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Record W2134250747 · doi:10.1017/s0022112007005988

Evolution and mixing of asymmetric Holmboe instabilities

2007· article· en· W2134250747 on OpenAlexaff
Jeffrey R. Carpenter, Gregory A. Lawrence, William D. Smyth

Bibliographic record

VenueJournal of Fluid Mechanics · 2007
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysicsInstabilityMixing (physics)MechanicsStratified flowsTurbulenceShear flowAsymmetryStratified flowShear (geology)Schmidt numberClassical mechanicsReynolds numberMaterials science

Abstract

fetched live from OpenAlex

When a stably stratified density interface is embedded in a region of strong velocity shear, hydrodynamic instabilities result. Here we generalize the stratified shear layer to allow an offset between the centre of the shear layer and the density interface. By including this asymmetry, and keeping the density interface thin with respect to the shear layer, the asymmetric Holmboe (AH) instability emerges. This study examines the evolution and mixing behaviour of AH instabilities, and compares the results to the well-known Kelvin–Helmholtz (KH) and Holmboe instabilities. This is done by performing a series of direct numerical simulations (DNS). The simulation results show that there are two different mixing mechanisms present. The first is a feature of KH instabilities and leads to the mixing and production of intermediate density fluid. The second mixing mechanism is found in AH and Holmboe instabilities and consists of regions of mixing and turbulence production that are located on one or both sides of the density interface. Since the Holmboe-type instabilities do not generate a large-scale overturning of the central isopycnal, the density interface is able to retain its identity throughout the mixing event. The amount of mixing that takes place is found to be strongly dependent on the degree of asymmetry in the flow.

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)0.001
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 score0.783
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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

Citations64
Published2007
Admission routes1
Has abstractyes

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