New Generalized Viscosity Model for Non-Colloidal Suspensions and Emulsions
Bibliographic record
Abstract
The viscous behavior of solids-in-liquid suspensions and liquid-in-liquid emulsions of non-Brownian solid particles and liquid droplets dispersed in Newtonian liquids is thoroughly discussed and reviewed. The full concentration range of the dispersed particles/droplets is covered, that is, 0<ϕ<ϕm, where ϕ is the volume fraction of inclusions (particles or droplets) and ϕm is the maximum packing volume fraction of inclusions. The existing viscosity models for suspensions and emulsions are evaluated using a large pool of experimental viscosity data on suspensions and emulsions. A new generalized model for the viscosity of suspensions and emulsions is proposed and evaluated. The model takes into consideration the influence of shear-induced aggregation of particles and droplets. It also includes the effect of the droplet-to-matrix viscosity ratio λ on the viscosity of emulsions. In the limit of high ratio of droplet viscosity to matrix viscosity (λ→∞), the model reduces to the suspension viscosity model. The proposed model uncovers some important and novel characteristics of suspension systems rarely discussed heretofore in the literature. The model is validated using twenty sets of experimental viscosity data on solids-in-liquid suspensions and twenty-three sets of experimental viscosity data on liquid-in-liquid emulsions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".