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UKAEA-CCFE-PR(23)1862023
In recent years, physics-informed neural networks (PINN) have been used to solve stiff-PDEs mostly in the 1D and 2D spatial domain. PINNs still experience issues solving 3D problems, especially, problems with conflicting boundary conditions at adjacent edges and corners. These problems have discontinuous solutions at edges and corners that are di…
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UKAEA-CCFE-PR(21)102021
The divertor target plates are the most exposed in-vessel components to high heat flux loads in a fusion reactor due to a combination of plasma bombardment, radiation and nuclear heating. Reliable exhaust systems of such a huge thermal power required a robust and durable divertor target with a sufficiently large heat removal capability and lifetime…
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UKAEA-CCFE-CP(19)492019
This paper presents the application of the image-based finite element method (IBFEM) to quantify the impact of micro-structure caused by manufacturing processes on the performance of fusion energy heat exchangers. The components, containing tungsten, were imaged with high-power lab-based X-ray CT and neutron CT. Volume data was converted into part-…
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UKAEA-CCFE-PR(18)662018
Functional materials’ properties are influenced by microstructures which can be changed during manufacturing. Experimental characterisation is often time consuming and expensive. A technique is presented which digitises graphite foam via X-ray tomography and converts it into image-based models to determine properties in silico. By simulating a la…
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UKAEA-CCFE-PR(18)532018
Within a tokamak fusion energy device, the performance and lifespan of a divertor monoblock under high heat flux cycles is of particular interest. Key to this is the quality of manufacture, especially the material joining interfaces. Presented here is a comparative study between X-ray and neutron tomography to investigate the quality of manufacture…
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