Penetration dynamics of non-Newtonian fluids into axially varying capillaries
Bibliographic record
Abstract
The capillary-driven penetration of non-Newtonian fluids in capillaries with irregular walls is crucial in industrial applications, such as anode manufacturing for aluminum production, where a mixture of coal-tar pitch and fine petroleum coke particles (binder matrix) impregnates the open pores of coarse coke particles. Our study presents a semi-analytical model for capillary-driven flow of shear-thinning fluids in axially varying, wavy-walled microchannels, representative of coke open pore geometries. Incorporating weak inertia, viscous dissipation, and dynamic contact angle behavior (governed by a molecular kinetic theory), the model is systematically derived using lubrication theory and a power-law rheology, yielding a reduced-order equation for the advancing meniscus. The model is validated and calibrated via computational fluid dynamics simulations to extract the dynamic contact angle correction parameter. Our analysis quantifies three distinct penetration regimes and their transition dynamics: inertia-dominated, interfacial dissipation-dominated, and viscous dissipation-dominated. Analytical scaling laws and regime transition correlations are validated across varying power-law indices, Laplace numbers, contact angles, and geometrical features. The power-law index most strongly influences penetration, followed by static contact angle and geometric phase shift, while Laplace number affects early-time behavior. Dynamic contact angle analysis highlights the critical role of interfacial dissipation in irregular geometries. Applied to binder matrices with measured rheology, the model shows that increased fine coke content or channel irregularity significantly delays impregnation.
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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) | 0.000 | 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 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".