Experimental Investigation of Sand Jets Passing Through Immiscible Fluids
Bibliographic record
Abstract
Laboratory experiments were conducted to study the dynamics of sand jets passing through two immiscible fluids. Different oil layer thicknesses, nozzle diameters, and sand masses were employed. Evolution of oily sand jets with time was investigated using image processing and boundary visualization techniques. Different shapes of the frontal head and trailing wave section were observed and cloud formation was classified into different categories based on Reynolds number, normalized oil layer thickness, and evolution time. It was found that the effect of Reynolds number on evolution of oily sand jets was more significant than the other parameters. Width and frontal velocity of oily sand jets were measured at different times. It was observed that oily sand jets became unstable after a distance of ten times larger than the nozzle diameter. Instability of oily sand jets caused intense spreading with a spreading rate of 0.4. The thin layer of oil encapsulated sand cluster was ruptured due to excess shear stress and caused bursting of the frontal head into a cloud of sand particles. Three different bursting mechanisms were observed and a correlation was found between the densimetric Froude number and the normalized bursting time. Data mining and boundary visualization techniques were used to model oily sand jets. Model trees were developed to classify and predict the growth of oily sand jets at different conditions. Modeling results indicated that the Model tree can predict the growth of sand jets with an uncertainty of ±8.2%, ±6.8%, and ±8.7% for width, velocity, and distance, respectively.
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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 | 0.000 | 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".