XGPT: Extending Monte Carlo Generalized Perturbation Theory capabilities to continuous-energy sensitivity functions

Abstract

a b s t r a c tThe XGPT method extends the Generalized Perturbation Theory capabilities of Monte Carlo codes to continuous-energy sensitivity functions. In this work, this method is proposed as a new approach to nuclear data uncertainty propagation. XGPT overcomes some of the limitations of legacy perturbationbased approaches. In particular, it allows the nuclear data uncertainty propagation to be performed adopting continuous energy covariance matrices, instead of discretized (multi-group) data. The XGPT capabilities are demonstrated in three simple fast criticality benchmarks for 239 Pu and 208 Pb cross section uncertainties. The new method is also applied in selected cases to estimate higher moments of the k eff distribution, starting from TENDL random evaluations. The XGPT estimates, when compared against reference Total Monte Carlo (TMC) results, show a good agreement and a significant reduction in computational requirements with respect to the TMC approach. Finally, the capabilities for uncertainty propagation involving adjoint-weighted response functions are demonstrated.

Correlationν̅p − σ − χin the fast neutron range via integral information

“…They have already been used and characterized in previous work, see for instance references [2,3,26]. All possible nuclear data are varied, such as cross sections, angular distributions, emitted spectra and multiplicities.…”

“…Many simulation tools are capable of using such matrices to propagate nuclear data uncertainties on final quantities, with either perturbation theories [1][2][3][4][5][6], or Monte Carlo sampling [3,[7][8][9][10][11][12][13]. These results can for instance be used for the review procedure of new facilities, or during the safety assessment of new reactor core designs [14].…”

Development of a Reduced Order Model for Fuel Burnup Analysis

“…Indeed, POD was introduced to study the coherent structures in experimental turbulent flows. In the nuclear field (as in other fields of engineering), it has been shown that POD basis functions carry out physical information on distribution of scalar and energy group fluxes, fluid velocity distributions in thermal-hydraulics and cross section uncertainties [21][22][23][24]. For this reason, we expect that POD modes for burnup matrices and fluxes provide the main physical information on fuel burnup.…”

“…In the nuclear field, POD has been successfully applied to neutronics [21][22][23], thermal-hydraulics [24], sensitivity and uncertainty quantification [25,26]. Notwithstanding, there is a lack of literature in the employment in burnup calculations, nevertheless it would be necessary to solve the issue of computational burden previously discussed.…”

Eigenvalue sensitivity analysis capabilities with the differential operator method in the superhistory Monte Carlo method

“…Carlo method have been developed (Choi and Shim, 2016b;Qiu, et al, 2016a;Perfetti and Rearden, 2016;Aufiero et al, 2016), the present paper focuses on the sensitivity analysis of keff-eigenvalue. This paper scrutinizes the source perturbation effect on a sensitivity coefficient due to the change of nuclear data through the multi-group Monte Carlo calculations.…”