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Record W2904549326 · doi:10.1139/cjp-2018-0625

Vortex identification in turbulent flows past plates using the Lagrangian method

2018· article· en· W2904549326 on OpenAlexvenueno aff
Bashar Attiya, I-Han Liu, Muhannad Altimemy, Cosan Daskiran, Alparslan Öztekin

Bibliographic record

VenueCanadian Journal of Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsVortexPhysicsTurbulenceVorticityClassical mechanicsLyapunov exponentVector fieldReynolds numberFlow (mathematics)MechanicsVortex sheddingEulerian pathDissipationField (mathematics)Nonlinear systemStatistical physicsLagrangianTheoretical physicsMathematics

Abstract

fetched live from OpenAlex

Vortex identifications in turbulent flows past arrays of tandem plates are performed by employing the velocity field obtained by high-fidelity large eddy simulations. Lagrangian coherent structures (LCSs) are extracted to examine the evolution and the nonlinear interaction of vortices and to characterize the spatial and temporal characteristics of the flow. LCSs’ identification method is based on the finite-time Lyapunov exponent (FTLE), which is evaluated using the instantaneous velocity data. The simulations are performed in three-dimensional geometries to understand the physics of fluid motion and the vortex dynamics in the vicinity of plates and surfaces at Reynolds number of 50 000. The instantaneous vorticity fields, Eulerian Q-criterion, and LCSs are presented to interpret and understand complex turbulent flow structures. The three-dimensional FTLE fields provide valuable information about the vortex generation, spatial location, evolution, shedding, decaying, and dissipation of vortices. It is demonstrated here that FTLE can be used together with Eulerian vortex identifiers to characterize the turbulent flow field effectively.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.994

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 head0.014
GPT teacher head0.241
Teacher spread0.228 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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