Mean and Unsteady Surface-Pressure Measurements on the BeVERLI Hill
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Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-0468.vid The Benchmark Validation Experiments for RANS and LES Investigation (BeVERLI) project is an ongoing collaborative effort between experimental and computational fluid dynamics groups focusing on the advancement of our understanding and modeling capabilities for flows with a complex non-equilibrium turbulent boundary layer. Previous experimental campaigns have been focused on the collection of benchmark data sets for the flow over a 3D hill geometry to motivate and validate advancements in turbulence modeling techniques. The results presented are from experiments conducted using the same model at the Virginia Tech (VT) Stability Wind Tunnel and the University of Toronto Institute for Aerospace Studies (UTIAS) recirculating wind tunnel. Testing at these two facilities enabled for the identification of facility dependent phenomena. Mean and unsteady surface-pressure measurements were collected for the orientations of 0-deg and 45-deg. Mean surface-pressure contour plots are presented for the data collected at VT. Facility comparison is conducted through the mean surface-pressure trends along the model centerline and centerspan. Measurements collected at VT match with previously reported results using a larger scale model at the same facility. A previously reported bimodal asymmetry at 0-deg is explored in greater detail through the analysis of unsteady surface-pressure measurements using proper orthogonal decomposition, conditional averaging and tracking of the time elapsed between switching events. The first POD mode captures the bi-stable switching seen in the surface pressure field. The time elapsed between switches of the bi-stability was found to be a random process and not focused at a specific shedding frequency. Experiments at UTIAS are are ongoing to expand on these results and identify the mechanisms governing the bi-stability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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