Solid-Liquid Mixing In Agitated Tanks : Experimental And CFD Analysis
Bibliographic record
Abstract
Solid-liquid mixing plays a significant role in crystallization, suspension polymerization, leaching, solid-catalyzed reaction and adsorption. In this study, a computational fluid dynamic (CFD) model was developed for solid-liquid mixing in a cylindrical tank equipped with a top-entering impeller. The multiple reference frame (MRF) technique, k-ε model and Eulerian-Eulerian approach were employed to simulate the impeller rotation, turbulent flow and multiphase flow, respectively. The effects of impeller speed, solid concentration, particle size, solid density and impeller clearance on the mixing performance of four different impellers (A310, marine propeller, pitched blad turbine and A320) were investigated. The CFD results were in good agreement with experimental data measured using electrical resistance tomography (ERT). In order to investigate the mixing quality in this study, the impeller speed required for maximum homogeneity, clouding height, and just-suspended impeller speed were investigated.
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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 | 0.001 | 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".