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Record W2060578485 · doi:10.1115/fedsm2002-31409

Investigation of Various Structure Identification Methods and the Effects of Tabs on the Near Field of Round Jets

2002· article· en· W2060578485 on OpenAlexaff
Stephanie Waterman, T. Holme, Stuart McIlwain, A. Pollard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsNational Research Council CanadaQueen's University
Fundersnot available
KeywordsVisualizationVortexTurbulenceVorticityStructure tensorVector fieldInvariant (physics)Eigenvalues and eigenvectorsFlow visualizationField (mathematics)Statistical physicsMathematicsComputer scienceFlow (mathematics)PhysicsMechanicsGeometryData miningArtificial intelligenceImage (mathematics)

Abstract

fetched live from OpenAlex

The effects of various combinations of vortex-generating tabs on the turbulence structures in the near-field of a round jet are investigated using LES simulation and flow visualization techniques. The visualization methods include stream-wise and non-streamwise vorticity, and a variety of methods that use the invariant of the velocity gradient tensor (the discriminant, Q value and the second eigenvalue condition). Integration of the LES data sets suggest that the structural changes as a result of introducing tabs is significant; however, the methods used to deduce these changes are not always consistent with one another. In some cases, one scheme will produce large amounts of background “noise” while others are less prone to this effect. It is concluded that qualitatively, the four tab case produces the greatest amount of small scale structure.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2002
Admission routes1
Has abstractyes

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