HEAT TRANSFER OF IMPINGING SEAWATER SPRAY AND ICE ACCUMULATION ON MARINE VESSEL SURFACES
Bibliographic record
Abstract
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".