Uncertainty Quantification and Sensitivity Analysis for Digital Twin Enabling Technology: Application for Bison Fuel Performance Code
Abstract
As US Nuclear Regulatory Committee (NRC) recently announced machine learning (ML) and artificial intelligence (AI) will be the main research topics in the nuclear industry. One of the applications is the development of new nuclear fuels using digital twin technology, in which machine learning-Based data analysis methods will significantly contribute to accelerate developments. This chapter introduces the ML-Based uncertainty quantification and sensitivity analysis methods and shows its actual application to nuclear fuel development codes: A finite element-Based nuclear fuel performance code BISON.
Recommended Citation
K. Kobayashi et al., "Uncertainty Quantification and Sensitivity Analysis for Digital Twin Enabling Technology: Application for Bison Fuel Performance Code," Handbook of Smart Energy Systems: Volume 1-4, vol. 1 thru 4, pp. 2265 - 2277, Springer, Jan 2023.
The definitive version is available at https://doi.org/10.1007/978-3-030-97940-9_205
Department(s)
Nuclear Engineering and Radiation Science
Keywords and Phrases
BISON; Fuel performance code; Machine Learning; Nuclear power system; Sensitivity analysis; Uncertainty quantification
International Standard Book Number (ISBN)
978-303097940-9;978-303097939-3
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Springer, All rights reserved.
Publication Date
01 Jan 2023