Conjugate turbulent forced convection in a channel with an array of ribs
Bibliographic record
Abstract
Purpose Performance of various k‐ε models on turbulent forced convection in a channel with periodic ribs is assessed. Design/methodology/approach The influence of the Yap correction and the non‐linear stress‐strain relation on the predictions of mean‐flow, turbulence quantities and local heat transfer rate is examined. The effect of thermal boundary conditions on the heat transfer predictions is investigated by employing both the prescribed heat flux approach and the conjugate heat transfer approach. Findings It was found that the inclusion of the Yap correction in the ε‐equation significantly improves the predictions of mean velocity and wall heat transfer for both high‐Reynolds number and low‐Reynolds number k‐ε models in the present ribbed channel flow with massive flow separation. The employment of the non‐linear stress‐strain relation only marginally improves the predictions of turbulence quantities: the turbulence anisotropy is reproduced although the level of turbulence intensity is still too low. In general, the conjugate heat transfer approach predicts better average Nusselt number than the prescribed heat flux approach. However, both approaches under‐predict the experimental value by about 28‐33 percent when the low‐Reynolds number k‐ε model of Lien and Leschziner (1999) with the Yap term is adopted. Originality/value Thorough numerical treatments of the thermal boundary conditions at the solid‐liquid interface, and detailed periodic condition in the periodic regime, were given in the paper to benefit researchers interested in solving similar problems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".