Silicone Based Icephobic Coating Assessment Under Offshore Winter Conditions
Bibliographic record
Abstract
Offshore exploration and shipping activity related to mining in northern territories are now really active, though these areas are concerned by harsh weather conditions. The offshore structures are subjected to atmospheric and marine icing phenomenon where heavy ice accumulations may increase the danger for workers and may cause failure in apparatus, detectors and safety devices. Several thermal and mechanical de-icing methods exist for some parts of the structure; however the use of efficient icephobic coating could reduce energy consumption and improve the safety level of workers. But, the question remains: how do we assess the performance of the icephobic coating? Moreover, what is the level of icephobicity needed to claim it achieves its role? To answer these interrogations, a complete in-lab evaluation procedure of the coatings performance subjected to offshore harsh winter conditions is suggested. The evaluation method consists of two steps, firstly ice adhesion tests, under both atmospheric icing and, sea water icing, and re-evaluation after numerous icing-de-icing cycles and UV degradation. The second step consists of ice accumulation tests, under sea spray icing and interaction spray icing. In order to validate this evaluation procedure, a silicone-based icephobic coating has been evaluated. Results show significant reductions in ice adhesion, by comparison in centrifuge adhesion testing, and accumulation, by compared to the mass on bare metal samples. A chart has been introduced to present concise results allowing a fair comparison between different coatings, in order to select the best. Several other parameters can also be used to evaluate the effectiveness of the icephobic, such as the effect of cold temperature, effect of corrosion, rain erosion, sand erosion and even its effect on environment. Finally, testing has been carried out to demonstrate an ice self-shedding of a coating under offshore conditions.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.003 | 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 teacher head, 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".