Study of a fine-mesh 1:1 Computational Fluid Dynamics – Monte Carlo neutron transport coupling method with discretization uncertainty estimation

•Novel Fine-mesh 1:1 CFD and Monte Carlo coupled simulation procedure.•GCI method applied for uncertainty estimation of coupled calculations.•Noise reduction process is necessary to produce smooth Monte Carlo power profiles.•CFD calculations are more mesh demanding than Monte Carlo simulations. In t...

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Veröffentlicht in:Annals of nuclear energy 2020-12, Vol.148, p.107718, Article 107718
Hauptverfasser: Vieira, Tiago Augusto Santiago, Barros, Graiciany P., Campolina, Daniel, Vasconcelos, Vitor, Campagnole dos Santos, André A.
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Sprache:eng
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Zusammenfassung:•Novel Fine-mesh 1:1 CFD and Monte Carlo coupled simulation procedure.•GCI method applied for uncertainty estimation of coupled calculations.•Noise reduction process is necessary to produce smooth Monte Carlo power profiles.•CFD calculations are more mesh demanding than Monte Carlo simulations. In this work, a fine-mesh 1:1 Computational Fluid Dynamics (CFD) – Monte Carlo Neutron Transport (MC) coupled calculations for a IPR-R1 TRIGA MARK I fuel pin was performed. The proposed methodology allowed the simulation of the fuel pin behavior in steady-state for different meshes and the evaluation of the discretization uncertainty. Comparison between mesh-based and Constructive Solid Geometry (CSG) MC simulations was performed. An extended Grid Convergence Index (GCI) method was applied to quantify numerical discretization uncertainty for keff, temperature and power density profiles. Serpent Nuclear Code was used for neutronics simulations, while OpenFOAM was used for thermal–hydraulics calculations. The proposed coupling method was proved convergent. This coupling method did not produce a smooth power density profiles. In order to achieve high-quality profiles, neutronic noise reduction procedure was performed. The results were encouraging, nevertheless, the methodology should be employed on the study of problems with increased complexity for further assessment.
ISSN:0306-4549
1873-2100
DOI:10.1016/j.anucene.2020.107718