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Record W2559470854 · doi:10.1115/pvp2016-63893

Experimental Investigation of Resonant Coupling Between Turbulent Flow Past a Rectangular Cavity and a Standing Gravity Wave

2016· article· en· W2559470854 on OpenAlexaff
Jonathan Reaume, Peter Oshkai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMechanicsParticle image velocimetryTurbulencePhysicsOpticsResonatorStanding waveCoupling (piping)AmplitudeMaterials science

Abstract

fetched live from OpenAlex

Coupling between self-excited oscillations of turbulent flow of water in an open channel along the opening of a rectangular cavity and the standing gravity wave in the cavity was investigated experimentally for a range of inflow velocities and characteristic depths of the water. The objective of the current investigation is to examine the effect of water depth on the onset of fully coupled oscillations of the shear flow past the cavity. Video recording of the oscillating free-surface inside the resonator cavity in conjunction with free-surface elevation measurements using a capacitive wave gauge provided representation of the resonant wave modes of the cavity as well as the degree of the flow-wave coupling in terms of the amplitude and the quality factor of the associated spectral peak. Moreover, application of digital particle image velocimetry (PIV) provided insight into the evolution of the vortical structures that formed across the cavity opening. Coherent oscillations were attainable for a wide range of water depths. Variation of the water depth affected the degree of coupling between the shear layer oscillations and the gravity wave as well as the three-dimensionality of the flow structure.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.021
GPT teacher head0.211
Teacher spread0.191 · 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 designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

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