Natural Convection in an Enclosure with Distributed Heat Sources
Bibliographic record
Abstract
Presented in this article is a computational analysis of the heat transfer due to an array of distributed heat sources on the bottom wall of a horizontal enclosure. The heat sources are modeled as flush-mounted sources with prescribed heat flux boundary conditions. Optimum heat transfer rates and the onset of thermal instability triggering various regimes are found to be governed by the length and spacing of the sources and the width-to-height aspect ratio of the enclosure. With respect to source spacing, we found that spacing equal to that of the source length provides effective convective heat transfer, and increasing the source spacing further does not result in significant improvements. The transition from a conduction-dominated regime to a convection-dominated regime is found to be characterized by a range of Rayleigh numbers, in contrast to the classical bottom wall heating problem. The range of Rayleigh numbers at which transition takes place decreases as the source length increases. At the transition region for very small source lengths, the Rayleigh-Bénard cell structure grows significantly to form fewer and larger cells, which accounts for higher heat transfer rates compared to configurations with longer heat sources where the cell structure remains the same throughout transition. Following the transition to a convection-dominated regime, bifurcations in the Rayleigh-Bénard cell structures as well as further regime changes are observed, reflecting the instabilities in the physical system.
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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 | 0.001 |
| 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".