Towards drag reduction on a morphing wing by transition detection and delay using kulite sensors and smart material actuators
Bibliographic record
Abstract
In this paper, wind tunnel experiment was performed for boundary layer transitioncontrol on a morphing wing. Surface pressure fluctuations weremeasured using Kulite transducersat high sampling rate. A controller, linking the Kulites signals fluctuation to the smart material actuators, is incorporated to the control system, where thesignals are analyzed and the transition location is detected. Several wind tunnel test runs for various Mach numbersand angles of attack were performed in the 6 ×9 ft2 wind tunnel at the Institute for Aerospace Research at the National Research Council of Canada. A rectangular finite aspect ratio wing,having a morphing airfoil cross section due to a flexible skin installed on the upper surface of the wing, was instrumented with sixteen Kulite transducers. The Mach numberwasvaried from 0.2 to 0.3and the angle of attack between -1oand 2o. Unsteady pressure signals were recordedand analyzed and a thorough comparison,in terms of mean pressure coefficientsand their standard deviations, was performedagainst theoretical predictions using the XFoil computational fluid dynamics code. The unsteady pressure signals were analyzed through FFT spectral decomposition for detecting the Tollmien-Schlichting waves frequencies that trigger transition. In addition, infra red measurements were performed to asses the performance of the Kulite transducersin detecting the transition location and serve as an additional validation of the XFoil code in predicting the transition location.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".