Journal article 878 views
Use of massively parallel computing to improve modelling accuracy within the nuclear sector
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J D Arregui-Mena,
P M Mummery,
R Akers,
E Surrey,
A Shterenlikht,
M Broggi,
L Margetts
The International Journal of Multiphysics, Volume: 10, Issue: 2, Pages: 215 - 236
Swansea University Author:
Llion Evans
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DOI (Published version): 10.21152/1750-9548.10.2.215
Abstract
The extreme environments found within the nuclear sector impose large safety factors on modelling analyses to ensure components operate in their desired manner. Improving analysis accuracy has clear value of increasing the design space that could lead to greater efficiency and reliability.Novel mate...
| Published in: | The International Journal of Multiphysics |
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| ISSN: | 1750-9548 |
| Published: |
2016
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa40001 |
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| Abstract: |
The extreme environments found within the nuclear sector impose large safety factors on modelling analyses to ensure components operate in their desired manner. Improving analysis accuracy has clear value of increasing the design space that could lead to greater efficiency and reliability.Novel materials for new reactor designs often exhibit non-linear behaviour; additionally material properties evolve due to in-service damage a combination that is difficult to model accurately. To better describe these complex behaviours a range of modelling techniques previously under-pursued due to computational expense are being developed.This work presents recent advancements in three techniques: Uncertainty quantification (UQ); Cellular automata finite element (CAFE); Image based finite element methods (IBFEM). Case studies are presented demonstrating their suitability for use in nuclear engineering made possible by advancements in parallel computing hardware that is projected to be available for industry within the next decade costing of the order of $100k. |
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| Keywords: |
high-performance computing, nuclear, materials characterisation, finite element analysis, image-based modelling, uncertainty quantification, cellular automata |
| College: |
Faculty of Science and Engineering |
| Issue: |
2 |
| Start Page: |
215 |
| End Page: |
236 |