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Effects of roughness on the turbulent Prandtl number, timescale ratio, and dissipation of a passive scalar

2022· article· en· W4311418133 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysical Review Fluids · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Ontario
KeywordsTurbulent Prandtl numberPrandtl numberTurbulenceScalar (mathematics)MechanicsDissipationPhysicsMomentum (technical analysis)Surface finishStatistical physicsClassical mechanicsNusselt numberMathematicsReynolds numberGeometryThermodynamicsHeat transferEngineeringMechanical engineeringEconomics

Abstract

fetched live from OpenAlex

Turbulence models for scalar and momentum transport are often analogous to each other, but roughness breaks this similarity. The turbulent Prandtl number, the momentum and scalar time-scales, and the dissipation budget are among the central quantities in the modeling of scalar transport. This study examines how roughness affects them by analyzing the results of direct numerical simulations of channel flow with resolved roughness. The flow field below the crest of the elements, which is very difficult to access experimentally, provides answers to long-standing questions.

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.

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 score0.900
Threshold uncertainty score0.356

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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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