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Record W2141888004 · doi:10.2514/1.25752

Boundary Layer on a Moving Wall

2006· article· en· W2141888004 on OpenAlexaff
E. P. Menu, Stavros Tavoularis

Bibliographic record

VenueAIAA Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFreestreamBoundary layerDragMechanicsTurbulenceParasitic dragAerospace engineeringGeologyPhysicsEngineeringReynolds number

Abstract

fetched live from OpenAlex

M OVING belts and other types of moving walls have been used as partial boundaries of wind tunnels, water channels, wings, and other facilities and components in many experimental investigations of a variety of flow phenomena. In applied studies, moving walls have been used to simulate the ground effect on moving vehicles [1–3] or as a means of boundary layer control and drag reduction [4–7],whereasmoving belts have also been employed in fundamental investigations of the turbulence structure and associated heat transfer [8–13]. For a smooth, plane belt that is long enough to approximate the ideal case of an infinite movingwall and a uniform freestream, one would expect that the characteristics of the boundary layer (BL) would depend only on the relative velocity between the belt and the freestream. In practice, however, the operations of the belt and the flow facility could be coupled in apparent or subtle ways, and other factors may play a role in the nearwall velocity variation. Because it is not always feasible or economical to document directly the BL state under the actual experimental conditions, one may be compelled to adopt the use of information collected in conventional BL over stationary walls, even in moving-wall cases. This Note reports some experience in using a moving belt as the upper wall of a water channel, in the hope that it will be of help to other researchers using similar arrangements or planning to do so.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.347

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 head0.189
Teacher spread0.184 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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