MétaCan
Menu
Back to cohort
Record W3116351756 · doi:10.1115/1.4049400

On the Use of a Buoyancy Parameter for Distinguishing Deteriorated From Normal Heat Transfer in Upward Flows at Supercritical Pressures

2020· article· en· W3116351756 on OpenAlexaff
Nathan Kline, Stavros Tavoularis

Bibliographic record

VenueJournal of Fluids Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBuoyancyThermodynamicsMechanicsSupercritical fluidMass fluxHeat transferHeat fluxRefrigerantWork (physics)Mass transferFlux (metallurgy)Materials scienceChemistryHeat exchangerPhysics

Abstract

fetched live from OpenAlex

Abstract An extensive analysis of two versions of a buoyancy parameter as supercritical heat transfer deterioration (DHT) identifiers was conducted for large databases obtained in carbon dioxide flowing through three electrically heated tubes with internal diameters equal to 4.6, 8.0, and 22.0 mm and in Refrigerant R134a through an 8.0 mm tube. For the first time, buoyancy parameter profiles along each tube were considered for wide ranges of closely incremented operating conditions. The occurrence of DHT in each test section was first assessed confidently by observation of wall temperature profiles and comparison of measurements with wall temperature predictions of a correlation for normal heat transfer (NHT). The objective of this work was to determine whether a universal buoyancy parameter threshold could be used as a means for identifying DHT in a test section. It was found that correction factors were required for both parameters to account for an observed shift of the threshold for DHT occurrence, as the mass flux was changed. The resulting threshold for one of the buoyancy parameters identified correctly DHT for cases having a mass flux up to a certain value, but failed to do so for cases with a higher mass flux.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.044
GPT teacher head0.223
Teacher spread0.179 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Fluids EngineeringSame topicHeat transfer and supercritical fluidsFrench-language works237,207