Predicting Impact Outcomes and Maximum Spreading of Drop Impact on Heated Nanostructures Using Machine Learning
Bibliographic record
Abstract
Accurate prediction of droplet behavior upon impact on a heated nanostructured surface is vital for various industrial applications. In this study, we leverage multiple data-driven machine learning (ML) techniques to model the impact outcome and droplet spreading, employing existing experimental data. Our approach incorporates a comprehensive range of critical control parameters, such as the impact velocity ( V ), surface temperature ( T s ), nanopillars’ packing fraction (ϕ), and surface roughness ( r ). We obtain optimal results when utilizing the artificial neural network classification (ANNC) to construct a phase diagram that encompasses all of the experimental impact behaviors. Additionally, we utilize the support vector regression (SVR) method to model the maximum spreading factor (β max ) as a function of the Weber number ( We ), defined as the ratio of droplet kinetic to surface energy, and T s for each surface combination. Consistent with previous experimental observations, our results illustrate that nanostructures not only introduce distinct impact behaviors, such as central jetting, but also influence the boundaries among the deposition, rebound, and splashing regimes within the phase diagram. An increase in ϕ at a constant r promotes deposition and spreading events, while increasing r at a constant ϕ results in enhanced heat transfer to promote the Leidenfrost effect for the rebound regime and a greater disturbance of the liquid lamella to trigger splashing. The SVR prediction reveals the existence of a We -number threshold governed by the nanostructure parameters. Beyond this threshold, the maximum spreading factor (β max ) of a spreading droplet becomes independent of the surface temperature ( T s ) as We increases, suggesting that fluid properties are likely the dominating factors influencing the spreading dynamics in the extreme We range.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".