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Record W1991211278 · doi:10.1063/1.4731295

Scaling range of velocity and passive scalar spectra in grid turbulence

2012· article· en· W1991211278 on OpenAlexafffund
S. K. Lee, A. Benaïssa, L. Djenidi, Philippe Lavoie, R. A. Antonia

Bibliographic record

VenuePhysics of Fluids · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of TorontoRoyal Military College of Canada
FundersLuonnontieteiden ja Tekniikan Tutkimuksen ToimikuntaAustralian Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsTurbulenceScalingReynolds numberScalar (mathematics)Taylor microscaleKolmogorov microscalesIsotropyHomogeneous isotropic turbulencePower lawMechanicsTurbulence kinetic energyDirect numerical simulationQuantum mechanicsK-omega turbulence modelGeometryStatistics

Abstract

fetched live from OpenAlex

Isotropic velocity and scalar fluctuations are closely approximated by slightly stretching a heated grid flow through a short (1.36:1) contraction. The heating is such that temperature serves as a passive scalar, and the velocity/scalar time scale ratio is about one. At small values of Taylor microscale Reynolds number (10 < Rλ < 102), the spectrum of the temperature fluctuations has a more discernible scaling range than the spectrum of the velocity fluctuations. The scaling-range exponent for the thermal spectrum, mθ, exhibits a power-law function of Rλ and tends to the Kolmogorov value of 5/3 more rapidly than that for the velocity spectrum, mu. Both mθ and mu are closer to the Kolmogorov value with the contraction than with no contraction. The trends for the present measurements supplemented with previously published data for larger Rλ (>102) indicate that, to obtain a 5/3 scaling range, Rλ must exceed 103. The ratio (5/3 + mu)/mθ is approximately 2, in close conformity with the proposal of Danaila and Antonia [“Spectrum of a passive scalar in moderate Reynolds number homogeneous isotropic turbulence,” Phys. Fluids 21, 111702 (2009)10.1063/1.3264881].

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.481

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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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

Citations18
Published2012
Admission routes2
Has abstractyes

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