Benchmark calculations of sensitivities to secondaries' angular distributions

U. Fischer*, I. Kodeli, R. L. Perel

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

A novel algorithm, based on the differential operator approach, for Monte Carlo calculation of sensitivities to secondaries' angular distributions has recently been developed. It has been implemented in a local version of the MCNP Monte-Carlo code. The quantity calculated is the sensitivity of a response such as the neutron leakage flux to possible changes in Legendre moments of the angular distribution of the neutrons scattered by a given reaction. In the current work, this method, and its implementation, has been validated through the analysis of a fusion-relevant benchmark on an iron spherical shell assembly with a 14 MeV neutron source in the centre. The sensitivities of the neutron leakage flux to the angular distribution of the elastic scattering by 56Fe have been calculated with the Monte Carlo algorithm as well as with the well-known sensitivity algorithm that uses the direct and adjoint fluxes, which are calculated using the discrete ordinates method. Additional estimates of sensitivities were obtained by calculations that were performed - both with Monte-Carlo or discrete ordinates methods - using 56Fe cross-sections having a selectively perturbed elastic-scattering angular distribution. The various methods yielded consistent sensitivities. Thus, The Monte-Carlo method for calculation of sensitivities to secondaries' angular distributions, and its implementation, has been validated.

Original languageAmerican English
Pages1773-1788
Number of pages16
StatePublished - 2005
EventMonte Carlo 2005 Topical Meeting - Chattanooga, TN, United States
Duration: 17 Apr 200521 Apr 2005

Conference

ConferenceMonte Carlo 2005 Topical Meeting
Country/TerritoryUnited States
CityChattanooga, TN
Period17/04/0521/04/05

Keywords

  • Angular distribution
  • Cross section sensitivity and uncertainty analysis
  • Discrete ordinates
  • Monte Carlo

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