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Record W4387185957 · doi:10.1017/jfm.2023.719

Flow dynamics in sinusoidal channels at moderate Reynolds numbers

2023· article· en· W4387185957 on OpenAlexaff
S. W. Gepner, J. M. Floryan

Bibliographic record

VenueJournal of Fluid Mechanics · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsWestern University
FundersNarodowym Centrum NaukiPolitechnika WarszawskaNarodowe Centrum Nauki
KeywordsReynolds numberMechanicsPhysicsFlow (mathematics)CurvatureVortexBifurcationHele-Shaw flowPressure gradientWavelengthOpen-channel flowHydrodynamic stabilityNonlinear systemClassical mechanicsMathematicsGeometryOpticsTurbulence

Abstract

fetched live from OpenAlex

Pressure-gradient-driven flows through sinusoidal channels have been studied. The analysis was carried out up to the formation of secondary nonlinear states and spanned a range of low and moderate Reynolds numbers. Direct numerical simulations were used to identify and determine the properties of steady as well as non-stationary, two-dimensional (2-D) and three-dimensional secondary flows. Our results indicate the existence of several distinct solution types. Two-dimensional, stationary flows with periodicity determined by the corrugation represent the first type. The second type is associated with the appearance of 2-D oscillatory flows arising from the onset of unstable travelling waves. Such oscillatory solutions are generally out of phase with the wall corrugation but could be in phase in special cases determined by the ratio of the critical disturbance wavelength and the channel corrugation wavelength. Consequently, several distinct types of time-dependent solutions are possible. The third type of solution results from the centrifugal effect caused by wall curvature and leads to three-dimensionalization of the flow through the onset of stationary streamwise vortices. Finally, various states resulting from the interaction of different solution types are possible. We examine those states and present a bifurcation diagram illustrating the formation of some of them. The results presented in this paper might help with the development of small-scale flow measurement and detection devices operating at low and moderate Reynolds numbers, as well as in the use of wall topographies for the intensification of mixing in flows with moderate, subturbulent Reynolds numbers.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.204
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

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

Citations9
Published2023
Admission routes1
Has abstractyes

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