Sockeye: A One-Dimensional, Two-Phase, Compressible Flow Heat Pipe Application

Hansel, Joshua; Berry, Ray A.; Andrš, David; Kunick, Matthias; Martineau, Richard

doi:10.1080/00295450.2020.1861879

Cited by 39 publications

(19 citation statements)

References 37 publications

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“…A suite of applications supporting various domains of nuclear reactor systems analysis is under active development at Idaho National Laboratory. THM provides a foundation for thermal hydraulic applications in this area, including RELAP-7 (Berry et al, 2018), which models two-phase flow in light-water reactors and coolant flow in gas-cooled reactors, and Sockeye (Hansel et al, 2021), which models heat pipes used in nuclear microreactors.…”

Section: Statement Of Needmentioning

confidence: 99%

The MOOSE Thermal Hydraulics Module

Hansel,

Andrs,

Charlot

et al. 2024

JOSS

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The Multiphysics Object-Oriented Simulation Environment (MOOSE) is an open-source objectoriented finite element framework written in C++ (Lindsay et al., 2022). The Thermal Hydraulics Module (THM) is an optional MOOSE physics module that provides capabilities for studying thermal hydraulic systems. Its core capability lies in assembling a network of coupled components, for instance, pipes, junctions, and valves.THM provides several new systems to MOOSE to enable and facilitate thermal hydraulic simulations, most notably the Components system, which provides a higher-level syntax to MOOSE's lower-level objects. This system is extensible by the user, but the current library primarily includes components based on a one-dimensional, single-phase, variable-area, compressible flow model, as well as heat conduction.

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Section: Statement Of Needmentioning

confidence: 99%

The MOOSE Thermal Hydraulics Module

Hansel,

Andrs,

Charlot

et al. 2024

JOSS

Self Cite

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“…As evident in the chart, the new examples demonstrate a variety of NEAMS applications and physics phenomena: MOOSE (open source physics modules and framework), Griffin [4] (reactor physics), Pronghorn [5] (engineering scale fluids), SAM [6] (systems scale fluids), Nek5000 [7] (computational fluid dynamics), Sockeye [8] (heat pipe analysis), Bison [9] (fuel performance and heat conduction), and Cardinal [10] (multiphysics application coupling the NekRS CFD solver [11] to MOOSE).…”

Section: Neams Models Recently Added To the Virtual Test Bedmentioning

confidence: 99%

NEAMS Advanced Reactor Model Contributions to the NRIC Virtual Test Bed

Shemon

Giudicelli

Abou-Jaoude

2022

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The U.S. Department of Energy (DOE) Office of Nuclear Energy's Advanced Modeling and Simulation (NEAMS) Program develops models of advanced reactor phenomena to demonstrate code applicability to challenging physics problems, drive code development through user assessment, and perform code verification and validation. Meanwhile, the U.S. DOE's National Reactor Innovation Center (NRIC) hosts an open-source website and associated GitHub repository called the Virtual Test Bed (VTB) on which computational models for advanced reactors are documented and shared with the reactor community.Several NEAMS advanced reactor models (including input files, documentation, and discussion of results) have recently been contributed to the NRIC VTB. The open sharing of these models benefits the reactor community by providing "best practice" examples using NEAMS tools for advanced reactor physics problems. This report summarizes and provides links to these new models, which include modeling phenomena important to liquid metal cooled fast reactors, molten salt reactors, high temperature gas-cooled reactors, and microreactors. This document is an overview report of NEAMS activities that have recently contributed to the VTB. Details of individual work done by the modeling teams have been or will be published in separate reports.

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“…Heat-pipe simulation offers the ability to accurately predict heat transfer for applications involving heat pipes, including heat-pipe-cooled microreactors. Additionally, it provides insight on heat-pipe performance; operational heat-pipe limits can be predicted in transient conditions and with greater flexibility than steady-state analyses can provide [9].…”

Section: Modeling and Simulation Statusmentioning

confidence: 99%