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Accelerating Monte Carlo particle transport with adaptively generated importance maps

Michel Nowak 1
1 LTSD - Laboratoire de Transport Stochastique et Déterministe
SERMA - Service des Réacteurs et de Mathématiques Appliquées : DEN/DM2S/SERMA
Abstract : Monte Carlo methods are a reference asset for the study of radiation transport in shielding problems. Their use naturally implies the sampling of rare events and needs to be tackled with variance reduction methods. These methods require the definition of an importance function/map. The aim of this study is to propose an adaptivestrategy for the generation of such importance maps during the Montne Carlo simulation. The work was performed within TRIPOLI-4®, a Monte Carlo transport code developped at the nuclear energy division of CEA in Saclay, France. The core of this PhD thesis is the implementation of a forward-weighted adjoint score that relies on the trajectories sampled with Adaptive Multilevel Splitting, a robust variance reduction method. It was validated with the integration of a deterministic module in TRIPOLI-4®. Three strategies were proposed for the reintegrationof this score as an importance map and accelerations were observed. Two of these strategies assess the convergence of the adjoint score during exploitation phases by evalutating the figure of merit yielded by the use of the current adjoint score. Finally, the smoothing of the importance map with machine learning algorithms concludes this work with a special focus on Kernel Density Estimators.
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Michel Nowak. Accelerating Monte Carlo particle transport with adaptively generated importance maps. Computational Physics [physics.comp-ph]. Université Paris-Saclay, 2018. English. ⟨NNT : 2018SACLS403⟩. ⟨tel-02135102⟩

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