Size and velocity correlation for splashing droplets generated by jet impingement
Bibliographic record
Abstract
Abstract Municipal drainage systems can transmit harmful microorganisms such as COVID-19 virus through wastewater as well as drain airflow with suspended tiny bio-droplets. The generation of tiny droplets in drainage systems can be simplified as the phenomenon of jet impingement and splashing, which is also common in industries, but the size and velocity correlation and the kinetic energy of splashing droplets remain unclear. This paper uses high-speed photography to study splashing from a jet impinging on a horizontal plate. Since the jet can break into successive drops before impingement, successive drops impingement and continuous jet impingement are defined and their splashing modes are revealed. Successive drops impingement and continuous jet impingement respectively produce splashing droplets with sizes smaller than 0.25 and 0.3 times the size of the impact drops and jet, and with the velocities up to 3.0 and 1.5 times the impact velocity. Correlations between size and velocity of splashing droplets are established: the range and maximum of velocity increase as diameter decreases, and the high velocity splashing droplets have ejection angles within 10° to 20°. For successive drops impingement, the percentage of total kinetic energy of splashing droplets relative to impact kinetic energy increases with impact Weber number and can reach nearly 50%, however, it varies little with impact Weber number for continuous jet impingement, reaching only 10%. Successive drops impingement produces more splashing droplets characterized by a combination of smaller size, higher velocity and larger ejection angle, resulting in a greater risk of producing airborne microorganisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".