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Propagation, breaking and interaction of regular and irregular waves over a complex bathymetry with an oil rig

2021· article· en· W4213142523 on OpenAlexaff
Hasanat Zaman, Ayhan Akintürk, Lawrence Mak

Bibliographic record

VenueOCEANS 2021: San Diego – Porto · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBathymetryBreaking waveSubmarine pipelineGeologyWavelengthReflection (computer programming)Waves and shallow waterDiffractionWind waveShoaling and schoolingSeabedWave propagationMarine engineeringMechanicsOpticsGeotechnical engineeringPhysicsOceanographyComputer scienceEngineering

Abstract

fetched live from OpenAlex

The oil and gas platform in the nearshore and offshore region are sometimes vulnerable due to the extremely high waves approaching the structure. The bathymetry of such a region plays a vibrant role in modifying the incoming wave fields. Depending on the changes of bathymetry, the deepwater wave can change into a shallow water wave and could cause wave shoaling, modification in wavelength, and in many cases leads to wave breaking. After breaking the leftover wave energy is reformed and keeps propagating. A 3D dispersive numerical model is developed and utilized for the prediction of the wave reflection, diffraction, breaking, and propagation in the presence of varying water depth and an offshore oil rig. A finite difference method has been employed for the numerical computation that uses ADI (Alternating Direction Implicit) algorithm. In this work, a sea bottom of varying bathymetry is utilized extended from a deeper region to a shallower region may be treated as a natural or an artificial submerged obstacle or sea bottom. A square shaft of an oil and gas platform is located in the shallower region. In the simulation, a set of relevant regular and irregular waves are studied. Limited field data of irregular waves are used for model validation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score1.000

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)0.0010.000

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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Same venueOCEANS 2021: San Diego – PortoSame topicCoastal and Marine DynamicsFrench-language works237,207