Ship-Wave Impact Generated Sea Spray: Part 2 — Formulating Spray Frequency
Bibliographic record
Abstract
Abstract In certain, but not all, circumstances a cloud of spray forms after a wave impacts a ship. The frequency of spray events affects the icing process. Previous spray frequency formulas are derived empirically from field observations considering only the ship’s forward speed and oceanographic conditions. The significance of various degrees of ship motions on the spray frequency is ignored. However in reality, the interrelationships of heave and pitch motions under wave actions together with surge motion determine the number of spray events that a ship may experience in a given period of time. This paper introduces a theoretical model for estimating the frequency of sea spray considering ship motions. Ship motions can be easily estimated by strip/panel methods. However, in this work, the aim was to develop a simple framework for a quick estimate of spray frequency. The model inputs are, therefore, restricted to ship’s principal particulars, its operating conditions, and the environmental conditions. The wave-induced motions are estimated by semi empirical analytical expressions. A novel spray threshold is developed to keep the deck wetness frequency separated from the spray frequency. The proposed spray frequency formula is validated against available full-scale field measurements from a Russian fishing vessel, MFV Narva, and reasonable agreement is found. Limitations of previous empirical formulas are also discussed.
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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 | 0.000 | 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) | 0.001 | 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 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".