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Record W2982482495 · doi:10.1115/icnmm2019-4216

Laminar Heat Transfer of Gas-Liquid Segmented Flows in Circular Ducts With Constant Wall Temperature

2019· article· en· W2982482495 on OpenAlexaff
K. Alrbee, Yuri S. Muzychka, Xili Duan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsLaminar flowNusselt numberSlug flowHeat transferReynolds numberThermodynamicsMechanicsDimensionless quantityMaterials scienceHeat fluxFlow (mathematics)Two-phase flowPhysicsTurbulence

Abstract

fetched live from OpenAlex

Abstract Laminar heat transfer of gas-liquid Taylor flow in circular tubes is considered. Previous studies have found that introducing a gas phase into a flow stream of a liquid phase significantly increases the heat transfer rate. Other studies considered the effect of slug length on heat transfer rates. The present study’s aim is to demonstrate heat transfer enhancement due to the shortening of liquid slug lengths in a segmented flow and to further validate a model previously developed by the second author. An experimental setup was assembled using mini scale horizontal tube in which the two phase fluid flow is heated under constant wall temperature. New experimental data for gas-liquid Taylor flow in mini scale were carefully obtained using 1 cSt silicone oil which was segmented by air. The experiments were performed with a liquid fraction maintained constant at 0.5 and Reynolds numbers from 50 to 320. In the present work, it is shown that for constant wall temperature, the dimensionless mean wall flux and Nusselt number have been increased by a factor of two at the upper limit of laminar flow which was considered with ReD = 320, when the slug aspect ratio LS/D equal to 10. On other hand the enhancement becomes three times at the same limit of flow when slug aspect ratio has reduced to 1.25 which almost approaches the tube diameter.

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 score0.006
Threshold uncertainty score0.551

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 head0.005
GPT teacher head0.180
Teacher spread0.175 · 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
Published2019
Admission routes1
Has abstractyes

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