A Scaling Model for Droplet Characteristics in a Spray Cloud Arising From Wave Interactions With Marine Objects
Bibliographic record
Abstract
The objective of this study was to develop a model for predicting spray droplet size and velocity distributions during spray cloud formation arising from a wave impact with an object. The study looked at scaling issues and developed a scaling model to relate the spray characteristics measured in a tow tank to large-scale spray formations arising from wave impact with vessels. Several phenomena related to spray scaling have been studied to develop the scaling rules in large enclosures. These are wave theories for deep water, air entrainment process during the wave impact, water sheet disintegration and droplet size distribution as well as the scaling of two-phase flow interfaces (water/air). The focus of this study was on atomization and particle motions, and the thermodynamic part of scaling was ignored. The formation of upstream droplets caused by a wave impact on the bow of a vessel is the result of sheet and droplet breakup. Scaling models related to the process of air entrainment, which is caused by the wave impact, water sheet breakup, and spray cloud formation, were investigated to implement a comprehensive scaling model. A mathematical formulation, considering the aforementioned phenomena, was developed to calculate the final average droplet diameter and maximum run-up velocity. The effects of initial wave characteristics, the geometrical characteristics of the water sheet at the moment of water impact, and a spray parameter, on the final average droplet diameter were investigated. Predictions of wave characteristics and final droplet diameter are compared with previously published field observation data.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".