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Record W4224276224 · doi:10.3390/jmse10040522

A Measurement System to Monitor Propulsion Performance and Ice-Induced Shaftline Dynamic Response of Icebreakers

2022· article· en· W4224276224 on OpenAlexafffundabout
Alessandro Zambon, Lorenzo Moro, Jeffrey Brown, Allison Kennedy, Dan Oldford

Bibliographic record

VenueJournal of Marine Science and Engineering · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsNational Research Council CanadaMemorial University of Newfoundland
FundersNational Research Council CanadaNewfoundland and LabradorAmerican Bureau of Shipping
KeywordsPropulsionMarine engineeringCoast guardPropellerMarine propulsionThrustTorqueEngineeringAerospace engineeringAutomotive engineeringEnvironmental science

Abstract

fetched live from OpenAlex

Polar navigation entails challenges that affect the continuation of ship operations in severe ice conditions. Due to ice-propeller interaction, propulsion shafting segments are often at a high risk of failure. Efficient methods for shaft line design are hence needed to ensure the safety of ice-going vessels and propulsion reliability. To this end, full-scale measurements have proven essential to support the development of ship-design tools and updated safety regulations for ice-going vessels. This paper presents a unique integrated measurement system that employs measuring equipment to monitor Polar-Class vessel performance and shaft line dynamics during ice navigation. The system was installed on board the Canadian Coast Guard (CCG) icebreaker Henry Larsen. This experimental concept aims to monitor the shaft’s torque and thrust fluctuations during ice navigation to obtain information about the ship’s propulsion efficiency. In the paper, we describe the arrangement of the measurement system and the components it features. Finally, we present preliminary datasets acquired during two icebreaking expeditions. This work is framed into a broader research project, which includes the long-term objective to determine a correlation between sea ice conditions and the dynamic response of shaft lines.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.195
Teacher spread0.187 · 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 designObservational
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
Published2022
Admission routes3
Has abstractyes

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