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Record W2181509110 · doi:10.1115/omae2015-41304

Experimental Studies of Hydrodynamic Interaction of Two Bodies in Waves

2015· article· en· W2181509110 on OpenAlexafffund
Quan Zhou, Ming Liu, Heather Peng, Wei Qiu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSeakeepingTowingFree surfaceExperimental dataElevation (ballistics)HullFlow (mathematics)MechanicsMarine engineeringPotential flowBenchmark (surveying)GeologySurface waveStructural engineeringEngineeringPhysicsMathematicsOpticsGeodesy

Abstract

fetched live from OpenAlex

There are challenges in the prediction of low-frequency load and especially the resonant free surface elevation between two bodies in close proximity. Most of the linear potential-flow based seakeeping programs currently used by the industry over-predict the free surface elevation between the vessels/bodies and hence the low-frequency loadings on the hulls. Various methods, such as the lid technique, have been developed to suppress the unrealistic values of low-frequency forces by introducing artificial damping coefficients. However, without the experimental data, it is challenging to specify the coefficients. This paper presents the experimental studies of motions of two bodies with various gaps and the wave elevations between bodies. Model tests were performed at the towing tank of Memorial University. The objective was to provide benchmark data for further numerical studies of the viscous effect on the free surface predictions. The experimental data were compared with numerical solutions based on potential flow methods. The effect of tank walls were examined. Preliminary uncertainty analysis was also carried out.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.044
GPT teacher head0.327
Teacher spread0.283 · 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 designBench or experimental
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
Published2015
Admission routes2
Has abstractyes

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