MétaCan
Menu
Back to cohort
Record W1999675783 · doi:10.1115/1.2426993

Ice Loads Acting on a Model Podded Propeller Blade (OMAE2005-67416)

2006· article· en· W1999675783 on OpenAlexafffund
Jungyong Wang, Ayhan Akintürk, Stephen J. Jones, Neil Bose, Moon-Chan Kim, Ho-Hwan Chun

Bibliographic record

VenueJournal of Offshore Mechanics and Arctic Engineering · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsNational Research Council CanadaMemorial University of Newfoundland
FundersPusan National UniversityTransport Canada
KeywordsPropulsorMarine engineeringPropellerSea iceArctic ice packArcticGeologyEngineeringOceanography

Abstract

fetched live from OpenAlex

Abstract With the increase in popularity of podded propulsors and arctic navigation, understanding the interaction between a podded propulsor and ice has become more important. Propeller-ice interaction itself is a complicated process with a high level of uncertainty resulting from the uncertainties associated with the properties of the ice and with the propeller-ice interaction conditions. Model tests provide relatively well-controlled ice properties and interaction conditions to reduce the uncertainties. In order to improve the understanding of this interaction and to develop numerical models of it, a model podded propulsor was used in “Puller” mode, and ice loads were measured on its instrumented blade and propeller shaft. The results of the experiments conducted to simulate the interactions (milling) of the instrumented blade with ice in different operating conditions are reported in this paper. Loads measured during the milling consist of ice milling loads, “inseparable” hydrodynamic loads, and “separable” hydrodynamic loads. The sample results presented here include ice milling and inseparable hydrodynamic loads for various advance coefficients and depths of cut (amount of blade penetration into ice). Some results are compared with existing ice load models.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.634

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 head0.008
GPT teacher head0.181
Teacher spread0.173 · 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

Citations24
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Offshore Mechanics and Arctic EngineeringSame topicArctic and Antarctic ice dynamicsFrench-language works237,207