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Record W2064737154 · doi:10.1115/1.1598993

Influence of Three-Dimensional Roughness on Pressure-Driven Flow Through Microchannels

2003· article· en· W2064737154 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Fluids Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrochannelMicroscale chemistryMaterials scienceSurface finishPressure dropSurface roughnessMechanicsFlow (mathematics)Volume of fluid methodComposite materialNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Surface roughness is present in most of the microfluidic devices due to the microfabrication techniques or particle adhesion. It is highly desirable to understand the roughness effect on microscale flow. In this study, we developed a three-dimensional finite-volume-based numerical model to simulate pressure-driven liquid flow in microchannels with rectangular prism rough elements on the surfaces. Both symmetrical and asymmetric roughness element arrangements were considered, and the influence of the roughness on pressure drop was examined. The three-dimensional numerical solution shows significant effects of surface roughness in terms of the rough elements’ height, size, spacing, and the channel height on both the velocity distribution and the pressure drop. The compression-expansion flow around the three-dimensional roughness elements and the flow blockage caused by the roughness in the microchannel were discussed. An expression of the relative channel height reduction due to roughness effect was presented.

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.

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: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.682

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 head0.009
GPT teacher head0.203
Teacher spread0.195 · 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