MétaCan
Menu
Back to cohort
Record W1988166711 · doi:10.1115/1.1951770

Experimental Analysis of the Near Wake from a Ducted Thruster at True and Near Bollard Pull Conditions Using Stereo Particle Image Velocimetry (SPIV)

2005· article· en· W1988166711 on OpenAlexafffund
S El Lababidy, Neil Bose, Pengfei Liu, Dan Walker, Fabio Di Felice

Bibliographic record

VenueJournal of Offshore Mechanics and Arctic Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsMemorial University of Newfoundland
FundersNational Research Council Canada
KeywordsWakeParticle image velocimetryMarine engineeringSubmarine pipelinePropellerAerospace engineeringGeologyVelocimetryWater tunnelMechanicsEngineeringPhysicsTurbulenceGeotechnical engineeringVortex

Abstract

fetched live from OpenAlex

Thrusters working at low advance coefficients are employed in a wide range of offshore and marine applications on Floating, Production, Storage, and Offloading (FPSO) systems; shuttle tankers; tug boats; and mobile offshore units. Therefore, an understanding of the flow around the thrusters is of great practical interest. Despite this interest, there is lack of knowledge in the description of the hydrodynamic characteristics of a ducted thruster’s wake at bollard pull and low advance coefficient values. This work was aimed at providing detailed data about the hydrodynamic characteristics of a Dynamic Positioning (DP) thruster near wake flow at different low advance coefficient values. Wake measurements were made during cavitation tunnel tests carried out on a ducted propeller model at the Italian Ship Model Basin (INSEAN), Rome, Italy. Through these experiments, the DP thruster near wake velocity components at different downstream axial planes, up to 1.5 diameters downstream, were obtained using a Stereoscopic Particle Image Velocimetry (SPIV) system. These experiments were carried out at different advance coefficient (J) values [bollard pull (J=0), J=0.4 and J=0.45].

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 score0.612
Threshold uncertainty score0.519

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations7
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Offshore Mechanics and Arctic EngineeringSame topicCavitation Phenomena in PumpsFrench-language works237,207