Maximum spreading of a ferrofluid droplet under the effect of magnetic field
Bibliographic record
Abstract
This study presents a theoretical and an experimental study of the effects of an applied external magnetic field on the maximum spreading of a ferrofluid droplet impacting on a solid substrate. Although many studies have explored the theoretical modeling of the droplet impact scenario, a theoretical model representing the impact of ferrofluid droplets of different magnetic characteristics, strongly affected by the magnetic field, is yet to be addressed. In this study, we developed a theoretical model based on the principle of the conservation of energy to predict the maximal deformation of both diamagnetic and paramagnetic ferrofluid droplets upon impact under the influence of the magnetic field. The physics behind the variation of maximum drop spread, as a function of Weber number (We), Reynolds number (Re), and magnetic Bond number (Bom) for 5–45, 150–400, and 150–3000, respectively, was studied. By validating the theoretical model with the experimental observations, we demonstrated that the proposed theoretical model could successfully predict experimental observations. Through theoretical analysis and extensive experimental investigations, a rational understanding was formulated which allowed us to comment on the effect of all the governing dimensionless numbers (We, Re, and Bom) on the maximum spreading of a ferrofluid droplet upon impact.
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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 | 0.000 | 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".