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Record W2969750469

A flat wave-piercing bow concept for high speed monohulls

2003· article· en· W2969750469 on OpenAlexvenueno aff
David Molyneux, G. Tam

Bibliographic record

VenueNPARC · 2003
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
Fundersnot available
KeywordsBow waveGeologyAcousticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

High-speed monohulls are known to experience excessive motions and structural loads caused by accelerations and slamming when operating in heavy weather. This paper presents a concept of a new wave-piercing bow designed to reduce adverse motions and structural loads. That goal has been achieved by introduction of a bow form that features upper surfaces shaped to generate downward lifting forces, which counterbalance the displacement forces that lift the bow up while moving through a wave and initiate pitching motion; counterbalancing these forces stabilizes the hull. Extensive model testing has been carried out on several models between Deceber 2000 and March 2002. Resistance forces, accelerations and bow pressures were recorded and used to define critical loading cases, subsequently used in a global finite element analysis of the structural arrangement of a generic 165ft Gulf of Mexico crew boat, its scantlings determined using direct approach under ABS High Speed Craft Guide. The research indicated a potential for significant reduction of motions, structural loads, scantlings, structural weight and power requirements.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.043
GPT teacher head0.244
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 source (direct Gemma or distilled Codex), 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
Published2003
Admission routes1
Has abstractyes

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