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Record W2761156471 · doi:10.1115/omae2017-61137

Improving the Panel-Free Method for the Prediction of Ship Motions and Wave Induced Loads

2017· article· en· W2761156471 on OpenAlexafffund
Wei Qiu, Heather Peng, Junshi Wang, Shahriar Nizam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputationShip motionsResponse amplitude operatorComputer scienceSeakeepingMarine engineeringSimple (philosophy)Control theory (sociology)EngineeringAlgorithmControl (management)Artificial intelligenceHull

Abstract

fetched live from OpenAlex

Frequency-domain methods are proven efficient and reliable, especially for zero forward speed, in early design stage for the prediction of ship motions and wave-induced wave loads. There are still challenges for ships with forward-speed due to the inaccuracy in the computation of m-terms. In this paper, the panel-free method is further improved to predict motions and wave-induced loads on real ships with forward speeds. A simple algorithm has been developed to re-arrange the control points for Non-Uniform Rational B-Splines (NURBS) surfaces. This method led to reliable and accurate m-term computations and therefore improved ship motion and load predictions. Validation studies have been carried out for a hydroelastic model of a frigate. Computed motions and loads were compared with experimental data.

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 score0.914
Threshold uncertainty score0.199

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 head0.059
GPT teacher head0.266
Teacher spread0.206 · 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
Published2017
Admission routes2
Has abstractyes

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