A New Model for Predicting Drag Coefficient and Settling Velocity of Coarse Mineral Particles in Newtonian Fluid
Bibliographic record
Abstract
Efficient transport in vertical pipeline hydraulic lifting systems is vital for coarse-grained ore, necessitating a deep comprehension of the settling traits of coarse mineral particles. In this study, we conducted a series of settling experiments on individual coarse particles in Newtonian fluids with varying viscosities, employing a self-designed and manufactured settling apparatus. A total of 133 sets of experimental data on the free settling of coarse particles in Newtonian fluids were obtained by recording the particle settling process with a high-speed camera and applying image processing techniques. A mechanical model was employed to perform statistical analysis on the experimental data and establish a predictive model for the drag coefficient and an explicit predictive model for the settling terminal velocity of coarse-grained ore in Newtonian fluids. The average relative errors between the predicted values and experimental values of the drag coefficient and settling terminal velocity models are 4.26% and 7.34%, respectively. This confirms the reliability of the provided predicted model, providing a theoretical foundation for determining the hydraulic lifting speed of coarse mineral particles in vertical pipelines for deep mining.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".