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Infinite series formulation for slip flow through a finite thickness orifice plate

2025· article· en· W4413097782 on OpenAlexafffund
Michael S. H. Boutilier, Rohit G. S. Ghode

Bibliographic record

VenueEuropean Journal of Mechanics - B/Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsWestern University
FundersCompute Canada
KeywordsBody orificeSlip (aerodynamics)Orifice plateSeries (stratigraphy)MechanicsFlow (mathematics)GeologyMathematicsMaterials scienceCalculus (dental)GeometryEngineeringMechanical engineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Liquid transport through membrane nanopores is often modelled as creeping flow through a finite thickness orifice plate. Experiments and molecular simulations have revealed the importance of slip in such pores, where the diameter can be orders of magnitude smaller than the slip length for materials such as carbon nanotubes and graphene. Approximate hydrodynamic resistance models considering access resistance to the pore and fully developed slip flow within the pore are sometimes applied to estimate flow rates. While this approach is very accurate without slip, it can result in large errors for long slip lengths. Even with large slip lengths, flow development in the entry/exit regions contribute significant pressure drops that should be accounted for. In this paper, we extend an infinite series formulation for no-slip creeping flow through a finite thickness orifice plate to slip flow through the same geometry. We develop an algebraic system of equations for the series coefficients that can be efficiently computed to determine the velocity and pressure fields for the selected pore aspect ratio and slip length. Accurate volume flow rates can be quickly calculated, and are tabulated for convenience. We refine the approximate hydrodynamic resistance model for this flow to include losses in the entry region and obtain a fit for the volume flow rate accurate to within 2.5% for all slip lengths and pore aspect ratios.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.257
Teacher spread0.230 · 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
GenreMethods

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
Published2025
Admission routes2
Has abstractyes

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