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Record W2536246599 · doi:10.1115/omae2016-54054

Predicted Ice Accretion on Horizontal Surfaces of Marine Vessels and Offshore Structures in Arctic Regions

2016· article· en· W2536246599 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
KeywordsIcingSubmarine pipelineArctic ice packWind speedSea sprayEnvironmental scienceGeologySea iceSeawaterMass fluxHeat fluxArcticTemperature salinity diagramsSalinityAtmospheric sciencesOceanographyMeteorologyMechanicsHeat transferAerosol

Abstract

fetched live from OpenAlex

Sea spray icing is one of the most significant problems for the operation of marine vessels and offshore structures in Arctic regions. This phenomenon affects the stability of marine vessels and offshore structures, and also the safety of human activities onboard. In this paper, a new predictive icing model for large horizontal surfaces of a marine vessel is developed. To obtain the total flux of sea spray during the icing process, both wind spray and wave spray are considered. By applying heat, mass and salt concentration balances, the freezing fraction, temperature distribution, ice layer and water film thicknesses are determined. Moreover, the effects of different parameters on the freezing fractions at various air temperatures are investigated. The results indicate that air temperature, wind velocity, vessel speed, spray water salinity, height from the water surface, and angle between the vessel heading and wind/wave direction are major parameters in the growth rate of the ice. This theoretical method provides a reasonably accurate and simple way for predicting the sea spray icing phenomena on marine vessels and offshore structures.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.219

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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