TY - JOUR

T1 - A comparison of uncertainty propagation techniques using NDaST - full, half or zero Monte Carlo?

AU - Dyrda, James

AU - Hill, Ian

AU - Fiorito, Luca

AU - Cabellos, Oscar

AU - Soppera, Nicolas

N1 - Score=10

PY - 2018/5/6

Y1 - 2018/5/6

N2 - 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 formula’ type propagation of uncertainty onto the selected benchmarks.
This allows us to draw some broad conclusions about the relative merits of selecting a technique with either full, half or zero degree of Monte Carlo simulation

AB - 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 formula’ type propagation of uncertainty onto the selected benchmarks.
This allows us to draw some broad conclusions about the relative merits of selecting a technique with either full, half or zero degree of Monte Carlo simulation

KW - Quantification

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/34638912

U2 - 10.1051/epjn/2018016

DO - 10.1051/epjn/2018016

M3 - Article

SN - 2491-9292

VL - 4

SP - 1

EP - 8

JO - EPJ Nuclear Sciences and Technologies

JF - EPJ Nuclear Sciences and Technologies

M1 - 14

ER -