Ship-Wave Impact Generated Sea Spray: Part 1 — Formulating Liquid Water Content and Spray Cloud Duration
Bibliographic record
Abstract
Abstract When a wave impacts a ship, a cloud of water spray may form. This spray water, in cold climates, significantly contributes to the deposition of icing on the ship. Estimation of the spray flux is a first step towards predicting the marine icing. The amount of spray water, termed as liquid water content (LWC), the time of ship exposure to the spray cloud in a spray event known as spray duration, and the frequency at which the spray is generated are all important parameters required to define the spray flux. Most of the spray flux formulas found in the literature are based on field observations of small fishing vessels. Moreover, they consider meteorological and oceanographic parameters only and ignore the characteristic behaviors of the vessel. These formulas are therefore not applicable to any size and type of vessel. This paper develops methods to quantify the spray properties in terms that can be applied to vessels of any size. Formulas to estimate two crucial spray properties, LWC and spray duration, are derived based on the energy conservation principles and by non-dimensional analysis. The formulas take into account the ship’s principal particulars, its operating conditions, and the environmental parameters. The formulas are validated against full-scale field measurement from a Russian fishing trawler, MFV Narva, and a medium-size US coast guard vessel, USCGC Midgett. Reasonable agreements are found in both cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".