A numerical study of the transient natural convective heat transfer from thin, horizontal, isothermal plates of simple and complex shapes under turbulent flow conditions
Bibliographic record
Abstract
Natural convective heat transfer finds importance in applications such as cooling of low power electronics, Printed Circuit Boards (PCBs) and of components in a restricted space. The variable load on such components also warrants the study of transient behaviour. In this investigation, unsteady natural convection was numerically computed around thin horizontal flat isothermal plates of different shapes using commercial CFD solver Ansys FLUENT. The plates were maintained at 400K and exposed to air at ambient temperature of 300K. Boussinesq approximation was adopted. Transient heat transfer from the top and bottom surface of the plates were presented in terms of the Nusselt number, Rayleigh number and dimensionless time, calculated using two length scales- 4*Total Area/Total Perimeter and square root area of single side surface area. Rayleigh numbers between 10 5 -10 7 were considered. Analysis of the transient heat transfer profiles, calculated using the two length scales revealed that plate shapes had more pronounced effect on the heat transfer process compared to the length scale. Prior to reaching the steady state, the Nusselt number profile undergoes transition from initial conductive phase to convective flow development. Increasing the Rayleigh number tends to shorten the conduction and convective flow development phases. Further investigation revealed that unlike in laminar flow regime, the heat transfer in turbulent regime is significantly affected by fluid flow pattern, influenced by plate shape. Normalized Nusselt number was used to incorporate this shape effect, which enabled obtaining a singular transient heat transfer variation independent of plate shape and size. These profiles would assist the designers to compute the complete transient natural convective heat transfer characteristics of any plate shape using only the steady state heat transfer value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".