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Record W2762157816 · doi:10.1115/omae2017-62193

Spray Cloud Formation Over Marine Vessels by a Water Breakup Model

2017· article· en· W2762157816 on OpenAlexaff
S. R. Dehghani, G.F. Naterer, Yuri S. Muzychka

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsMemorial University of Newfoundland
FundersStatoil
KeywordsBreakupMechanicsStripping (fiber)Reynolds numberMaterials sciencePhysicsTurbulenceComposite material

Abstract

fetched live from OpenAlex

Water breakup affects the variety of droplet sizes and velocities in a cloud of spray resulting from a sea wave striking a vessel bow. The Weber and Reynolds numbers of droplets are the main parameters for water breakup phenomena. “Stripping breakup” is a faster phenomenon than “bag breakup” and occurs at higher velocities and with larger diameters of droplets. A water breakup model employs droplet trajectories to develop a predictive model for the extent of spray cloud. The governing equations of breakup and trajectories of droplets are solved numerically. Stripping breakup is found as the major phenomenon in the process of the formation of wave-impact sea spray. Bag breakup acts as a complementary phenomenon to the stripping breakup. The extent of the spray as well as wet heights, for a Mediumsized Fishing Vessel (MFV), are obtained by numerical solutions. The results show that bag breakup occurs at higher heights. In addition, there is no breakup when droplets move over the deck.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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