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.

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

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