Calculating uncertainty on k-effective with monk10

Christopher Baker, Paul N. Smith, Robert Mason, Max Shepherd, Simon Richards, Richard Hiles, Ray Perry, Dave Hanlon, Geoff Dobson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


Criticality safety assessments require a demonstration that a particular configuration of fissile material has an adequate sub-critical margin (k-effective sufficiently below unity) to ensure that the risk of criticality under normal operation and accident conditions is acceptable. The required sub-critical margin depends upon the uncertainty in the estimated value of k-effective. The uncertainty in the calculated value of keffective arises from a number of sources, including: manufacturing tolerances on input data to the code (affecting geometry, compositions and densities), uncertainty in the nuclear data used by the code, stochastic uncertainty resulting from Monte Carlo simulation and modelling approximations/errors, including the inevitable bugs in the software. The ANSWERS Software Service, in collaboration with industrial partners, is developing a number of techniques to better understand and quantify uncertainty on predicted values of k-effective, using MONK. The SPRUCE utility code has been developed to allow uncertainty to be estimated using sampling methods. This can include the sampling of input parameters (including dimensions, compositions and densities) from statistical distributions. It can also include sampling different nuclear data libraries. A set of nuclear data libraries has been generated for this purpose by sampling from statistical distributions that represent the uncertainties in the published nuclear data evaluated files; a set of libraries has been produced for Latin Hypercube Sampling. By varying the input data and nuclear data, separate and combined uncertainties due to manufacturing tolerances and nuclear data can be derived. By performing least squares fitting on the results it is also possible to estimate the contribution of each of the uncertain inputs and a sensitivity method in MONK can break down the nuclear data uncertainty.

Original languageEnglish
Title of host publicationICNC 2015 - International Conference on Nuclear Criticality Safety
PublisherAmerican Nuclear Society
Number of pages10
ISBN (Electronic)9780894487231
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event2015 International Conference on Nuclear Criticality Safety, ICNC 2015 - Charlotte, United States
Duration: 13 Sept 201517 Sept 2015


Conference2015 International Conference on Nuclear Criticality Safety, ICNC 2015
Country/TerritoryUnited States


  • Criticality
  • Monte Carlo
  • Sensitivity analysis
  • Uncertainty quantification


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