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Record W1987253259 · doi:10.1115/1.4025503

Prediction of the Onset of Flow Instability in a Single Horizontal Microtube With an Inlet Orifice

2013· article· en· W1987253259 on OpenAlexaff
Yanfeng Fan, Ibrahim Hassan

Bibliographic record

VenueJournal of Heat Transfer · 2013
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsInstabilityMechanicsBody orificePressure dropInletFlow coefficientMass fluxMass flow rateMaterials scienceTwo-phase flowHeat fluxThermodynamicsVolumetric flow rateMass flowFlow (mathematics)Mass flow meterPhysicsHeat transferEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

A methodology to predict the onset of flow instability (OFI) in a single horizontal microtube with an inlet orifice is developed based on the predication of pressure drop. The predictive methodology states, for the same flow rate, the flow instability occurs as the single-phase liquid pressure drop under no heating condition equals the two-phase pressure drop under heating condition in a single microtube. The addition of inlet orifice increases the heat flux at the onset of flow instability by increasing the upstream pressure. The present methodology is validated by comparing the predicted heat flux at the onset of flow instability with our previous experimental data in the microtubes with three sizes of inlet orifices. The results show that the present method can predict the heat flux at the onset of flow instability with a deviation of 30% and mean absolute error of 13% at mass fluxes from 700 to 3000 kg/m2 s. The effects of inlet orifice size and saturation pressure on the onset of flow instability are also studied based on the present methodology. It is found that, at mass fluxes from 100 to 2000 kg/m2 s, the area ratio less than 15% eliminates the flow instability completely before the critical heat flux occurs.

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 score0.307
Threshold uncertainty score0.273

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 head0.012
GPT teacher head0.183
Teacher spread0.172 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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