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Record W1994033151 · doi:10.1115/ht2013-17283

New Predictive Methodology for the Onset of Flow Instability in Single Horizontal Microtube With an Inlet Orifice

2013· article· en· W1994033151 on OpenAlexaff
Yanfeng Fan, Ibrahim Hassan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsBody orificeMechanicsPressure dropVolumetric flow rateInletMaterials scienceHeat fluxMass flow rateInstabilityMass fluxVolume (thermodynamics)Drop (telecommunication)ThermodynamicsHeat transferPhysicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

A new methodology to predict the onset of flow instability (OFI) in single horizontal microtube with inlet orifice is proposed. The predictive methodology states that OFI occurs as the pumping power under no heating condition is equal to the pumping power under heating condition in the microtube at the same volume rate. Since the pumping power can be simply described as the product of volume rate and pressure drop cross the microtube, the heat flux at OFI is determined as the two-phase pressure drop under heating condition is equal to the single-phase pressure drop under no heating condition at same flow rate. The addition of inlet orifice increases the pumping power under no heating condition. The increased pumping power by orifice delays the onset of flow instability. The predictive methodology is validated by comparing the predicted heat flux at OFI with our previous experimental data in the microtubes with three different inlet restriction ratios. The result shows that the proposed method is capable of prediction of heat flux at OFI with a deviation of 30% and mean absolute error of 13% at mass flux less than 2000 kg/m2·s.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.495
Threshold uncertainty score0.211

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 head0.031
GPT teacher head0.243
Teacher spread0.212 · 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 designSimulation or modeling
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

Citations1
Published2013
Admission routes1
Has abstractyes

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