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Record W2090596927 · doi:10.1115/icnmm2008-62187

Laminar Fully Developed Flow in Periodically Converging-Diverging Microtubes

2008· article· en· W2090596927 on OpenAlexafffund
Majid Bahrami, Mohsen Akbari, David Sinton

Bibliographic record

VenueASME 2008 6th International Conference on Nanochannels, Microchannels, and Minichannels · 2008
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLaminar flowMechanicsReynolds numberPressure dropHagen–Poiseuille equationFlow (mathematics)RADIUSMaterials scienceFlow conditioningParametric statisticsWork (physics)TurbulencePhysicsMathematicsThermodynamicsComputer science

Abstract

fetched live from OpenAlex

Laminar fully developed flow and pressure drop in linearly varying cross -section converging-diverging microtubes have been investigated in this work. These microtubes are formed from a series of converging-diverging modules. An analytical model is developed for frictional flow resistance assuming parabolic axial velocity profile in diverging and converging sections. Frictional flow resistance is found to be only a function of the geometrical parameters. To validate the model, a numerical study is carried out for the Reynolds number ranging from 0.01 to 100, for various taper angles and maximum-minimum radius ratios ranging from 0.5 to 1. Comparisons between the model and numerical results show that the proposed model predicts the axial velocity and the flow resistance accurately. As expected, the flow resistance is found to be effectively independent of the Reynolds number from numerical results. Parametric study shows that the effect of radius ratio is more significant than the taper angle. It is also observed that for small taper angles, flow resistance can be determined accurately by applying the local Poiseuille flow approximation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.237
Teacher spread0.204 · 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.

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

Citations15
Published2008
Admission routes2
Has abstractyes

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