Exploring the signature of distributed pressure measurements on non-slender delta wings during axial and vertical gusts
Bibliographic record
Abstract
For a broad range of aerodynamic bodies, vortex structures arising from perturbations such as gusts cause characteristic surface pressure signatures that are coupled to the observed aerodynamic loads. The present study evaluates the extent to which sparsely measured pressure signatures can be used to identify the spatio-temporal evolution of vortex structures and, specifically, their relationship to the bulk aerodynamic loads. A non-slender delta wing experiencing axial and vertical gusts under various initial stall conditions is selected as a test case. Time-resolved loads, distributed surface pressures, and time-resolved flow fields (particle image velocimetry) are collected for a wide range of parameters in a towing-tank facility. By linearly mapping the sparse pressure data to the aerodynamic loads, the spatio-temporal relation of loads and pressure can be extracted. The static mapping coefficients are determined through linear regression at each incidence angle as well as for an angle-independent (aggregate) case. Despite slightly larger errors when compared to the angle-specific fits, the aggregate method maintains a good fit quality over all angles of attack and thereby provides a robust pressure-load mapping. Thus, the existence of a common mechanism across gusts and angles of attack is identified despite the stark differences in flow conditions, i.e., light vs deep dynamic stall. In addition, the lasso regularization used in the study provides valuable insight into sensor reduction. The distribution of fewer regression predictors indicates specific pressure ports that capture the footprint of dominant flow features and thereby suggest sensitive locations for future clusters of sensors.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".