Predicted Ice Accretion on Horizontal Surfaces of Marine Vessels and Offshore Structures in Arctic Regions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".