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Record W4205754341 · doi:10.1115/1.4053632

Thermophysical Characteristics of Liquid Metal In-Bi-Sn Eutectic (Field's Metal) as a Similarity Coolant

2022· article· en· W4205754341 on OpenAlexafffund
Adam Lipchitz, Glenn Harvel, Takeyoshi Sunagawa

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2022
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceEutectic systemLiquid metalMelting pointThermodynamicsThermal conductivityFusible alloyCoolantCritical point (mathematics)BismuthWork (physics)Newtonian fluidAlloyMetallurgyComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract This work investigated the thermophysical characteristics of liquid indium–bismuth–tin eutectic alloy also known as Field's Metal for the purposes of use as a similar fluid for liquid metal reactors. The density, specific heat capacity, viscosity, thermal conductivity, and the coefficient of thermal expansion were determined for liquid Field's Metal for temperature ranges from its melting point 333 K to 423 K. The work captured the effect of temperature on these properties and each property's magnitude. The findings were used to create mathematical correlations to predict the value of the thermophysical property at a specified temperature for use in natural circulation studies. Notably, the work also observed non-Newtonian shear thinning behavior of the alloy near the melting point and that the non-Newtonian behavior relaxes as the material obtains more energy. The results are consistent with the behavior of other liquid metals including variances that occur close to the melting point.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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 designBench or experimental
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

Citations22
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Nuclear Engineering and Radiation ScienceSame topicMetallurgical Processes and ThermodynamicsFrench-language works237,207