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Record W2065544603 · doi:10.1115/omae2014-24015

Numerical and Experimental Research on a Podded Propulsor

2014· article· en· W2065544603 on OpenAlexfundno aff
Mohammed Islam, Ron Ryan, David Molynuex

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsnot available
FundersAtlantic Canada Opportunities Agency
KeywordsPropulsorPropellerReynolds-averaged Navier–Stokes equationsThrustMarine engineeringTorquePropulsive efficiencyOpen waterAdvance ratioEngineeringComputational fluid dynamicsAerospace engineeringMechanicsBlade pitchPhysicsTurbine

Abstract

fetched live from OpenAlex

This paper presents methodologies and some results of a numerical and experimental program to evaluate the effects of static azimuthing conditions on the propulsive characteristics of a puller podded propulsor in open water. In the experimental effort, the model propulsor was instrumented to measure thrust, torque and rotational speed of the propeller, and three orthogonal forces and moments, and azimuthing angle of the pod. The experimental results included the bare propeller (ahead only) and the combined propeller and pod over a range of advance coefficients at various static azimuthing angles in the range of −180° to 180°. A complementary numerical study is being carried out to predict the hydrodynamic forces of podded propulsor in static azimuthing conditions. A Reynolds-Averaged Navier Stokes solver is used to predict the propulsive performance of the bare propeller as well as the podded propulsor system. The thrust and torque for the bare propeller were compared to the corresponding measurements. The propeller thrust and torque as well as the loads on the pod in straight-ahead condition and at static azimuthing angles were then compared with the measurements. Preliminary analysis demonstrates that the RANS solver could predict the performance coefficients of the bare propeller as well as the podded propulsor in straight-ahead and static azimuthing angles in puller configurations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.244

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 head0.044
GPT teacher head0.336
Teacher spread0.292 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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