Energy Budget of Liquid Drop Impact at Maximum Spreading: Numerical Simulations and Experiments
Bibliographic record
Abstract
The maximum spreading of an impinging droplet on a rigid surface is studied for low to high impact velocity, until the droplet starts splashing. We investigate experimentally and numerically the role of liquid properties, such as surface tension and viscosity, on drop impact using three liquids. It is found that the use of the experimental dynamic contact angle at maximum spreading in the Kistler model, which is used as a boundary condition for the CFD-VOF calculation, gives good agreement between experimental and numerical results. Analytical models commonly used to predict the boundary layer thickness and time at maximum spreading are found to be less correct, meaning that energy balance models relying on these relations have to be considered with care. The time of maximum spreading is found to depend on both the impact velocity and surface tension, and neither dependency is predicted correctly in common analytical models. The relative proportion of the viscous dissipation in the total energy budget increases with impact velocity with respect to surface energy. At high impact velocity, the contribution of surface energy, even before splashing, is still substantial, meaning that both surface energy and viscous dissipation have to be taken into account, and scaling laws depending only on viscous dissipation do not apply. At low impact velocity, viscous dissipation seems to play an important role in low-surface-tension liquids such as ethanol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".