Surface-Pressure-Based Estimation of the Velocity Field in a Separation Bubble
Bibliographic record
Abstract
The effectiveness in estimating the velocity field in a laminar separation bubble using surface-pressure-based stochastic estimation methods is examined. A separation bubble is formed over a NACA 0018 airfoil at a Reynolds number of 125,000 and an angle of attack of 4°, while the velocity field and surface pressure fluctuations are measured simultaneously. Single-time-delay and multi-time-delay estimation techniques, in both single-point and multipoint formulations, are employed and compared, showing that the accuracy of the estimates increases notably through the inclusions of additional predictor events in both space and time. The multipoint, multi-time-delay estimation technique is shown to produce the most accurate estimates, with the reconstructed velocity fields capturing all essential flow features across the scales of interest. The accuracy of the estimates is shown to depend on location within the bubble, with the best results found in the region of the mean maximum bubble height, whereas performance decreases near the mean separation point. The latter is due to the transition to turbulence increasing the randomness of fluctuations and can be mitigated through more advanced stochastic estimation, whereas the former is a result of low disturbance amplitudes that fall within the noise level of the measurements.
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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".