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Record W1996000176 · doi:10.1209/0295-5075/106/44003

The stochastic mode of the Faraday instability of shallow fluid layers

2014· article· en· W1996000176 on OpenAlexaff
Marek Stastna, Francis Poulin

Bibliographic record

VenueEurophysics Letters (EPL) · 2014
Typearticle
Languageen
FieldComputer Science
TopicNonlinear Dynamics and Pattern Formation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInstabilityFaraday cageMode (computer interface)MechanicsPhysicsComputer scienceMagnetic fieldQuantum mechanics

Abstract

fetched live from OpenAlex

The instability of a vertically oscillated layer of fluid is a classical problem whose history dates back to Faraday in the 19th century. We consider the stability of a shallow layer for which the oscillation, as expressed through the effective gravitational acceleration, is a random function of time. Using both theoretical linear stability analysis and high-resolution numerical simulations, including both individual realizations and ensemble calculations, of the nonlinear system of equations, we find that two different stochastic modes of instability exist. Both modes find their expression in finite amplitude oscillations of the free surface that exhibit sharp crests and broad troughs, or in other words, that resemble the classical Stokes wave. We demonstrate that a necessary condition for the first type of instability is snapshot, or instantaneous, instability. The subdominant instability resembles classical parametric resonance that can exist in a harmonically oscillated layer of fluid, and occurs even when the flow is always snapshot stable (or the gravitational acceleration is non-negative).

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)0.001
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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
Published2014
Admission routes1
Has abstractyes

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