Acoustic-optical Investigation of water droplet xxplosion in hot oil: kitchen fire prevention
Bibliographic record
Abstract
Deep-frying in hot oil is a common cooking activity; however, dangerous kitchen fires often occur from accidental oil spattering. Moreover, the resulting spray can be source of indoor air pollution. The underlying mechanisms of the oil droplet ejection and the transport are not well understood in addition to the complex multiphase acoustics. In this preliminary study, we focus on the rapid ejection of these oil droplets following a bubble bursting at the oil surface. The initial formation of small droplets follows from a Rayleigh-Taylor instability for a water droplet immersed in hot oil (180-195 C) transitioning to a splash phase with spray dispersal. The droplet dynamics, as a function of stand-off distance, are visualized by high speed video (Photron, Fastcam SA-5) synchronized with a microphone (Earthworks, QTC 40). Characteristic acoustic signatures of the oil film expansion into a bag and subsequent atomization are quantified. Droplet spray velocities and size distributions are determined optically and compared with the spectral acoustic content. A long term objective is the development of an inexpensive acoustic detection system for preventing cooking fires and monitoring the indoor air quality in commercial and household kitchens.
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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".