Experimental Investigation of Settling Velocity of Natural Sands in Power-Law Fluid Using Particle Image Shadowgraph
Bibliographic record
Abstract
In this study a new experimental technique, particle image shadowgraph (PIS), is used to investigate the settling velocity of natural sand particles in Power-law fluids. The particle settling velocity measurements are conducted within the Reynolds number of range of 0.01 to 17.00. Natural sands with mean sieve diameters in the range, 0.35 mm – 1.4 mm, are used. Six different equivalent diameter definitions are used to characterize size of the natural sand particles. Using the size and shape measurements in conjunction with PIS, correlations between the mean sieve diameter and equivalent diameters are obtained. Empirical correlations for predicting the settling velocity of sand particles in Power-law fluids are developed. Multiple linear regression analyses are performed with each fluid data and empirical coefficients for the models are also reported as functions of n and K. The models presented in the study give an average error of less than 20%. In addition, the multiple linear regression tools are applied to enhance the efficiency of the correlations by 3–5%. One of the major contributions of this study is that one can use any associated diameter to predict the settling velocity, which leads to greater flexibility.
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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) | 0.001 | 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".