A numerical study of the transient natural convective heat transfer from thin, horizontal, isothermal plates of different shapes in lower Rayleigh number regime
Bibliographic record
Abstract
Natural convective heat transfer is of paramount importance in cooling of low power electronic devices or devices in a restricted space. In the current study, unsteady natural convective heat transfer from both sides of thin, isothermal, horizontal plates of simple and complex shapes have been numerically investigated. The plates at 400K were exposed to air at ambient conditions. The Boussinesq approximation was adopted, i.e., all fluid properties, except density, were assumed to be constant. The simulation models were solved using the commercial CFD software ANSYS FLUENT. Mean heat transfer rates from the upper and lower surfaces of the plate were calculated using three length scales namely; width of the plate, square root of single side surface area and 4*Total area/Total perimeter and were expressed in terms of the transient Nusselt number for the Rayleigh numbers ranging between 10 2 to 10 5 . At the lowest Rayleigh number, the heat transfer was found to be primarily through conduction. At higher Rayleigh numbers, the Nusselt number first decreased to a minimum and then increased to the steady state value, indicating a combined process of conduction and convection. Unlike width and root area, 4A/P as the characteristic length scale yielded transient Nusselt number variation, largely independent of plate shape and size. The minor variations of heat transfer amongst plates of different shapes at higher Rayleigh numbers has been explained in terms of the pressure coefficient.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".