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Record W3211948583 · doi:10.1063/5.0067912

Double-diffusive instability in a thin vertical channel

2021· article· en· W3211948583 on OpenAlexaff
Sierra Legare, Andrew P. Grace, Marek Stastna

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhysicsInstabilityBuoyancyDouble diffusive convectionMechanicsConvectionDiffusionRayleigh–Taylor instabilityAsymmetryGrashof numberClassical mechanicsRayleigh numberNatural convectionReynolds numberThermodynamicsNusselt numberTurbulence

Abstract

fetched live from OpenAlex

Double-diffusive convection in the finger regime is studied using direct numerical simulations in a confined domain. For narrow (1–4 mm horizontal extent) domains, we demonstrate active instabilities that are uniquely double-diffusive, or in other words that no instabilities develop when differential diffusion is not present. The novel double-diffusive instabilities are influenced by the boundaries, but demonstrate complex time-dependent evolution down to lateral extents of 1.25 mm. We quantify the energetics, the horizontal asymmetry, and the buoyancy flux due to the instability. We utilize these results to characterize the instability within regimes and point out that while coherent instabilities associated with larger gaps are well characterized by the ratio of diffusive effects to buoyancy forces (the time dependent Grashof number), for smaller gap widths, regime characterization is more difficult. Nevertheless, even at a gap of 1.25 mm, the instability remains robust, and thus it can be concluded that double diffusion can be employed to drive localized mixing in highly confined settings for which single constituent Rayleigh–Taylor does not manifest.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.515

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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.

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

Citations18
Published2021
Admission routes1
Has abstractyes

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