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Record W4385725000 · doi:10.1063/5.0159676

Sedimentation in particle-laden flows with and without velocity shear

2023· article· en· W4385725000 on OpenAlexaff
Adam J. K. Yang, Jason Olsthoorn, Mary‐Louise Timmermans

Bibliographic record

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsQueen's University
Fundersnot available
KeywordsSettlingInstabilityPhysicsRayleigh–Taylor instabilityMechanicsStratified flowsStratification (seeds)Shear (geology)Rayleigh scatteringShear velocityShear flowStratified flowClassical mechanicsGeologyTurbulenceThermodynamicsOptics

Abstract

fetched live from OpenAlex

The vertical transport of sediment from particle-laden flows in marine settings can be enhanced by a settling-driven convective instability. The presence of a horizontal velocity shear can further influence this vertical transport. We conduct numerical simulations to investigate the vertical sediment transport in the presence and absence of shear. We show how this transport is determined by a competition between the growth of the settling-driven convective instability (Rayleigh–Taylor) and the stratified shear instability (Kelvin–Helmholtz). In the absence of shear, the Rayleigh–Taylor instability drives enhanced vertical sediment transport; this effect increases with the Stokes settling velocity of the particles and decreases with the stratification strength. In the presence of shear, there are two regimes of effective settling. When the Kelvin–Helmholtz instability grows rapidly and suppresses the Rayleigh–Taylor instability, the effective settling velocity is significantly reduced. On the other hand, if the Rayleigh–Taylor instability dominates and completely inhibits the Kelvin–Helmholtz instability, the effective settling velocity is enhanced due to the additional energy input by shear. We explore the parameter space of these regimes and interpret their physics.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.116

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 head0.025
GPT teacher head0.244
Teacher spread0.219 · 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 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
Published2023
Admission routes1
Has abstractyes

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