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

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

Research output: Chapter in Book/Report/Conference proceedingChapter

Original languageEnglish
Title of host publicationHandbook of Smart Energy Systems
Subtitle of host publicationVolume 1-4
PublisherSpringer International Publishing
Pages2265-2277
Number of pages13
Volume1-4
ISBN (Electronic)9783030979409
ISBN (Print)9783030979393
DOIs
StatePublished - Jan 1 2023

ASJC Scopus Subject Areas

  • General Economics,Econometrics and Finance
  • General Business,Management and Accounting
  • General Mathematics
  • General Environmental Science
  • General Energy
  • General Engineering

Keywords

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

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