Effect of Elastic Properties of the Fluids on the Particle Settling Velocity
Bibliographic record
Abstract
An experimental study was performed to investigate the influence of fluid elastic properties on the settling velocity of spherical particles in viscoelastic polymer fluids. The Particle Image Shadowgraph (PIS) technique was used to measure the settling velocity of the spherical particles (with average diameter of 2mm) in the hydrolyzed poly acrylamide (HPAM) polymer test fluids. Test fluids were prepared by mixing 3 different grades of HPAM (with molecular weights of; 500,000; 8,000,000; and 20,000,000) at polymer concentrations of 0.09 and 0.1% by weight. Shear viscosity and oscillatory measurements were carried out to characterize the test fluids. The test fluids were formulated in such a way that they had almost identical shear viscosity characteristics while showing significantly different elastic properties. The relaxation time was used to quantify the elastic characteristics of the fluids. To quantify the impact of elasticity, the experimentally measured settling velocities were compared to the values calculated by using the model developed for predicting settling velocity of spherical particles in power law (visco-inelastic) fluids [1]. Experimental results indicated that the settling velocity of spherical particles in visco-elastic fluids decreased significantly with the increasing elasticity (measured in terms of relaxation times) of the fluids.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".