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An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics

Helin Gong Orcid Logo, Sibo Cheng Orcid Logo, Zhang Chen, Qing Li, César Quilodrán-Casas, Dunhui Xiao Orcid Logo, Rossella Arcucci

Annals of Nuclear Energy, Volume: 179, Start page: 109431

Swansea University Author: Dunhui Xiao Orcid Logo

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DOI (Published version): 10.1016/j.anucene.2022.109431

Published in: Annals of Nuclear Energy
ISSN: 0306-4549
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa61228
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spelling v2 61228 2022-09-15 An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics 62c69b98cbcdc9142622d4f398fdab97 0000-0003-2461-523X Dunhui Xiao Dunhui Xiao true false 2022-09-15 ACEM Journal Article Annals of Nuclear Energy 179 109431 Elsevier BV 0306-4549 Operational digital twins; Machine learning; Latent assimilation; SVD-autoencoder; Nuclear reactor physics 15 12 2022 2022-12-15 10.1016/j.anucene.2022.109431 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University This work is supported by the National Natural Science Foundation of China (Grant No. 11905216, Grant No. 12175220), and the Stability Support Fund for National Defence and Science and Technology on Reactor System Design Technology Laboratory. This research is partially funded by the Leverhulme Centre for Wildfires, Environment and Society through the Leverhulme Trust, Grant No. RC-2018-023. This work is partially supported by the EP/T000414/1 PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE). 2024-07-17T12:10:12.1024119 2022-09-15T09:41:11.9759667 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Helin Gong 0000-0002-4094-6795 1 Sibo Cheng 0000-0002-8707-2589 2 Zhang Chen 3 Qing Li 4 César Quilodrán-Casas 5 Dunhui Xiao 0000-0003-2461-523X 6 Rossella Arcucci 7 61228__25264__1bd26a6e429348b2bc518f7a731d763d.pdf 61228.pdf 2022-09-29T15:30:59.8877382 Output 1624400 application/pdf Accepted Manuscript true 2023-09-10T00:00:00.0000000 ©2022 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
spellingShingle An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
Dunhui Xiao
title_short An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
title_full An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
title_fullStr An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
title_full_unstemmed An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
title_sort An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics
author_id_str_mv 62c69b98cbcdc9142622d4f398fdab97
author_id_fullname_str_mv 62c69b98cbcdc9142622d4f398fdab97_***_Dunhui Xiao
author Dunhui Xiao
author2 Helin Gong
Sibo Cheng
Zhang Chen
Qing Li
César Quilodrán-Casas
Dunhui Xiao
Rossella Arcucci
format Journal article
container_title Annals of Nuclear Energy
container_volume 179
container_start_page 109431
publishDate 2022
institution Swansea University
issn 0306-4549
doi_str_mv 10.1016/j.anucene.2022.109431
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 1
active_str 0
published_date 2022-12-15T12:10:11Z
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score 11.036706

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