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Record W4312525096 · doi:10.1115/omae2022-81324

On the Quality of Laboratory Water Waves Generated Mechanically by Flap Wavemakers

2022· article· en· W4312525096 on OpenAlexaff
Sébastien Fouques, Andreas Holm Akselsen, Trevor Harris, Kent Brett

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsCommunity Sector Council Newfoundland and Labrador
Fundersnot available
KeywordsSpurious relationshipAmplitudeAcousticsGeologyQuality (philosophy)TowingSurface waveMechanicsMarine engineeringOpticsEngineeringPhysicsComputer science

Abstract

fetched live from OpenAlex

Abstract In laboratories such as ocean basins, surface waves are generated mechanically by imposing an oscillatory motion on one or several vertical boundaries. The shape and characteristics of the wavemaker have a direct impact on the wave heights and periods that can be generated, but also on the quality of the waves obtained in the basin. Here, quality is defined in terms of spurious waves generation, meaning it is directly related to the predictability of the wave field in the basin. Additional factors affecting wave quality can be either mechanical, like the precision of the imposed flap motion, or hydrodynamic, like the amplitude of the flap angle. In this paper, we investigate the quality of waves generated mechanically by several types of flap-wavemakers, including double-hinged and single-hinged ones, with various hinge depths. The amplitude of second-order spurious waves is evaluated analytically and compared to laboratory experiments carried out in SINTEF’s ocean basin. Third-order effects that results from using a second-order correction of the flap motion are also addressed. Then, the ability of deep-hinged wavemakers to generate short waves, either steep or with small amplitudes, is assessed based on measurements performed at NRC’s towing tank facility. Finally, recommendations on choosing a wavemaker design are proposed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.172
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0090.000

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 head0.214
Teacher spread0.198 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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