Two-Equation Turbulence Modeling for Impeller Stirred Tanks
Bibliographic record
Abstract
In this study, the predictive performance of six different two-equation turbulence models on the flow in an unbaffled stirred tank has been investigated. These models include the low Reynolds number k-ε model of Rodi, W., and Mansour, N. N., “Low Reynolds Number k-ε Modeling With the Aid of Direct Simulation Data,” J. Fluid Mech., Vol. 250, pp. 509–529, the high and low Reynolds number k-ω models of Wilson, D. C., 1993, Turbulence Modeling for CFD, DCW Industries, La Canada, CA., the RNG k-ε model, and modified k-ω and k-ε models which incorporate a correction for streamline curvature and swirl. Model results are compared with experimental laser Doppler velocimetry (LDV) data for the turbulent velocity field in an unbaffled tank with a single paddle impeller. An overall qualitative agreement has been found between the experimental and numerical results with poor predictions observed in some parts of the tank. Discrepancies in model predictions are observed in the anisotropic regions of the flow such as near the impeller shaft and in the impeller discharge region where the model overpredicts the radial velocity component. These results are discussed and a strategy for improving two-equation models for application to impeller stirred tanks is proposed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".