Dynamics of surfactant-laden drops in shear flow by lattice Boltzmann method
Bibliographic record
Abstract
We developed and applied a diffuse interface lattice Boltzmann method for simulating immiscible liquids with soluble surfactants using a modified Ginzburg–Landau free energy functional. We first validated the approach through simulations of planar interfaces and drop equilibration in quiescent fluid. The proposed method accurately captures the phase and surfactant fields with diminishing spurious velocities of 10−6. We systemically examined the effects of capillary number, comparing viscous to surface forces, the combined effect of surfactant and viscosity ratio (λ) of the drop to the continuous phase, and the bulk surfactant load on the deformation and breakage in a shear flow. At a given capillary number (0.05<Ca<0.32), drop behavior is influenced by reduced surface tension, tip-stretching, Marangoni stresses, and surface dilution. These effects either promote (by tip-stretching) or hinder (via Marangoni stresses, surface dilution) the surfactant distribution at the interface, consequently affecting the final drop morphology. As Ca increases, the competition between the viscosity ratio and the presence of surfactant determines drops' topological changes. The presence of surfactants can overcome the effect of viscosity ratio (when 0.05≤λ≤1.7) and promote drop breakup, whereas highly viscous drops (either λ<0.05 or λ>1.7) do not break. Furthermore, high surfactant loads result in higher drop deformation and earlier drop breakup. In brief, our method successfully captures the dynamics of surfactant-laden drops in shear flow, elucidating the complex interplay between flow hydrodynamics and surfactant transport with 3D quantitative phase and surfactant concentration fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".