Experimental Investigation of Vertical Marine Surface Icing in Periodic Spray and Cold Conditions
Bibliographic record
Abstract
In this paper, the ice load accumulated on a vertical plate of marine platforms during periodic spray icing in a cold room was investigated experimentally. The mass and thickness of ice formation on the plate along with several parameters such as relative humidity, the front and back surface temperatures of the vertical plate, initial temperature of water, and the spray mass flux impinging on the plate were measured and discussed. Analysis of variance (ANOVA), which is a statistical data analysis method, was utilized to interpret the contribution of the investigated parameters during the icing experiments, comparing the effect of each parameter and their interactions on the quantity of ice accumulated on the vertical plate. The primary analysis of the empirical results illustrates that the ambient temperature, airflow velocity, the distance between the fan and the plate, salinity and the timing of spray events have influences in the icing intensity and the amount of ice formation on the vertical plate. The errors between the average ice thicknesses obtained from two different experimental approaches were from 5 to 20%. For the saline ice formation, the temperature difference between the front and back of the vertical plate was greater than that of the pure ice formed during the spray icing event. The primary experimental results alongside the ANOVA analysis verified that airflow velocity is the most effective parameter, with a high level of interaction for time and temperature.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".