Improving the variance in Monte Carlo criticality calculations with adaptative multilevel splitting

Kévin Fröhlicher (1) , Eric Dumonteil (2) , Loic Thulliez (2) , Julien Taforeau (1) , Mariya Brovchenko (1)

1 IRSN/PSN-RES/SNC/LN - Laboratoire de Neutronique
2 IRFU - Institut de Recherches sur les lois Fondamentales de l'Univers

This paper proposes a new iterative algorithm for Monte Carlo criticality calculations based on the Adaptative Multilevel Splitting (AMS) algorithm initially developed as a variance reduction technique. The objective is to modify the AMS and use it in place of the standard Power Iteration technique, aiming at reducing the variance, the bias, the spatial and cycle-to-cycle correlations. As a first step towards proof of concept, the method has been tested on one-dimensional homogeneous rods of different sizes, in a mono-energetic framework. The preliminary results presented here suggest that the method could provide promising improvements regarding cycle-to-cycle and spatial correlations, and thus reducing clustering. The AMS combined with branchless collision allowed for an increase in the Figure of Merit by a factor 63 for the keff, and by three order of magnitude on average for the spatial flux (from 4 to 105) on a 80 cm rod.

Mots clés: Monte Carlo k-eigenvalue correlations clustering Adaptative Multilevel Splitting; Monte Carlo; k-eigenvalue; correlations; clustering; Adaptative Multilevel Splitting

Domaines: Physique [physics]

Kévin Fröhlicher, Eric Dumonteil, Loic Thulliez, Julien Taforeau, Mariya Brovchenko. Improving the variance in Monte Carlo criticality calculations with adaptative multilevel splitting. PHYSOR 2022 - International Conference on the Physics of Reactors, American Nuclear Society (ANS), May 2022, Pittsburgh, United States. ⟨irsn-03948886⟩