Haynes, T. A. ORCID: https://orcid.org/0000-0003-0058-2939, Battistini, A., Leide, A. J., Liu, D., Jones, L., Shepherd, D. and Wenman, M. R. (2023) Peridynamic modelling of cracking in TRISO particles for high temperature reactors. Journal of Nuclear Materials, 576. ISSN 0022-3115
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Abstract
A linear-elastic computer simulation (model) for a single particle of TRISO fuel has been built using a bond-based peridynamic technique implemented in the finite element code ‘Abaqus’. The model is able to consider the elastic and thermal strains in each layer of the particle and to simulate potential fracture both within and between layers. The 2D cylindrical model makes use of a plane stress approximation perpendicular to the plane modelled. The choice of plane stress was made by comparison of 2D and 3D finite element models. During an idealised ramp to normal operating power for a kernel of 0.267 W and a bulk fuel temperature of 1305 K, cracks initiate in the buffer near to the kernel-buffer interface and propagate towards the buffer-iPyC coating interface, but do not penetrate the iPyC and containment of the fission products is maintained. In extreme accident conditions, at around 600% (1.60 W) power during a power ramp at 100% power (0.267 W) per second, cracks were predicted to form on the kernel side of the kernel-buffer interface, opposite existing cracks in the buffer. These were predicted to then only grow further with further increases in power. The SiC coating was predicted to subsequently fail at a power of 940% (2.51 W), with cracks formed rapidly at the iPyC-SiC interface and propagating in both directions. These would overcome the containment to fission gas release offered by the SiC ‘pressure vessel’. The extremely high power at which failure was predicted indicates the potential safety benefits of the proposed high temperature reactor design based on TRISO fuel.
Item Type: | Article |
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Faculty \ School: | Faculty of Science > School of Engineering (former - to 2024) |
UEA Research Groups: | Faculty of Science > Research Groups > Sustainable Energy Faculty of Science > Research Groups > Numerical Simulation, Statistics & Data Science |
Depositing User: | LivePure Connector |
Date Deposited: | 23 Jan 2023 15:30 |
Last Modified: | 14 Nov 2024 00:50 |
URI: | https://ueaeprints.uea.ac.uk/id/eprint/90704 |
DOI: | 10.1016/j.jnucmat.2023.154283 |
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