Article Dans Une Revue Journal of Computational Physics Année : 2022

Efficient uncertain keff computations with the Monte Carlo resolution of generalised Polynomial Chaos based reduced models

Résumé

In this paper, we are interested in taking into account uncertainties for keff computations in neutronics. More generally, the material of this paper can be applied to propagate uncertainties in eigenvalue/eigenvector computations for the linear Boltzmann equation. In [1], [2], an intrusive MC solver for the gPC based reduced model of the instationary linear Boltzmann equation has been put forward. The MC-gPC solver presents interesting characteristics (mainly a better efficiency than non-intrusive strategies and spectral convergence): our aim is to recover these characteristics in an eigenvalue/eigenvector estimation context. This is done in practice at the price of few well identified modifications of an existing Monte Carlo implementation.
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Dates et versions

cea-04761562 , version 1 (31-10-2024)

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Gaël Poëtte, Emeric Brun. Efficient uncertain keff computations with the Monte Carlo resolution of generalised Polynomial Chaos based reduced models. Journal of Computational Physics, 2022, 456, pp.111007. ⟨10.1016/j.jcp.2022.111007⟩. ⟨cea-04761562⟩
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