Aerodynamic state estimation from sparse sensor data by pairing Bayesian statistics with transition networks
Why this work is in the frame
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Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-1669.vid Swimmers and flyers in nature take advantage of distributed sensor feedback to control the interaction between the surrounding fluid and their bodies, even in challenging environments such as wake flows or gusts. Inspired by this behavior, we suggest a novel data-driven method that thereby could enable effective flow control. Sparse sensor data captured on the propulsor are combined with a pre-trained algorithm to provide an estimate of the present aerodynamic state. By combining transition network theory and Bayesian statistics, a low-order model of the highly non-linear system is obtained, which is robust towards the high noise levels ubiquitous in experimental (real) pressure data. For the current study, the flow around an accelerating elliptical plate is selected as a test case. The plate is accelerated and decelerated at various (fixed) angles of attack, and the flow is captured by load and pressure measurements. The aerodynamic loads are then estimated for angle-of-attack cases that were not included in the training data, thus showing the method effectiveness under unknown (untrained) configurations.
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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 | 0.001 | 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) | 0.001 | 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