Acceleration motion of geometric and spherical particles in two dimensions and implications in design of continuous sedimentation rectangular tanks
Bibliographic record
Abstract
The design procedure outlined by Camp (1946) for design of continuous gravity sedimentation tank was revisited. The viscous effects of flowing fluid were included in the model by development of the velocity profile of the fluid in the horizontal direction. The transient motion of the spherical and geometric particles unhindered prior to reaching terminal settling velocity, was simulated using a desktop computer. The governing equations in two dimensions, vertical and horizontal were written in terms of velocity of the particle and the drag coefficient in transient motion was assumed to be of the same functional form as that obtained from empirical observations at steady state. The five constant expressions of Turton and Levenspiel (1989) was used and the trajectory of the particle was obtained relative to the motion of the fluid by use of fifth order Runge-Kutta method of numerical integration. As the density of the particle and size of the particle increases, the acceleration zone of the particles increased in size. Deeper tanks have to be constructed for such systems. The geometric particles reached their terminal settling velocities sooner compared with the spherical particles. The pressure drop, throughput and separation efficiency trade-offs are discussed. Key words: Continuous gravity sedimentation, trajectory of particle, drag coefficient correlations, transition flow, fifth order Runge-Kutta method.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".