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HEAT TRANSFER OF IMPINGING SEAWATER SPRAY AND ICE ACCUMULATION ON MARINE VESSEL SURFACES

2017· article· en· W2738747060 on OpenAlexaff
Alireza Dehghani-Sanij, G.F. Naterer, Yuri S. Muzychka

Bibliographic record

VenueHeat Transfer Research · 2017
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSea ice growth processesIcingThermal conductionHeat transferBrineMaterials scienceSeawaterSalinityHeat fluxThermal conductivityEnvironmental scienceSea ice thicknessSea iceMechanicsGeologyAtmospheric sciencesThermodynamicsArctic ice packComposite materialOceanography

Abstract

fetched live from OpenAlex

In this paper, a new predictive model for the ice layer and water film growth, which occurs due to seawater spray impinging on large horizontal surfaces of a supply vessel, is developed using a Stefan-type problem formulation. The icing model includes conduction heat transfer in the ice and brine film layer, assuming the volume and distribution of brine pockets and air bubbles within the ice accumulation are uniform. The model also uses heat and mass balances to predict the freezing fraction, temperature distribution, ice layer and water film thickness. The results show that the water film salinity and icing intensity change with time during the icing period. Additionally, the water film salinity variations affect the freezing temperature, thermal conductivity and specific heat capacity of ice formation. As a result, heat conduction within the accumulated ice changes with time due to the variations of salinity; thus, the conduction heat flux has a significant effect on the ice thickness growth rate. This new model is a useful tool for forecasting and assessing the potential ice accumulation on marine vessels and 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)0.001
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 score0.050
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.108
GPT teacher head0.362
Teacher spread0.254 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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