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Record W2330940521 · doi:10.1115/omae2015-41306

Analysis of Ice Accretion on Vertical Surfaces of Marine Vessels and Structures in Arctic Conditions

2015· article· en· W2330940521 on OpenAlexaff
Alireza Dehghani-Sanij, Yuri S. Muzychka, G.F. Naterer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIcingEnvironmental scienceHeat transferSea sprayMaterials scienceMass fluxFlow (mathematics)MechanicsAccretion (finance)Multiphase flowHeat fluxArcticMarine engineeringGeologyMeteorologyOceanographyEngineeringAerosolPhysics

Abstract

fetched live from OpenAlex

The phenomenon of icing in cold climates is a challenging problem of engineering analysis, which involves heat transfer, phase change and multiphase flow with water droplets. This phenomenon has an important impact on the performance and operation of marine vessels, offshore structures, and others such as wind turbines, power lines, and aircraft surfaces. In this paper, a predictive icing model for large vertical surfaces of a marine vessel is developed theoretically. The total flux of sea-spray, including wave spray and wind spray, is analyzed during the spray process. By using heat, mass and salt concentration balances, the freezing fraction, temperature distribution, ice layer thickness, and liquid film thickness are determined. The results are compared with the numerical and experimental results of other studies. Good agreement between the theoretical predictions and other results demonstrates the improved accuracy of the proposed method over past 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.155

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 head0.017
GPT teacher head0.254
Teacher spread0.236 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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