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Record W4400734085 · doi:10.3390/jmse12071195

Smoothed Particle Hydrodynamics Modelling of Bergy Bit and Offshore Structure Interactions Due to Large Waves

2024· article· en· W4400734085 on OpenAlexaff
Mohammed Islam, Tanvir Sayeed

Bibliographic record

VenueJournal of Marine Science and Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSmoothed-particle hydrodynamicsMechanicsPhysicsParticle (ecology)Bit (key)ViscositySubmarine pipelineWork (physics)GeologyGeotechnical engineeringComputer science

Abstract

fetched live from OpenAlex

This research utilised an open-sourced smoothed particle hydrodynamics (SPH) tool to model and predict the change in wave-induced forces and motions of a free-floating bergy bits approaching a fixed structure in regular waves. Simulation parameters, including particle resolution, fluid viscosity, initial wave condition and boundary treatments, are varied, and their effect on the load imparted to the bergy bit and the structure are investigated. The predicted motions are compared with previously published physical measurements for corresponding scenarios. Both predictions and measurements showed that, in regular waves, the surge motion slowed as the bergy bit approached the structure, and the heave motion increased. For wave loading on bergy bits, the agreement with the experimental data for the root mean square (RMS) force was within 2%. The pressure and velocity fields of the wave–structure–bergy bit interactions are discussed in light of the SPH predictions. This work confirms that the SPH model can accurately capture viscosity–dominated interactions, hydrodynamic damping, and eccentric impact like phenomena and predict both the impact and hydrodynamic loads due to a bergy bit drifting in waves towards a fixed offshore 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 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 score0.098
Threshold uncertainty score0.290

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 communication0.0000.001
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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