A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Author:

Kajihara TakanoriORCID,Bao HanORCID,Chapman Daniel B.ORCID,Qin SunmingORCID,Fleming Austin D.

Funder

Idaho National Laboratory

Publisher

Elsevier BV

Reference33 articles.

1. Development and assessment of a nearly autonomous management and control system for advanced reactors;Lin;Ann. Nucl. Energy,2021

2. Development and assessment of prognosis digital twin in a NAMAC system;Lin;Ann. Nucl. Energy,2022

3. Online autonomous calibration of digital twins using machine learning with application to nuclear power plants;Song;Appl. Energy,2022

4. V. Yadav, et al., “Technical challenges and gaps in digitaltwinenabling technologies for nuclear reactor applications,” Idaho national laboratory, Idaho falls, Idaho, 2021. https://www.nrc.gov/docs/ML2136/ML21361A261.pdf.

5. Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: a review;Lin;Ann. Nucl. Energy,2021

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