Nusselt number correlation for a jacketed stirred tank using computational fluid dynamics
Bibliographic record
Abstract
Abstract The heat exchange in stirred tanks occurs mainly due to the impeller rotation and overall circulation of the fluid that promotes a more effective heat transfer between fluid and heat transfer surfaces. The type of impeller used and the size of the vessel (that affects the relation heat transfer area and volume of the vessel) have significant effects on the heat transfer. The heat transfer coefficient is very much dependent on the impeller and the speed of rotation. Empirical correlations are usually used to estimate the process side heat transfer coefficient. However, its dependence on the geometrical parameters restricts the use of those correlations to specific tank configurations. In this respect, the use of computational fluid dynamics (CFD) has recently emerged as an alternative to experimental studies. The procedure proved to be faster and lower cost, and the results proved to be accurate. In this study, CFD was applied to obtain a Nusselt number correlation for a jacketed stirred tank equipped with a six‐blade Rushton turbine impeller. The Nusselt number correlation obtained from the simulated model agrees with experimental data providing a reliable representation of the heat transfer in the tank.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".