Flow of Boger fluids through packed bed of spherical and non‐spherical particles: Asymptotic behaviour
Bibliographic record
Abstract
Abstract The effect of fluid elasticity on frictional pressure drop during the flow of Boger fluids (constant viscosity elastic fluids) through packed bed of spherical and non‐spherical particles has been examined and comprehensive data on pressure drop–flow rate behaviour were generated using spheres, cylinders, triangles, and square plates as packing material to cover the sphericity range of 0.547 ≤ ɸ ≤ 1. Boger fluids used in the present study were 0.01%–0.045% wt./vol. polyacrylamide in glycerol water blends. The phenomenon of drag enhancement was observed in comparison to purely viscous fluid which follows asymptotic behaviour with elasticity number ( E = We/Re mod ). The asymptotic behaviour depicts the dominance of elasticity at low elasticity number and effect of inertia at higher Reynolds number (Re). Based on the observed experimental results, elasticity number function f ( E ), which well captures the dominance of elasticity at low E and inertial effects at higher Re, is incorporated in the existing Ergun equation (applicable to packed bed flow of purely viscous fluids) to predict the friction factor for Boger fluids. The developed correlation can adequately be applied to the available experimental data on non‐Newtonian inelastic fluids, shear thinning viscoelastic fluids, Boger fluids as well as for non‐spherical particles if the diameter of non‐spherical particle is replaced with equivalent diameter, that is, volume mean diameter times sphericity ( d p = d eq = d v ɸ ).
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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".