A Numerical Study of Laminar and Turbulent Mixed Convective Flow Over a Vertical Isothermal Plate
Bibliographic record
Abstract
Mixed (or combined) convective flow is flow with heat transfer in which there is a forced flow but in which the buoyancy forces that arise due to temperature variations in the flow have a significant effect on the flow and therefore on the heat transfer rate. In such flows the buoyancy forces can also have a very significant influence on the conditions under which transition from laminar to turbulent flow occurs. In the present study this effect of the buoyancy forces on the conditions under which transition occurs have been studied for the particular case of flow in the vertically upward direction over a heated vertical flat plane surface that is maintained at a uniform temperature that is higher than the temperature of the undisturbed fluid flow, i.e., attention has been restricted to assisting (or aiding) mixed convective flow. The flow has been assumed to be steady and it has also been assumed that the fluid properties are constant except for the density change with temperature which gives rise to the buoyancy forces, this having been treated by using the Boussinesq approach. The solution has been obtained by numerically solving the governing equations subject to the boundary conditions using the commercial cfd solver, FLUENT. The k-epsilon turbulence model with full account being taken of the buoyancy forces has been used in obtaining the solutions. The mean heat transfer rate from the surface expressed in terms of the mean Nusselt number depends on the Reynolds number based on the free-stream forced velocity and the length of the heated surface, on the Rayleigh number based on the length of the heated surface and the overall surface to free-stream temperature difference, and on the Prandtl number. Results have only been obtained for a Prandtl number of 0.74. Solutions have been obtained for a series of increasing Rayleigh numbers between 105 and 1012 for a series of Reynolds numbers between approximately 1 and 107.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".