Morphing of an adaptive shock control bump using pressurized chambers
Bibliographic record
Abstract
The dawn of research on shock and boundary layer interaction control dates back to the 1970s, when humped transonic aerofoils were first studied as a means to improve the performance of supercritical aerofoil technology at off-design conditions. Since then, shock control bumps have been found to be promising devices for such kind of flow control. They have a smearing effect on the shock wave structure achieved through isentropic pre-compression of the flow upstream of the main shock and can significantly lower wave drag without incurring unacceptable viscous losses. However, their performance is strongly dependent on a set of geometrical parameters which must be adjusted according to the ever-changing flight conditions. A concept for an adaptive shock control bump is therefore presented. The proposed actuation mechanism aims at a compact, lightweight and simple structure which could be integrated into the spoiler region of near-future aircraft without major design changes required. Numerical optimization of a simplified analytical model of the structure is used to investigate the shock control bump adaptation to various aerodynamic target shapes. Compromises between geometrical conformity and both structural and actuation related requirements are studied. Furthermore, an outlook is given on design issues related to three-dimensional effects on a finite span shock control bump.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".