A Basic Numerical Study of the Effect of a Hot Air Vent on the Heat Transfer Rate From a Cold Window
Bibliographic record
Abstract
In colder climates hot air vents are often mounted below a cold window to improve thermal comfort of the occupants. The presence of the vent alters the rate of convective heat transfer to the window and changes the air-flow pattern near the window and this has here been numerically studied. The situation considered in this study is an approximate model of most real situations. The window is represented by a plane isothermal section recessed into the wall, this window section being colder than the room air far from the window. The vent is assumed to be placed against the wall and to have a uniform discharge velocity which is normal to the vent surface. The vent has been assumed to be centrally located below the window. The flow has been assumed to be steady and both laminar and turbulent flows have been considered. The fluid properties have been assumed constant except for the density change with temperature that gives rise to the buoyancy forces, this being dealt with using the Boussinesq approach. The governing equations have solved using the commercial cfd code FLUENT, the k-epsilon turbulence model with buoyancy force effects fully accounted for having been used in the turbulent flow calculations. The solution has the following parameters: the Rayleigh number, the Reynolds number based on the vent discharge velocity, the dimensionless depth that the window is recessed, the dimensionless window-to-undisturbed-air temperature difference, the Prandtl number, the dimensionless width of the window, the dimensionless depth and width of the hot air vent, and the dimensionless vent discharge temperature-to-undisturbed-air temperature difference. Results have only been obtained for a Prandtl number of 0.7. The effects of the other dimensionless variables on the window Nusselt number and on the flow pattern and air temperature distribution near the window have been numerically determined.
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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) | 0.000 | 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".