Bibliographic record
Abstract
Loss of control is the major source of accident for commercial transports and general aviation, and stall is about one third of them. The prediction and control of stall is therefore a major stake for flight safety. However, stall mechanisms occurring on aircraft are of high complexity, as they generally combine different mechanisms leading to the boundary layer separation, depending on local flow characteristics. Recent developments on numerical methods make possible the vision of an accurate prediction of stall in a near future, but to reach this objective, there is a need of high quality experimental database, including flow field measurements, for validation. A research program for the prevision, analysis and control of wing stall has been led at ONERA for a better understanding and prevision of basic physical phenomena leading to stall. This program, named PANDA (french acronym for Prevision et ANalyse du Decrochage d’Aile) considers both experimental and numerical aspects. Within the different work packages of the project, one considers a generic wing at low speed conditions, aiming at building up experimental database for basic stall phenomena, mainly trailing-edge and leading-edge stall. This work is part of a collaboration between ONERA and Polytechnique Montreal. The paper presents the design process, by the use of RANS method, of the wing-body model dedicated to this task. The NACA4412 airfoil has been selected as basic airfoil for wing generation. A first step of the design process considers a wing at wall configuration, which twist has been adapted in order to have a quasi bi-dimensional trailing-edge separation close to stall. Then, a generic “fuselage” has been adapted to this wing and designed in order to recover the flow characteristics observed for the wing at wall design. Finally, the effect of the wing sweep angle has been evaluated, and numerical results have been used for the equipment definition in term of steady and unsteady pressure sensors.
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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 | 0.002 | 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 | 0.001 | 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".