Turbulent Natural Convection in Non-Partitioned and Partitioned Cavities: CFD Predictions with Different Two-Equation Models
Bibliographic record
Abstract
Two dimensional turbulent natural convection in non-partitioned and partitioned cavities with differentially heated vertical walls and conducting horizontal walls has been simulated numerically using three different two equation models: the SST-k-ω of Menter (1993), the low Reynolds number model of Launder and Sharma (1974) and the k-ε Standard (Launder and Spalding, 1974) associated to a wall function correction. Comparisons with experimental benchmark values show that the different versions of the model give satisfactory predictions in the simple geometry while in the partitioned cavity significant differences are noted, especially in the bottom zone of the cavity. All models accurately predict the flow in the simple cavity with a slight over-estimation by the Standard k-ε due to the use of wall functions. When radiation is taken into account, a clear improvement of temperature profiles is obtained, especially near the horizontal walls. However, the quantitative improvement of radiation is not expected to be the same for all turbulence models. The success of models under consideration in the case of the simple cavity does not imply that they can predict accurately the flow in the partitioned one, even though in both cases we deal with confined natural convection. In the partitioned cavity, all models predict a bottom zone colder than the experiment data indicates. The corresponding vertical velocity in the top region of the cavity is the only quantity which is satisfactorily predicted. The horizontal velocity which is not negligible compared to the vertical one is qualitatively predicted only in the top zone of the cavity. Temperature profiles are satisfactorily predicted only at the two top levels (the partitioned cavity contains six levels where data is available). Radiation allows improved predictions in the top zone of the cavity but not in the bottom one, especially at the level of the second partition.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".