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Record W4385602904 · doi:10.1115/1.4063112

Development of Analysis Tools for Heat Pipes Used in Small Modular Reactors: Sodium Property Correlations

2023· article· en· W4385602904 on OpenAlexaff
T. Beuthe, Aleksandar Vasić, Chukwudi Azih, Pablo Diaz Gomez Maqueo

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2023
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Structural Properties of Metals and Alloys
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsSodium-cooled fast reactorModular designNuclear engineeringHeat pipeMaterials scienceMechanical engineeringComputer scienceHeat transferMechanicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Advanced small modular reactors strive to improve reactor safety through increased utilization of passive heat transport and safety systems. An innovative means of meeting this design goal is to use alkali metal heat pipes to cool the reactor under both normal and abnormal operating conditions. A heat pipe model has been added to the ARIANT thermalhydraulic code to enable reactor modeling and support the design and licensing of new reactors. Saturation fluid properties are a fundamental input to this model. Consequently, this article provides a comprehensive comparative overview of the best available sodium saturation property correlations developed over the past century. The results show most of the sodium property correlations needed to model a heat pipe are relatively well-defined and recommendations for their use can be provided.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.218
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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