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Record W4408139049 · doi:10.1080/10618562.2025.2469498

On the Invalidity of the Extended Navier-Stokes Equations to Compute Rarefied Gas Flows in a Cylinder Array

2024· article· en· W4408139049 on OpenAlexafffund
Jean‐Michel Tucny, Sébastien Leclaire, David Vidal, François Bertrand

Bibliographic record

VenueInternational journal of computational fluid dynamics · 2024
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCylinderNavier–Stokes equationsPhysicsMechanicsClassical mechanicsMathematicsMathematical analysisCompressibilityGeometry

Abstract

fetched live from OpenAlex

Computing rarefied gas flow fields through porous media with the Boltzmann equation is computationally prohibitive for industrial use. To reduce computational cost, extensions of the Navier-Stokes equations with phenomenological slip boundary conditions have been proposed. Models relying on a hypothesis of proportionality between the effective mean free path and the effective viscosity were previously validated for unidimensional planar and concentric geometries, but lacked verification for intermediate complexity cases like a bidimensional flow through a cylinder array. This article proposes using a lattice Boltzmann method, with the effective mean free path calculated via a verified ray-tracing algorithm. Although excellent agreement with experimental data was found for slit flow, quantitative agreement was not achieved for flow through a cylinder array, despite model convergence at a sublinear order (∼0.52). The study discusses the limitations of the extended Navier-Stokes model, concluding that it is unsuitable for rarefied gas flows with transverse components.

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)0.006
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.022
GPT teacher head0.285
Teacher spread0.263 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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