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Record W3110862400

Three-dimensional visualization of stratified turbulent wakes at varying Reynolds number

2019· article· en· W3110862400 on OpenAlexaff
Basem Halawa, Shadee Merhi, Cynthia Tang, Qi Zhou

Bibliographic record

VenueAPS Division of Fluid Dynamics Meeting Abstracts · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWakeReynolds numberTurbulenceMechanicsFlow visualizationVortexStratified flowsPhysicsStratified flowFlow (mathematics)MeteorologyGeology
DOInot available

Abstract

fetched live from OpenAlex

We present a series of three-dimensional visualizations of numerically simulated turbulent wakes in a stably stratified fluid with a focus on the effects of the wake Reynolds number, Re, on the wake flow. The visualization of stratified wakes is complicated by the coexistence of regions of distinct dynamics in the flow, including large-scale ‘pancake vortices,’ small-scale shear instabilities within layers of concentrated vertical shear, and internal waves emitted by the wake flow to the ambient fluid. We apply various techniques to identify dynamically distinct regions within the wake flow and visualize them separately. The volume fractions occupied by each of these regions are also quantified. Through the visualizations, we observe three significant effects on the wake’s evolution associated with increasing wake Reynolds number: (1) nontrivial modifications to the structure of the pancake vortices, (2) prolongation of the period of internal wave emission from the wake, and (3) greater longevity of small-scale shear instabilities within the anisotropic flow structures. These observations reveal a trend in Re that can potentially be extrapolated to stratified wakes at even larger Reynolds numbers that are typical of geophysical wakes and currently not accessible to laboratory experiments or numerical simulations.

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 categoriesInsufficient payload (model declined to judge)
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 score0.105
Threshold uncertainty score0.999

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)0.0020.000

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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