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Record W2324529864 · doi:10.1103/physreve.79.045307

Impact of the inherent separation of scales in the Navier–Stokes-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>α</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:math>equations

2009· article· lv· W2324529864 on OpenAlexaff
Tae‐Yeon Kim, Massimo Cassiani, J. D. Albertson, John E. Dolbow, Eliot Fried, Morton E. Gurtin

Bibliographic record

VenuePhysical Review E · 2009
Typearticle
Languagelv
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsHomogeneous isotropic turbulenceVorticityNavier–Stokes equationsBETA (programming language)Tensor (intrinsic definition)TurbulenceIsotropyPhysicsDirect numerical simulationEnergy (signal processing)Domain (mathematical analysis)MathematicsMathematical analysisAlgorithmGeometryComputer scienceCompressibilityVortexMechanicsQuantum mechanicsReynolds number

Abstract

fetched live from OpenAlex

We study the effect of the length scales $\ensuremath{\alpha}$ and $\ensuremath{\beta}$ in the Navier--Stokes-$\ensuremath{\alpha}\ensuremath{\beta}$ equations on the energy spectrum and the alignment between the vorticity and the eigenvectors of the stretching tensor in three-dimensional homogeneous and isotropic turbulent flows in a periodic cubic domain, including the limiting cases of the Navier--Stokes-$\ensuremath{\alpha}$ and Navier--Stokes equations. A significant increase in the accuracy of the energy spectrum at large wave numbers arises for $\ensuremath{\beta}&lt;\ensuremath{\alpha}$. The vorticity structures predicted by the Navier--Stokes-$\ensuremath{\alpha}\ensuremath{\beta}$ equations also improve as $\ensuremath{\beta}$ decreases away from $\ensuremath{\alpha}$. However, optimal choices for $\ensuremath{\alpha}$ and $\ensuremath{\beta}$ depend not only on the problem of interest but also on the grid resolution.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.278
Teacher spread0.260 · 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

Citations16
Published2009
Admission routes1
Has abstractyes

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Same venuePhysical Review ESame topicFluid Dynamics and Turbulent FlowsFrench-language works237,207