A catastrophe phenomenon produced by impact of drop trains
Bibliographic record
Abstract
The impact of drop trains is widespread in industrial and agricultural applications, as well as in nature, making it crucial to investigate. In this study, the impact of drop trains on solid surfaces is experimentally investigated using a high-speed camera. A catastrophe phenomenon that had previously been overlooked is discovered: with the successive impact of drop trains, the impact result undergoes a discontinuous catastrophe, from a thin film impact generating the crown splash to a thick film impact generating the Worthington jet. The thickness of the thin film is less than 0.23 times the impact drop's diameter, while the thickness of the thick film ranges from 0.52 to 1.05 times the impact drop's diameter. The reason for the catastrophe is revealed from a phenomenological perspective. The number of impact drops and the impact Weber number are important factors determining the occurrence of catastrophe, and the critical number of impact drops for the catastrophe is linearly and positively correlated with the impact Weber number. Based on the cusp catastrophe theory, a catastrophe threshold model for drop train impact is established. This model is able to predict the threshold for the occurrence of catastrophe and provide a method for identifying the thin film stage, the thick film stage, and the transient catastrophe stage between these two stages. The catastrophe threshold model achieves the identification accuracy of 83.48%, 91.72%, and 77.50% for the total measured data, the thin film stage, and the thick film stage, respectively, indicating its good performance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".