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Record W2497539652 · doi:10.5957/jsr.2016.60.2.61

Escort Tug Performance Prediction Using Computational Fluid Dynamics

2016· article· en· W2497539652 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Ship Research · 2016
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputational fluid dynamicsTowingHullThrustRange (aeronautics)MechanicsFlow (mathematics)Scale (ratio)Marine engineeringSimulationEngineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, we outline and validate a computational fluid dynamics (CFD) method for determining the hydrodynamic forces of an escort tug in indirect towing mode. We consider a range of yaw angles from 0° to 90° and a travel speed of 8 knots. We discuss the effects of scaling on prediction of flow separation and hydrodynamic forces acting on the vessel by carrying out CFD studies on both model and full-scale escort tugs performing indirect escort maneuvers. As the escort performance in terms of maximum steering forces is strongly dependent on the onset of flow separation from the hull and skeg of the tug, the model-scale simulations under-predict the maximum steering force by 12% relative to the full-scale simulations. In addition, we provide a method for converting the hydrodynamic forces of the CFD escort study into towline and thrust forces.

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.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.063
GPT teacher head0.324
Teacher spread0.261 · 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