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Record W4284994756 · doi:10.1063/5.0100979

Inertial instabilities of stratified jets: Linear stability theory

2022· article· en· W4284994756 on OpenAlexafffund
M. W. Harris, Francis Poulin, Kevin G. Lamb

Bibliographic record

VenuePhysics of Fluids · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBarotropic fluidBaroclinityPhysicsInstabilityReynolds numberMechanicsRossby numberHydrodynamic stabilityClassical mechanicsLinear stabilityStratified flowsTurbulenceJet (fluid)Stratified flowStatistical physics

Abstract

fetched live from OpenAlex

This paper uses a linear stability analysis to investigate instabilities of barotropic and baroclinic jets that satisfy the necessary condition for inerital instabilities within the context of a rotating, stratified Boussinesq model. First, we review the different types of instabilities that can occur in these jets and the conditions that make the jet subject to inertial instability but stable to Rayleigh–Taylor instability. Second, we numerically solve one-dimensional and two-dimensional eigenvalue problems for the linear stability problems and examine the dependence of the growth rates on the Rossby number, Burger number, the aspect ratio, and the Reynolds number. We find that there are two critical Reynolds numbers where there is a transition between what type of instability has the largest growth rate. Finally, we examine the characteristics of inertial instabilities in more detail for three selected parameter sets: a low Reynolds number regime, a high Reynolds number regime, and a regime with low Reynolds number and larger aspect ratio. The most unstable mode in the low Reynolds number regime is a barotropic–baroclinic instability and has a barotropic spatial structure. In contrast, the most unstable mode in the high Reynolds number regime is an inertial instability and its spatial structure is independent of the along-flow direction. Modes with this property are commonly referred to as symmetric instabilities. In the intermediate regime, the flow can be unstable to both barotropic–baroclinic and inertial instabilities, possibly with comparable growth rates.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.999

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)0.0020.000

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 head0.019
GPT teacher head0.212
Teacher spread0.193 · 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 designObservational
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

Citations8
Published2022
Admission routes2
Has abstractyes

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