Simulation of droplet impacting a square solid obstacle in microchannel with different wettability by using high density ratio pseudopotential multiple-relaxation-time (MRT) lattice Boltzmann method (LBM)
Bibliographic record
Abstract
In this paper, a pseudopotential high density ratio (DR) lattice Boltzmann model was developed by incorporating multi-relaxation-time collision matrix, large DR external force term, surface tension adjustment external force term, and solid–liquid pseudopotential force. It was found that the improved model can precisely capture the two-phase interface at high DR. Besides, the effects of initial Reynolds number, Weber number, solid wall contact angle (CA), ratio of obstacle size to droplet diameter (χ1), and ratio of channel width to droplet diameter (χ2) on the deformation and breakup of a droplet when impacting on a square obstacle were investigated. The results showed that with the Reynolds number increasing, the droplet will fall along the obstacle and then spread along both sides of the obstacle. Furthermore, by increasing Weber number, the breakup of the liquid film will be delayed and the liquid film will be stretched to form an elongated ligament. With decreasing of the wettability of solid particle (CA → 180°), the droplet will surround the obstacle and then detach from the obstacle. When χ1 is greater than 0.5, the droplet will spread along both sides of the obstacle quickly; otherwise, the droplet will be ruptured earlier. Furthermore, when χ2 decreases, the droplet will spread earlier and then fall along the wall more quickly; otherwise, the droplet will expand along both sides of the obstacle. Moreover, increasing the hydrophilicity of the microchannel, the droplet will impact the channel more rapidly and infiltrate the wall along the upstream and downstream simultaneously; on the contrary, the droplet will wet downstream only.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".