LAMINAR AND TURBULENT OPPOSING MIXED-CONVECTIVE FLOW OVER A VERTICAL PLATE WITH A UNIFORM SURFACE HEAT FLUX
Bibliographic record
Abstract
A numerical study of mixed natural and forced convective flow over a thin vertical flat plate which has a uniform surface heat flux has been undertaken. Attention has been restricted to the case where the buoyancy forces act in the opposite direction to the forced flow, i.e., to opposing mixed-convective flow. Laminar, transitional, and turbulent flow situations have been considered and the development of unsteady flow has been allowed for. The forced flow has been assumed to be steady and the Boussinesq approach has been used. The solution has been obtained by numerically solving the governing equations using the commercial CFD solver, ANSYS FLUENT© . The k-epsilon turbulence model with the full effect of buoyancy forces accounted for and with standard wall functions has been used in obtaining the solutions. The heat-transfer rate from the surface of the plate has been expressed in terms of the mean Nusselt number based on the overall plate length and the difference between the overall mean plate temperature and the undisturbed fluid temperature. This Nusselt number depends on the values of the heat flux Rayleigh number based on the plate length, the Reynolds number based on the plate length, and the Prandtl number. Results have been obtained for a Prandtl number of 0.74, i.e., essentially for the value for air. The conditions under which the flow can be assumed to be purely forced convective and under which the flow can be assumed to be purely natural convective, in particular, have been investigated.
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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) | 0.001 | 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 | 0.000 |
| 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".