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Record W2090188580 · doi:10.1115/omae2006-92040

Motion Response of a Full-Scale Life Raft in Laboratory Tow Experiments

2006· article· en· W2090188580 on OpenAlexaff
Lawrence Mak, Antonio Simo ̃es Re ́, Andrew Kuczora

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBallastMarine engineeringAccelerationRaftEngineeringStructural engineeringGeotechnical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

A 16-person full-scale life raft was towed in a tow tank in calm water, regular and irregular waves. The objectives were to assess the raft motion response, occupant motion, tow force, effect of tow speed, effects of different test variables (drogue deployment, floor inflation, weight distribution and ballast), and the likelihood of occupant motion sickness. Comparisons of RAOs obtained in regular and irregular waves demonstrated that irregular waves could be used as a cost effective means to determine raft response with a high degree of confidence. They also show that the life raft tow performance is different in waves than in calm water. For example, mean tow force is 20% higher in the sea state tested than in calm water. Floor inflation, drogue deployment, even weight distribution and tow speed increase mean tow force and tow force variation about its mean. The data also show that the same ballast types should be used to access the effects of different variables because manikin and water bag ballast produce different results. Measured occupant heave acceleration was about the same as the raft heave acceleration. From occupant heave acceleration, it was estimated that after 20 hours in the raft, 20% of occupants would vomit. Formulae were proposed to predict tow force in different sea states. Mean tow forces predicted using calm water tow resistance and RAOs derived from regular wave tow tests compared well with measured mean tow force in irregular waves.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.278

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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