Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics-Reference-Cited by-同舟云学术

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Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics

Published:2022-02-28 Issue:6 Volume:196 Page:668-693
ISSN:0029-5639
Container-title:Nuclear Science and Engineering
language:en
Short-container-title:Nuclear Science and Engineering

Author:

Gong Helin¹^ORCID,Cheng Sibo²³,Chen Zhang¹,Li Qing¹

Affiliation:

1. Nuclear Power Institute of China, Science and Technology on Reactor System Design Technology Laboratory, 610041 Chengdu, China

2. Imperial College London, Data Science Institute, Department of Computing, London SW7 2BU, United Kingdom

3. EDF R&D Saclay, 91120 Palaiseau, France

Funder

National Natural Science Foundation of China

Leverhulme Centre for Wildfires, Environment and Society through the Leverhulme Trust

Publisher

Informa UK Limited

Subject

Nuclear Energy and Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/00295639.2021.2014752

Reference105 articles.

1. Engine Fleet-Management: The Use of Digital Twins From a MRO Perspective

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1. Research on the connection calculation of direct kinetics simulation and predictor-corrector Quasi-Static method based on RMC code;Annals of Nuclear Energy;2024-12

2. Mode decomposition of core dynamics transients using higher-order DMD method;Nuclear Engineering and Design;2024-10

3. Hybrid vibro-acoustic model reduction for model updating in nuclear power plant pipeline with undetermined boundary conditions;Nuclear Engineering and Technology;2024-09

4. The time for revolutionizing small modular reactors: Cost reduction strategies from innovations in operation and maintenance;Progress in Nuclear Energy;2024-09

5. Evaluation of digital twin synchronization in robotic assembly using YOLOv8;The International Journal of Advanced Manufacturing Technology;2024-07-29

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