THEORETICAL FOUNDATION FOR PREDICTING DISPERSION EFFECTIVENESS DUE TO WAVES
Bibliographic record
Abstract
ABSTRACT The studies of dispersion of oil in wave tanks have reached their maturity in terms of analytical techniques for measuring and quantifying dispersion. However, there does not seem to be a theoretical framework for predicting or even interpreting the results based on the physics of the problem. One of the reasons is that the oil breakup studies were based on chemical reactors where the energy input is constant with time whereas the energy input to droplets varies with time under waves. For this reason, we present a holistic approach that accounts for the duration over which the oil is subjected to various intensities along with a droplet kinetics model that uses a variable energy dissipation rate function. A salient advantage of the droplet model is that it accounts for the effects of scale of problem, because it has been observed that large systems produce smaller droplets than smaller systems with the same average kinetic energy dissipation rate. We illustrate the usage of the model using simulated wave 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".