Oil droplets dispersion under a deep-water plunging breaker: Experimental measurements and numerical modeling
Bibliographic record
Abstract
Abstract (1141370) Wave tank experiments were performed to measure the droplets size distribution under the plunging breaking wave. A deep-water plunging breaker of height 20 cm was generated using the dispersive focusing method, and a shadowgraph camera was used to take images of droplets and bubbles of different sizes. For droplets smaller than the 1000 microns, the number-based DSD matched the DS correlation (Delvigne and Sweeney 1988), which gives N(d) ~ d−2.3, but N(d) ~ d−9.7 for diameters larger than 1000 microns. A numerical method was designed to study the oil dispersion under breaking waves by coupling the computational fluid dynamic (CFD) with the Lagrangian particle tracking code (NEMO3D) and population balance model (VDROP). The wave hydrodynamics was reproduced using the Reynolds-averaged Navier Stokes approach within a commercial CFD code ANSYS Fluent. The obtained wave hydrodynamics was then used as inputs for the NEM3D code and VDROP model. The numerical results show reasonable agreement with our experimental observation. The approach adopted to produce the DSD reduces the empiricism of the DS correlation, as the approach uses oil properties and measurable wave properties. The proposed numerical method was ready to be used in other scenarios of oil spills (i.e., oil jets in deep oceans and oil dispersion in riverine systems). It could also be potentially used in large scale forecast and hindcast simulations for oil spill response and research.
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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 | 0.000 |
| 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 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".