State-of-the-Art Review of Research on Ice Accretion Measurements and Modelling
Bibliographic record
Abstract
Abstract Ice accretion on vessels and offshore structures is a serious operational hazard. Heavy ice accumulation on vessel's deck can influence the stability and potentially capsize smaller vessels. It also creates unsafe operational conditions on vessel and offshore platform decks. Accurate estimation of loads due to ice accretion is thus necessary. This paper presents a state-of- the-art review of existing literature related to ice accretion on vessels and offshore structures. Existing reports for field measurements of icing are reviewed. Model scale experiments to simulate the ice accretion are also reviewed. Various numerical methods and approaches for ice accretion prediction are critically reviewed. The review covers some key factors of the icing process such as spray flux and heat transfer models. There are few international codes and standards available that require the ice-going ships to have adequate intact stability, taking into consideration a prescribed level of icing on exposed weather decks, gangways, and projected lateral areas of the superstructure. The codes were developed based on the limited population of ships and ship types and were focused on specific geographic regions. In fact, there is little agreement among the codes. Moreover, the formulae rely mostly on data collected for smaller vessels, and extrapolation to the larger vessels used in oil and gas operations are questionable. The paper gives a brief review of the international codes and critiques the inadequate standard for ice accretion calculation for various types and sizes of vessels. The paper concludes by discussing some aspects of improved ice accretion prediction models that may be particularly relevant for larger 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".