From Sparse Pressure Measurements to Prediction of Instantaneous Loads: A Test Case on Delta Wings in Axial and Transverse Gusts
Bibliographic record
Abstract
For a broad range of aerodynamic bodies, vortex structures arising from perturbations such as gusts admit recognizable surface pressure signatures and are coupled to the observed destabilizing loads. This 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 effect on the aerodynamic loadings. As a data-driven endeavour, a linear mapping from surface pressure to these loadings is developed and a non-slender delta wing experiencing axial and transverse accelerations is selected as test case. Direct time-resolved loads and distributed surface pressures are collected in a towing tank over three incidence angles (10, 20, 30 deg). The linear coefficients are extracted from true measured loads and sparse pressures by linear regression at each incidence angle and for an angle-independent aggregate case. The angle-specific fits have good agreement at low angles but, as the angle increases, reveal the limitations of a linear model. The aggregate method represents a more robust force-pressure mapping at the expense of a slightly decreased goodness of fit and infers the existence of a common mechanism across accelerations and angles despite the stark differences in flow conditions. A spatial interpretation of the regression coefficients supports this commonality by revealing regions of greater significance to the unsteady loads resulting from the separation and reattachment events in the flow.
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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) | 0.001 | 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 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".