Flow-conditioning of a subsonic wind tunnel to model boundary layer flows
Why this work is in the frame
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Bibliographic record
Abstract
This study aims at modeling boundary layers (BLs) encountered in sparse and built environments (i.e., open, suburban and urban) at the subsonic Wind Tunnel (WT) at Ryerson University (RU). This WT has an insignificant turbulence intensity and requires a flow-conditioning system consisting of turbulence generating elements (i.e spires, roughness blocks, barriers) to achieve proper turbulent characteristics. This system was developed and validated in the current study in three phases. In phase I, several Computational Fluid Dynamic (CFD) simulations of the tunnel with generating elements were conducted to understand the effect of each element on the flow. This led to a preliminary design of the system, in which horizontal barriers (slats) are added to the spires to introduce turbulence at higher levels of the tunnel. This design was revisited in phase II, to specify slat dimensions leading to target BLs encountered by tall buildings. It was found that rougher BLs require deeper slats and, therefore, two-layer slats (one fixed and one movable) were implemented to provide the required range of slat depth to model most BLs. This system only involves slat movement to change the BL, which is very useful for automatic wind tunnel testing of tall buildings. The system was validated in phase III by conducting experimental wind tunnel testingof the system and comparing the resulting flow field with the target BL fields considering two length scales typically used for wind tunnel testing. A very good match was obtained for all wind field characteristics which confirms accuracy of the system.
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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 it