| Issue |
EPJ Web Conf.
Volume 247, 2021
PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|
|
|---|---|---|
| Article Number | 06028 | |
| Number of page(s) | 8 | |
| Section | Advanced Modelling and Simulation | |
| DOI | https://doi.org/10.1051/epjconf/202124706028 | |
| Published online | 22 February 2021 | |
https://doi.org/10.1051/epjconf/202124706028
QUANTUM ANNEALING OPTIMIZATION OF A HEURISTIC SURROGATE MODEL FOR PWR FUEL LOADING
University of Cambridge Department of Engineering, Trumpington Street, Cambridge CB2 1PZ
ajw287@cam.ac.uk
gtp10@cam.ac.uk
Published online: 22 February 2021
An efficient fuel arrangement must be generated by PWR operators every 6–18 months. This complex problem has been extensively researched with two broad approaches, heuristic and stochastic methods, becoming accepted. This initial study qualitatively introduces the concept of encoding full-core PWR fuel loading patterns in a form suitable for quantum annealing. The concepts of adiabatic quantum computers and quantum annealing are introduced, and a surrogate model encoding of a set of heuristics for loading pattern design produced in a form suitable for use in present-day quantum annealers. The simulated results show significant similarity to benchmark loading patterns.
Key words: quantum computing / optimization / nuclear fuel management / loading pattern / PWR
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.