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Record W2033989736 · doi:10.1115/icmm2005-75170

On the Slip Mechanism of Microfluidics by Means of a Mean-Field Free-Energy Lattice Boltzmann Method

2005· article· en· W2033989736 on OpenAlexaff
Junfeng Zhang, Daniel Y. Kwok

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLattice Boltzmann methodsSlip (aerodynamics)SlippingSlip ratioMechanicsBoundary value problemMaterials sciencePhysicsGeometryThermodynamicsMathematics

Abstract

fetched live from OpenAlex

In this paper, we studied liquid-solid slip by employing a mean-field free-energy lattice Boltzmann approach recently proposed [Zhang et al., Phy. Rev. E. 69, 032602, 2004]. With a general bounce-back no-slip boundary condition applied to the interface, liquid slip was observed because of the specific fluid-solid interaction. The slip length is clearly related to the interaction strength: the stronger the interaction, the less hydrophobic the surface and hence results in less slipping. Unlike other lattice Boltzmann models, a contact angle value between 0–180° can be generated here without using a less realistic repulsive fluid-solid interaction. We found that system size does not affect the absolute slip magnitude; however, the ratio of the slip length to system size increases quickly as the system becomes smaller, illustrating that slip becomes important in smaller-scale systems. A small negative slip length can also be produced with a strong fluid-solid attraction. These results are in qualitative agreement with those from experimental and molecular dynamics studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2005
Admission routes1
Has abstractyes

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