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Record W2316454527 · doi:10.1115/icone22-30122

Study on Specifics of Forced-Convective Heat Transfer in Supercritical Carbon Dioxide

2014· article· en· W2316454527 on OpenAlexafffundabout
Eugene Saltanov, Igor Pioro, David Mann, Sahil Gupta, Sarah Mokry, Glenn Harvel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsOntario Tech University
FundersAtomic Energy of Canada LimitedKorea Atomic Energy Research Institute
KeywordsSupercritical fluidCoolantHeat transferNuclear engineeringHeat transfer coefficientEnergy transferSupercritical carbon dioxideEnvironmental scienceMaterials scienceThermodynamicsMechanical engineeringEngineeringPhysicsEngineering physics

Abstract

fetched live from OpenAlex

The appropriate description of heat-transfer to coolants at supercritical state is limited by the current understanding. Thus, poses one of the main challenges in development of supercritical-fluids applications for the Generation–IV reactors. The objective of the paper is, therefore, to discuss the basis for comparison of relatively recent experimental data on supercritical carbon dioxide (CO2) obtained at facilities of the Korea Atomic Energy Research Institute (KAERI) and Chalk River Laboratories (CRL) of Atomic Energy of Canada Limited (AECL). Based on the available instrumental error, a thorough analysis of experimental errors in wall- and bulk-fluid temperatures, and heat transfer coefficient is conducted. It is shown that rarely published data on instrumental errors tend to underpredict significantly actual experimental errors. A revised heat-transfer correlation for the CRL data is presented. A preliminary heat-transfer correlation for joint CRL and KAERI datasets is developed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.200
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.017
GPT teacher head0.235
Teacher spread0.219 · 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 teacher head, 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

Citations0
Published2014
Admission routes3
Has abstractyes

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