Aerodynamic state estimation from sparse sensor data by pairing Bayesian statistics with transition networks
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".