MétaCan
Menu
Back to cohort

Solitary Wave Breaking on Irregular 3D Bathymetry Using a Coupled Potential + Viscous Flow Model

2014· article· en· W2166862778 on OpenAlexfundno aff
Yi Zhang, Solomon C. Yim

Bibliographic record

VenueJournal of Engineering Mechanics · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsnot available
FundersOffice of Naval ResearchBritish Columbia Institute of Technology
KeywordsSolverBreaking waveMechanicsBathymetryDomain decomposition methodsPotential flowFlow (mathematics)Wedge (geometry)GeologyPhysicsWave propagationFinite element methodOpticsMathematicsMathematical optimization

Abstract

fetched live from OpenAlex

A three-dimensional (3D) coupled potential + viscous flow model based on a domain decomposition method is developed and applied to study shoaling and breaking of a solitary wave on a nonuniform bathymetry. The flow domain is decomposed into two subdomains separated by an interface: a wave generation and propagation subdomain modeled by a potential-flow (PF) solver and a shoaling and surf zone subdomain modeled by a Navier-Stokes equation (NSE) solver. The PF solver, based on a boundary-element method, is capable of modeling the motion of a piston wavemaker and the propagation of the resulting wave downstream. After the wave passes through the interface, it continues to propagate in the viscous-flow (NSE) subdomain, which is solved by a FEM. A free-surface capturing feature of the NSE solver allows simulation of wave breaking and the postbreaking behavior. Numerical results show good agreement with those of a large-scale wave-basin experiment of a solitary wave run-up on a 3D wedge. The simulation shows that breaking criteria and categorization for plane slopes do not apply to this particular bathymetry. The velocity and vorticity patterns during wave breaking are also examined to demonstrate the capabilities of the viscous-flow solver.

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: none
Teacher disagreement score0.675
Threshold uncertainty score0.617

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 head0.008
GPT teacher head0.178
Teacher spread0.170 · 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Engineering MechanicsSame topicCoastal and Marine DynamicsFrench-language works237,207