Oil plume simulations: Tracking oil droplet size distribution and fluorescence within high-pressure release jets
Bibliographic record
Abstract
ABSTRACT Optical measurements have been used during oil spill response for more than three decades to determine oil presence in slicks and plumes. Oil surveillance approaches range from simple (human eyeball) to the sophisticated (sensors on AUVs, aircraft, satellites). In situ fluorometers and particle size analyzers were deployed during the Deepwater Horizon (DWH) Gulf of Mexico oil spill to track shallow and deep subsea plumes. Uncertainties regarding instrument specifications and capabilities during DWH necessitated performance testing of sensors exposed to simulated, dispersed oil plumes. Seventy-two wave tank experiments were conducted at the Bedford Institute of Oceanography. Simulated were oil releases with varying parameters such as oil release rate, oil temperature (reservoir temp ~ 80 °C), water temperature (<8 °C and >15 °C), oil type, dispersant type (Corexit 9500 and Finasol OSR52) and dispersant to oil ratio (DOR). Plumes of Alaskan North Slope Crude (ANS), South Louisiana Crude (SLC) and IFO-120 oils were tracked using in situ fluorescence, droplet size distribution (DSD), total petroleum hydrocarbons (TPH) and benzene-toluene-ethylbenzene-xylene (BTEX). For the lighter SLC, bimodal droplet size with mean diameter < 70 μm was achieved for 1:20 and 1:100 DOR, regardless of water temperature. Similarly, the medium ANS crude exhibited mean droplet diameter <70 μm, but was bimodal only for the 1:20 treatment. Bimodal distribution was not achieved with the heavy IFO, but droplet < 70 μm were observed for 1:20 warm waters, indicating poor dispersibility of the high viscosity oil even for jet releases. Results offer valuable information on the behavior and dispersibility of oils over a range of viscosity, DOR and environmental conditions. Findings have implications for fate and transport models, where DSD, chemistry and fluorescence are all impacted by release variables. This research was supported by the Bureau of Safety and Environmental Enforcement.
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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 | 0.001 |
| 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".