Hydrodynamic Interactions Among Bubbles, Drops, and Particles in Non-Newtonian Liquids
Bibliographic record
Abstract
The understanding of hydrodynamic forces around particles, drops, or bubbles moving in Newtonian liquids is modestly mature. It is possible to obtain predictions of the attractive–repulsive interaction for moving ensembles of dispersed particulate objects. There is a certain intuition of what the effects of viscous, inertial, and surface tension forces should be. When the liquid is non-Newtonian, this intuition is gone. In this review, we summarize recent efforts at gaining fundamental understanding of hydrodynamic interactions in non-Newtonian liquids. Due to the complexity of the problem, most investigations rely on experimental observations. However, computations of non-Newtonian fluid flow have made increasingly significant contributions to our understanding of particle, drop, and bubble interactions. We focus on gravity-driven flows: rise or sedimentation of single spheroidal objects, pairs, and dispersions. We identify the effects of two main rheological attributes—viscoelasticity and shear-dependent viscosity—on the interaction and potential aggregation of particles, drops, and bubbles. We end by highlighting the open questions in the subject and by suggesting future directions toward the fundamental physics as well as applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".