tailieunhanh - A comparison of uncertainty propagation techniques using NDaST: full, half or zero Monte Carlo?

Instead of running hundreds or thousands of neutronics calculations, we therefore investigate the possibility to take those many cross-section file samples and perform ‘cheap’ sensitivity perturbation calculations. This is efficiently possible with the NEA Nuclear Data Sensitivity Tool (NDaST) and this process we name the half Monte Carlo method (HMM). | A comparison of uncertainty propagation techniques using NDaST full half or zero Monte Carlo EPJ Nuclear Sci. Technol. 4 14 2018 Nuclear Sciences J. Dyrda et al. published by EDP Sciences 2018 amp Technologies https epjn 2018016 Available online at https REGULAR ARTICLE A comparison of uncertainty propagation techniques using NDaST full half or zero Monte Carlo James Dyrda Ian Hill Luca Fiorito Oscar Cabellos and Nicolas Soppera OECD Nuclear Energy Agency Boulogne-Billancourt France Received 23 October 2017 Received in final form 18 January 2018 Accepted 4 May 2018 Abstract. Uncertainty propagation to keff using a Total Monte Carlo sampling process is commonly used to solve the issues associated with non-linear dependencies and non-Gaussian nuclear parameter distributions. We suggest that in general keff sensitivities to nuclear data perturbations are not problematic and that they remain linear over a large range the same cannot be said definitively for nuclear data parameters and their impact on final cross-sections and distributions. Instead of running hundreds or thousands of neutronics calculations we therefore investigate the possibility to take those many cross-section file samples and perform cheap sensitivity perturbation calculations. This is efficiently possible with the NEA Nuclear Data Sensitivity Tool NDaST and this process we name the half Monte Carlo method HMM . We demonstrate that this is indeed possible with a test example of JEZEBEL PMF001 drawn from the ICSBEP handbook comparing keff directly calculated with SERPENT to those predicted with NDaST. Furthermore we show that one may retain the normal NDaST benefits a deeper analysis of the resultant effects in terms of reaction and energy breakdown without the normal computational burden of Monte Carlo results within minutes rather than days . Finally we assess the rationality of using either full or HMMs by also using the covariance data to do simple linear sandwich .