Effects of tip streaming on the prediction of droplet size distribution in the presence of dispersants during subsea blowouts
Bibliographic record
Abstract
Abstract (2017-193) With the presence of surfactants in the fluid mixture, tip streaming phenomenon often occurs where daughter droplets of micron or sub-micron size are ejected from thin threads of the droplet poles. Recent experimental and modeling studies of tip streaming phenomenon have been focusing on the formation of individual droplets. However, effects of tip streaming on the prediction of droplet formation during subsurface oil blowouts have not been thoroughly investigated. Due to the high intensity flow in the blowout, the amount of micron or sub-micron size droplets resulting from tip streaming could be substantial and cannot be ignored. In this study, a new empirical-numerical scheme is developed in the thoroughly-validated droplet formation model, VDROP-J, to account for the tip streaming phenomenon when dispersants are presence. Calibration of the new scheme and model validations are performed in association with the underwater oil jet experiments. The new model development improves the capability of VDROP-J model in application to the cases when dispersants are used, which would provide valuable information of droplet formation during subsea blowouts for decision makers and research groups.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".