Uncertainty Quantification and Sensitivity Analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code

Kazuma Kobayashi; Dinesh Kumar; Matthew Bonney; Souvik Chakraborty; Kyle Paaren; Shoaib Usman; Syed Alam

doi:10.1007/978-3-030-72322-4_205-1

Uncertainty Quantification and Sensitivity Analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code

Kazuma Kobayashi, Dinesh Kumar, Matthew Bonney, Souvik Chakraborty, Kyle Paaren, Shoaib Usman, Syed Alam

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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.

Original language	English (US)
Title of host publication	Handbook of Smart Energy Systems
Editors	Michel Fathi, Enrico Zio, Panos M. Pardalos
Publisher	Springer
Chapter	205-1
Pages	1-13
ISBN (Electronic)	978-3-030-72322-4
DOIs	https://doi.org/10.1007/978-3-030-72322-4_205-1
State	Published - Jan 11 2023
Externally published	Yes

Keywords

Machine Learning
BISON
Fuel performance code
Nuclear power system
Sensitivity analysis
Uncertainty quantification

Online availability

10.1007/978-3-030-72322-4_205-1

Library availability

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Cite this

Kobayashi, K., Kumar, D., Bonney, M., Chakraborty, S., Paaren, K., Usman, S., & Alam, S. (2023). Uncertainty Quantification and Sensitivity Analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code. In M. Fathi, E. Zio, & P. M. Pardalos (Eds.), Handbook of Smart Energy Systems (pp. 1-13). Springer. https://doi.org/10.1007/978-3-030-72322-4_205-1

Uncertainty Quantification and Sensitivity Analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code. / Kobayashi, Kazuma; Kumar, Dinesh; Bonney, Matthew et al.
Handbook of Smart Energy Systems. ed. / Michel Fathi; Enrico Zio; Panos M. Pardalos. Springer, 2023. p. 1-13.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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AU - Kobayashi, Kazuma

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KW - Sensitivity analysis

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ER -

Uncertainty Quantification and Sensitivity Analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code

Abstract

Keywords

Online availability

Library availability

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