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Record W4388588243 · doi:10.12716/1001.18.01.25

Use of Simulations to Predict Lifeboat Survivability in Extreme Waves and the Effectiveness of Coxswain Performed Actions

2023· article· en· W4388588243 on OpenAlexafffund
Randy Billard, Robert Rees, Brian Veitch, António Simões Ré

Bibliographic record

VenueTransNav the International Journal on Marine Navigation and Safety of Sea Transportation · 2023
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSetbackSurvivabilityLaunch vehicleRogue waveSignificant wave heightLaunchedMarine engineeringEnvironmental scienceAeronauticsMeteorologyWind waveEngineeringAerospace engineeringGeologyPhysicsOceanography

Abstract

fetched live from OpenAlex

Simulations were used to investigate the performance of lifeboats in high sea states using a virtual wave tank. Numerical simulations were performed in regular and irregular waves to study launch performance in extreme weather conditions. Limitations in launch equipment and the role of the timing of coxswains’ actions were investigated. The study indicated that the lifeboat may not be able to successfully launch when significant wave heights are above 8 m and the lifeboat is launched near the trough of a wave. High initial setback and continuous wave forces result in the vessel being unable to clear away from the launch platform. As wave heights increase, the amount of setback and time to exit the launch area increases. Over 35% of launches resulted in the lifeboat being unable to clear from the launch area when significant wave heights were 10 m or above. The study also identified that delay in completion of actions performed by the coxswain, such as releasing the lifeboat hooks and applying throttle, can increase setback and time to exit the launch area.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.265

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)0.0000.000

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 head0.034
GPT teacher head0.262
Teacher spread0.228 · 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 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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueTransNav the International Journal on Marine Navigation and Safety of Sea TransportationSame topicShip Hydrodynamics and ManeuverabilityFrench-language works237,207