Persona:
Sauvan, Patrick

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0000-0002-9128-8817
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Sauvan
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Patrick
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2021 - 20242
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Autor: Alguacil Orejudo, Javier ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Construction of GVR weight windows maps from very low density transport simulations
    (Elsevier, 2024-05) Farga Niñoles, Gonzalo; Ogando Serrano, Francisco M.; Alguacil Orejudo, Javier; Sauvan, Patrick
    Fusion-related facilities present relevant neutron radiation fields even after penetrating through a considerable thickness of shielding material. Neutronic analyses performed via Monte Carlo codes, then, need Global Variance Reduction (GVR) techniques so that low statistical uncertainty is reached efficiently throughout the geometry. Mesh-based Weight Windows is a flexible methodology used extensively for variance reduction purposes, both for Local and Global Variance Reduction. Purely stochastic GVR methodologies based on Weight Windows usually construct weight maps so that they are proportional to the forward particle flux, which is unknown a priori. Therefore, an iterative cycle is established. In each iteration, a weight map is obtained from the forward flux that allows the next iteration to reach further into the geometry, until all of it is populated. However, this iterative cycle may take a considerable amount of computer time, as many iterations are needed to fully populate the geometry. An alternative to achieve relevant penetration in a single iteration is to perform calculations at very low densities. However, a reconstruction method is needed to estimate the flux at the real density. This work studies a scheme to reconstruct the fluxes from low density calculations and compares it to already existing techniques.
  • Miniatura
    Publicación
    Development of a methodology to estimate the statistical SDR uncertainty with R2S-UNED
    (ELSEVIER, 2021) Alguacil Orejudo, Javier; Catalán Pérez, Juan Pablo; Sanz Gozalo, Javier; Sauvan, Patrick; https://orcid.org/0000-0002-9128-8817
    The Rigorous-Two-Steps (R2S) is one of the most useful methods to estimate the Shutdown Dose Rate (SDR). The most advanced R2S tools couple neutron and photon transport, which are often simulated using Monte Carlo (MC) codes, through an activation simulation using mesh-based techniques to improve the spatial resolution of the neutron flux and the decay gamma source. One of the problems of the methodology is that the statistical uncertainty of the neutron flux due to the MC method used by the transport codes is not considered by most R2S implementations. Consequently, larger tolerance must be assumed affecting to the design of the nuclear facilities. This article describes a scheme allowing the calculation of the SDR statistical uncertainty without any additional assumptions than those used in the R2S methodology. The approach proposed in this article is suitable for cell- and mesh-based R2S implementations. In this work, the methodology was implemented in the R2S-UNED code. The accurate application of the methodology requires the full the neutron flux uncertainty (covariance matrix) as input data. MCNP was modified to calculate this matrix, although, it cannot be calculated for most of the realistic R2S simulations due to its size. If that is the situation, we propose a guideline to reduce the size of the covariance matrix to be calculated according to its element contribution to the SDR. When this guideline cannot be applied, the methodology still allows calculating the upper and lower SDR uncertainty bounds. In this article, the guideline is applied to the calculation of the SDR uncertainty in the computational benchmark of ITER. In addition, we also study the possible impact of the neutron flux correlation degree on the SDR uncertainty in this benchmark.