On using computational versus data-driven methods for uncertainty propagation of isotopic uncertainties

Majdi I. Radaideh, Dean Price, Tomasz Kozlowski

Research output: Contribution to journalArticlepeer-review


This work presents two different methods for quantifying and propagating the uncertainty associated with fuel composition at end of life for cask criticality calculations. The first approach, the computational approach uses parametric uncertainty including those associated with nuclear data, fuel geometry, material composition, and plant operation to perform forward depletion on Monte-Carlo sampled inputs. These uncertainties are based on experimental and prior experience in criticality safety. The second approach, the data-driven approach relies on using radiochemcial assay data to derive code bias information. The code bias data is used to perturb the isotopic inventory in the data-driven approach. For both approaches, the uncertainty in keff for the cask is propagated by performing forward criticality calculations on sampled inputs using the distributions obtained from each approach. It is found that the data driven approach yielded a higher uncertainty than the computational approach by about 500 pcm. An exploration is also done to see if considering correlation between isotopes at end of life affects keff uncertainty, and the results demonstrate an effect of about 100 pcm.

Original languageEnglish (US)
Pages (from-to)1148-1155
Number of pages8
JournalNuclear Engineering and Technology
Issue number6
StatePublished - Jun 2020


  • Criticality safety
  • Isotopic composition
  • Radiochemcial assay data
  • Uncertainty quantification

ASJC Scopus subject areas

  • Nuclear Energy and Engineering

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