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Generation and propagation of ship-borne waves - Solutions from a Boussinesq-type model

2017· article· en· W2736957381 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.

Bibliographic record

VenueCoastal Engineering · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsUniversity of Ottawa
FundersJapan Society for the Promotion of Science
KeywordsBathymetryComputationNonlinear systemGeologyWedge (geometry)MechanicsWave propagationField (mathematics)Range (aeronautics)Marine engineeringPhysicsComputer scienceMathematicsOpticsEngineeringAerospace engineeringAlgorithm

Abstract

fetched live from OpenAlex

Ship-borne waves are of significant interest for the design of port and waterway infrastructure and the maintenance of its surrounding environment. Computation of these nonlinear and dispersive waves has mainly been focusing on their near-field generation as a fluid-body interaction problem. This study presents an approach for the computation of ship waves generated by a moving pressure disturbance with phase-resolving and depth-averaged equations. To support a wide range of applicability, the paper deals with the evolution of the vessel wedge compared to an analytical solution for sub-to supercritical speeds and the assessment of wave patterns from a broad range of pressure term dimensions, including cross-references to findings in other studies. The conducted numerical experiments showcase the typical response of a Boussinesq-type model to a simplified moving pressure disturbance and identify the main factors and criteria for ship-wave propagation in the far-field of a vessel. Finally, a unique field dataset underlines the capability of an extended Boussinesq-type model to compute the propagation of vessel waves over an irregular bathymetry.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.329

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 head0.029
GPT teacher head0.206
Teacher spread0.177 · 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