Physics informed deep learning for fusion thermal hydraulics

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A significant challenge in magnetic confinement fusion is the high heat loading of the plasma-facingcomponents. High energy neutrons bombard these components, creating a non-uniform volumetric heatloading which must be transferred to prevent thermal damage. While numerical simulation offers anattractive route forward for the design of these components, its high cost, breadth and highdimensionality limits efficacy in design space exploration.In this project, the potential of physics aware deep learning surrogate models as candidates for full fluidflow PDE modelling will be investigated. Our motivation is efficient exploration of parameter space forfusion thermal hydraulics. Physics informed neural network (PINN) techniques have already shownsignificant promise in isothermal flows without magnetohydrodynamic (MHD) effects but need furtherdeveloping to account for fusion relevant conditions. PINNs will be used for broad and rapid parameterspace exploration while selected high-order Computational Fluid dynamics (CFD) studies (generated aspart of this PhD) will be used in training and testing of the model.This work will focus on turbulent buoyant MHD, with conjugate heat transfer and inhomogeneousvolumetric heating. The cases studies are a heated cavity and serpentine passage configuration, bothbeing central to blanket / divertor design in fusion. This will provide a vital understanding of the complexflow physics present in the plasma-facing components and is expected to lead to improved designs infuture fusion plants.