Surface Pressure Based Flow Field Estimation: Comparison of LSE and Machine Learning Algorithms
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Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2153.vid This study presents a cross-comparison of traditional linear stochastic estimation (LSE) and support vector machine (SVM) algorithms. The assessment of the capabilities of both estimation techniques is based on reconstructing the unsteady behavior of a laminar separation bubble (LSB) on a NACA 0018 airfoil at a Reynolds number of 100,000. The algorithms are trained based on time-resolved velocity field measurements performed simultaneously with sparse unsteady surface pressure measurements. The flow reconstructions performed based on surface pressure measurements are evaluated based on an independent set of flow field measurements. The results show a comparable performance across multi-point LSE and different SVM methods investigated, which are shown to capture the flow development of dominant coherent structures in the separated shear layer.
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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 | 0.000 | 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 it