Turbulence Modeling of Forced Convection Heat Transfer in Two-Dimensional Ribbed Channels
Bibliographic record
Abstract
Although the problem of 2D ribbed channels has been studied heavily in the literature as a benchmark or basic case for cooling of electronic packing, there is still a contradiction in the literature about the suitable turbulence model that should be used in such a problem. The accuracy of the computational predictions of heat transfer rates depends mostly on the choice of the proper turbulence model that is capable of capturing the physics of the problem, and on the corresponding wall treatment. The main objective of this work is to identify the proper turbulence model to be used in thermal analysis of electronic systems. A number of available turbulence models, namely, the standard k-ε, the renormalization group k-ε, the shear stress transport (SST), the k-ω, and the Reynolds stress models, have been investigated. The selection of the most appropriate turbulence model has been based upon comparisons with both direct numerical simulations (DNSs) and experimental results of other works. Based on such comparisons, the SST turbulence model has been found to produce results in very good agreement with the DNS and experimental results and hence it is recommended as an appropriate turbulence model for thermal analysis of electronic packaging.
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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) | 0.001 | 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".