Uncertainty Quantification and Sensitivity Analysis for Digital Twin Enabling Technology

Kazuma Kobayashi, Dinesh Kumar, Matthew Bonney, Souvik Chakraborty, Kyle Paaren, Shoaib Usman and Syed Alam ()
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Kazuma Kobayashi: Missouri University of Science and Technology
Dinesh Kumar: University of Bristol
Matthew Bonney: University of Sheffield
Souvik Chakraborty: Indian Institute of Technology Delhi
Kyle Paaren: Idaho National Laboratory
Shoaib Usman: Missouri University of Science and Technology
Syed Alam: Missouri University of Science and Technology

A chapter in Handbook of Smart Energy Systems, 2023, pp 2265-2277 from Springer

Abstract: 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.

Keywords: Machine Learning; Uncertainty quantification; Sensitivity analysis; Nuclear power system; Fuel performance code; BISON (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_205

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DOI: 10.1007/978-3-030-97940-9_205

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