Natural Convective Heat Transfer From the Horizontal Isothermal Surface of Polygons of Octagonal and Hexagonal Shapes
Bibliographic record
Abstract
Heat transfer often occurs effectively from horizontal elements of relatively complex shapes in natural convective cooling of electronic and electrical devices used in industrial applications. The effect of complex surface shapes on laminar natural convective heat transfer from horizontal isothermal polygons of hexagonal and octagonal flat surfaces facing upward and downward of different aspect ratios has been numerically investigated. The polygons’ surface is embedded in a large surrounding plane adiabatic surface, where the adiabatic surface is in the same plane as the surface of the heated element. For the Boussinesq approach used in this work, the density of the fluid varies with temperature, which causes the buoyancy force, while other fluid properties are assumed constants. The numerical solution of the full three-dimensional form of governing equations is obtained by using the finite volume method-based computational fluid dynamics (CFD) code, FLUENT14.5. The solution parameters include surface shape, dimensionless surface width, different characteristic lengths, the Rayleigh number, and the Prandtl number. These parameters are considered as follows: the Prandtl number is 0.7, the Rayleigh numbers are between 103 and 108, and for various surface shapes the width-to-height ratios are between 0 and 1. The effect of different characteristic lengths has been investigated in defining the Nusselt and Rayleigh numbers for such complex shapes. The effect of these parameters on the mean Nusselt number has been studied, and correlation equations for the mean heat transfer rate have been derived.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".