Modal Analysis of Sparse Surface Pressure Measurements in Highly Unsteady Aerodynamic Flows
Bibliographic record
Abstract
Focusing on an airfoil with pitching and plunging motions as well as a more complex delta wing experiencing gusts, we leverage linear proper orthogonal decomposition (POD) and two nonlinear autoencoders (AEs) to elucidate patterns in sparse surface pressure for highly unsteady aerodynamic problems. For the airfoil, both POD and AEs effectively decomposed and reconstructed the surface pressure data with only three modes or latent variables. Additionally, the first three POD modes distinctively identified key features of the surface pressure associated with the pitching and plunging motions. For the more complex delta wing case, both POD and AEs achieved accurate reconstruction using only three latent variables. However, the linear POD worked particularly well not only in surface pressure reconstruction but also in revealing physical insights: the identified modes were found to be intrinsically related to the mean flow structure, the Reynolds number, and the angle of attack, which were verified by the clusters in the identified low-dimensional manifold. These findings reveal that despite nonlinear flow complexities, underlying low-dimensional structures exist in surface pressure measurements, offering a new paradigm for aerodynamic state monitoring and manipulation.
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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 | 0.001 |
| 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".