Physics informed neural networks (PINNs) have already shown significant promise in isothermal flows and/or flows without magnetohydrodynamic (MHD) effects but need further developing to account for the extreme conditions relevant to fusion, where MHD effects and radiative heating are prevalent. PINNs will be used for broad and rapid parameter space exploration while selected high-order Computational Fluid Dynamics (CFD) studies (to be generated as part of this PhD) will be used in training and testing of the model.
This work will focus on turbulent buoyant MHD, with heat transfer between the fluid and solid walls, and inhomogeneous volumetric heating. The cases to be studied are a heated cavity and serpentine passage configuration, both being central to blanket / divertor design in the fusion industry. This will provide a vital understanding of the complex flow physics present in the plasma-facing components and is expected to lead to improved designs in future fusion plants.
You will have access to a range of internal training courses, aimed at developing both soft and technical skills. You will also have the opportunity to sit on MSc-level courses in CFD, machine learning and heat transfer. At a research group level, we run monthly paper study groups and fortnightly programming group to develop and support coding best practice.
Informal enquiries about the post can be made to Dr Alex Skillen ([Email Address Removed]). Please contact the Admissions Team at [Email Address Removed] with any queries you may have regarding the application process.
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