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Record W2090886594 · doi:10.1115/1.4007743

Second Law Characterization of Confined Turbulent Flows

2012· article· en· W2090886594 on OpenAlexafffund
G.F. Naterer, O. B. Adeyinka

Bibliographic record

VenueJournal of Fluids Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsImperial Oil (Canada)Memorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTurbulenceParticle image velocimetryMechanicsEntropy productionKolmogorov microscalesReynolds numberDirect numerical simulationStatistical physicsEntropy (arrow of time)PhysicsReynolds stressOpen-channel flowK-epsilon turbulence modelTurbulence kinetic energyPipe flowDissipationClassical mechanicsK-omega turbulence modelThermodynamics

Abstract

fetched live from OpenAlex

In this paper, a new measurement technique for turbulent entropy production is developed and applied to confined channel flows. Past methods of dimensional analysis, Clark gradient, and Smagorinsky models for subgrid turbulent stresses are examined to determine the flow irreversibilities throughout the flow field. The new experimental method obtains the turbulent irreversibilities up to a certain particle image velocimetry (PIV) cut-off wavelength, very close to the wall of the channel. Measured results of turbulence dissipation and entropy production at varying Reynolds numbers are presented and compared successfully against results from direct numerical simulations. The subgrid scale models of turbulent flow irreversibilities are shown to provide an effective alternative to direct PIV averaging of turbulent stresses, particularly close to the wall, where PIV resolution makes it difficult to precisely determine the averaged turbulence fluctuations. This paper develops a new PIV based method that enables the whole-field measurements of turbulent entropy production, and it presents new experimental data for entropy production in channel flows.

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 score0.791
Threshold uncertainty score0.642

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 head0.006
GPT teacher head0.181
Teacher spread0.176 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

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