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Record W1981842144 · doi:10.1063/1.3644694

On the transition between distributed and isolated surface roughness and its effect on the stability of channel flow

2011· article· en· W1981842144 on OpenAlexaff
J. M. Floryan, Masahito Asai

Bibliographic record

VenuePhysics of Fluids · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsWestern University
Fundersnot available
KeywordsSurface finishSurface roughnessContext (archaeology)PhysicsSmoothnessFlow (mathematics)Open-channel flowMechanicsGeometryOpticsGeologyMathematical analysisMechanical engineeringMathematicsThermodynamicsEngineering

Abstract

fetched live from OpenAlex

The question of whether a system of roughness elements has to be viewed either as a distributed roughness or a set of individual, hydrodynamically independent roughness elements has been considered. The answer has been given in the context of definition of hydraulic smoothness proposed by Floryan [Eur. J. Mech. B/Fluids 26, 305 (2007)] where a roughness system that cannot destabilize the flow is viewed as hydraulically inactive. Linear stability characteristics have been traced from the distributed to the isolated roughness limits. It has been shown that an increase of distance between roughness elements very quickly stabilizes disturbances in the form of streamwise vortices; however, roughness elements placed quite far apart are able to affect evolution of disturbances in the form of traveling waves. Transition from the distributed to the isolated roughness limit is achieved much faster in the case of roughness elements in the form of “trenches” forming depressions below the reference surface than in the case of roughness elements in the form of “ridges” protruding above the reference surface.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.197
Teacher spread0.180 · 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 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

Citations32
Published2011
Admission routes1
Has abstractyes

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