Free‐surface turbulent flow induced by a Rushton turbine in an unbaffled dish‐bottom stirred tank reactor: LDV measurements and CFD simulations
Bibliographic record
Abstract
Abstract Laser Doppler velocimetry measurements and computational fluid dynamic (CFD) simulations of turbulent flows with free‐surface vortex in an unbaffled dish‐bottom stirred tank reactor agitated by a Rushton turbine are presented. Measurements of the three mean and fluctuating components of the velocity vector are made in order to characterise the flow field and to provide data for CFD model validation. An Eulerian–Eulerian multiphase flow model coupled with a volume‐of‐fluid method for capturing the gas–liquid interface is applied to determine the vortex shape and to compute the flow field. Turbulence is modelled using the standard k−ε, shear‐stress transport and the differential Reynolds‐stress model with two variants of the pressure‐strain correlation. The predicted mean flow field obtained using all four turbulence models are on the whole similar and generally in good agreement with measurements. However, the Reynolds‐stress models provide somewhat better predictions of the mean axial velocity. The turbulent kinetic energy is well predicted in the flow below the impeller, near the bottom of the tank; whereas it is underpredicted in the region close to the impeller and near the wall by all turbulence models.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".