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CFD modelling of particle resuspension in a toroidal geometry resulting from airflows during a loss of vacuum accident (LOVA)

2019; Elsevier BV; Volume: 151; Linguagem: Inglês

10.1016/j.fusengdes.2019.111386

1873-7196

Thomas Gélain, F. Gensdarmes, S. Peillon, Laurent Ricciardi,

Particle Dynamics in Fluid Flows

During a loss of vacuum accident (LOVA), dusts that will be present in the future tokamak ITER are likely to be resuspended. Such dusts may present a risk of explosion and airborne contamination. Hence, an assessment of the amount of particles which may be resuspended in case of LOVA is a major safety issue in ITER and more widely for all the fusion reactors. This article presents the implementation of the Rock'n'Roll model for the particle resuspension in ANSYS CFX and its coupling with a validated particles transport and deposition model. After reminding the main phenomena contributing to the particle resuspension and their equations, this article proposes in a first time a checking of the good implementation of the resuspension model in ANSYS CFX. To do that, numerical results are compared to analytical calculations and experimental results acquired on facilities dedicated to the study of the resuspension. After this first step, this article proposes an application for a LOVA scenario in a toroidal vessel in order to demonstrate the feasibility of this kind of calculation in conditions similar to those encountered in a tokamak type geometry and the robustness of the coupling between the particle transport model and the resuspension model. The final application for modelling a LOVA in a toroidal geometry with different particle sizes shows a good behaviour of the coupled resuspension and transport models and a good agreement of the results with the expected physics. However, it must be reminded that this numerical application is a first step towards the analysis of dust resuspension and transport in a tokamak like ITER and the interpretation of quantitative results presented here should remain in this specific context.