Experimental study on the mixing and dispersing of floating particles in viscous system
Bibliographic record
Abstract
Abstract The effects of baffle configuration, impeller type, impeller diameter ratio, and immersion depth on the critical drawdown speed and power of floating particles were investigated by experiment, and the advantageous stirred vessel was determined on the basis of the low energy consumption principle. Furthermore, the effects of system viscosity, mean solid concentration, and particle size on the dispersing characteristic of floating particles were also researched with the advantageous stirred vessel. The results show that, (1) when the stirred vessel is equipped with a single baffle, the critical drawdown speed and power of floating particles are lower than those of others. (2) Among the propeller, six flat‐blade disc turbine (Rushton), and six 45° pitched‐blade turbine (PBT‐6), the Rushton turbine has the lowest critical drawdown speed, and the down‐pumping propeller (TXL) has the lowest critical drawdown power. (3) The critical drawdown speed and power are lower with a larger impeller diameter and a smaller immersion depth. (4) The critical drawdown speed and power increase with increasing system viscosity, mean solid concentration, and particle size. The experimental results provide useful guidelines for the advantageous design of the stirred vessel used for the dispersing of floating particles in a viscous system.
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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 | 0.001 | 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".