Multiscale thermal-hydraulic modeling of the pebble bed fluoride-salt-cooled high-temperature reactor
•Pronghorn’s multiscale thermal-hydraulic models are applied to the Pebble Bed.•Fluoride-Salt-Cooled High-Temperature Reactor (PB-FHR).•The Heat Source Decomposition method is compared against fully-resolved pebble heat conduction.•Anisotropic drag models are correlated with COMSOL for the outer ref...
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Veröffentlicht in: | Annals of nuclear energy 2021-05, Vol.154 (C), p.107968, Article 107968 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •Pronghorn’s multiscale thermal-hydraulic models are applied to the Pebble Bed.•Fluoride-Salt-Cooled High-Temperature Reactor (PB-FHR).•The Heat Source Decomposition method is compared against fully-resolved pebble heat conduction.•Anisotropic drag models are correlated with COMSOL for the outer reflector blocks.•Predictions are made for the bypass fraction and core thermal-hydraulic characteristics at nominal steady-state conditions.
The complex core geometry of Pebble Bed Reactors (PBRs) necessitates multiscale techniques for fast-turnaround design and analysis. This paper describes the multiscale model implemented in the Pronghorn PBR simulation tool and demonstrates application to steady-state analysis of the Mark-1 Pebble Bed Fluoride-Salt-Cooled High-Temperature Reactor (PB-FHR). Verification of the pebble model with fully-resolved heat conduction shows that material-wise pebble temperatures are predicted to within 10°C over a wide range in thermal conditions. Anisotropic drag models are correlated for the outer reflector blocks using COMSOL, providing closures for modeling of bypass flows. With a porous media model of the outer reflectors, the core bypass fraction and fuel, reflector, and structural material temperatures are predicted for a number of different inflow conditions. This work demonstrates the full-core analysis capabilities of the Pronghorn application and enables comprehensive reactor analysis with the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. |
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ISSN: | 0306-4549 1873-2100 |
DOI: | 10.1016/j.anucene.2020.107968 |