A Numerical Study of the Effect of Blind Opening on Laminar-to-Turbulent Transition in the Flow Over a Simple Recessed Window-Plane Blind System
Bibliographic record
Abstract
Most numerical studies of convective heat transfer between a window-blind system and a room are based on the assumption that the flow remains laminar. However, in the case of larger windows it is to be expected that transition to turbulent flow will occur in the system. The aim of the present study was to numerically determine when transition to turbulent flow occurs in a recessed window system and the effect of a simple partially open plane blind on when transition occurs. An approximate model of a recessed window that is covered by a partially open plane blind has been considered. The inner surface of the window is modeled as a plane vertical isothermal surface and the blind as a thin plane surface that offers no resistance to heat transfer. 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. Radiant heat transfer effects have been neglected. The k-epsilon turbulence model with the full effects of the buoyancy forces being accounted for has been used in obtaining the solution. The governing equations have been solved using the commercial finite-volume based cfd code FLUENT. The solution has as parameters: (1) the Rayleigh number, (2) the Prandtl number, (3) the dimensionless ‘window’ recess depth, (4) the dimensionless blind opening, and (5) whether the ‘window’ surface is at a higher or lower temperature than the room air. Because of the application being considered results have only been obtained for Pr = 0.7 and for the case where the ‘window’ surface is at a higher temperature than the room air. The effect of transition on the mean Nusselt number variation with Rayleigh number with various blind openings for various dimensionless window recess depths has in particular been studied.
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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 | 0.001 | 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".