CFD Investigation Of Mixing Of Yield-Pseudoplastic Fluid With Anchor Impeller
Bibliographic record
Abstract
The Anchor impeller, which is a close clearance impeller, produces high shear near the vessel wall and is recommended for mixing of highly viscous fluids. A thorough search of the literature suggests that few publications have beeen devoted to the computational fluid dynamics (CFD) modeling of mixing of non-Newtonian fluids with the anchor impeller. Thus the objectives of this study are (i)to generate a 3-D flow field for mixing of yield-pseudoplastic fluid in a flat bottom cylindrical tank equipped with two-and four-blade anchor impellers using CFD modeling technique, (ii) to evaluate the effects of fluid rheology agitator speed, number of blades, vessel clearance and impeller blade width on power consumption, mixing time and flow patterns, and (iii) to determine the optimum value of clearance to diameter ratio and impeller blade width to diameter ratio on the basis of minimum mixing time. The study was carried out for a yield-stress pseudoplastic fluid, using a CFD package (Fluent), to simulate the 3-D flow domain generated in a cylindrical tank equipped with two-and four-blade anchor impellers. The multiple reference frame (MRF) technique was employed to model the rotation of impellers. The rheology of the fluid was approximated using the Herschel-Bulkley model. To validate the model, CFD results for the power were compared to experimental data. After the flow fields were calculated, the simulations for tracer homogenization was performed to simulate the mixing time. The effect of impeller speed, fluid rheology, and number of impellers on power consumption, mixing time, and flow pattern were explored. The optimum values of c/D (clearance to diameter) and w/D (impeller blade width to diameter) ratios were determined on the basis of minimum mixing time.
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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 | 0.000 | 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 | 0.001 | 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".