Investigation of a Passive Flow Control Device in an S-Duct Inlet of a Propulsion System With High Subsonic Flow
Bibliographic record
Abstract
S-Ducts have wide application on air vehicles with embedded engines. The complex geometry is known to lead to separation downstream of curved profiles. This paper reports the influences on that flow of passive flow control geometries. In these experiments, stream-wise tubercles were applied in an effort to improve the internal performance of S-duct diffusers, parameters including pressure recovery, distortion and swirl. The test articles were tested with the high subsonic (Ma = 0.8) flow and were manufactured using 3D printing. Stream-wise static pressure and exit-plane total pressure were measured in a test rig using surface pressure taps and a 5-probe rotating rake, respectively; the baseline and variant S-ducts were simulated through computational fluid dynamics. The experiments showed that some subtle improvements to the S-Duct distortion could be achieved through careful selection of tubercle geometry. Generally, the recovered flow downstream of the inner radius of the second bend of the S-duct deteriorated, but overall pressure recovery improved. The simulations were useful in characterizing swirl, whereas experiments were not so equipped. Adjustments to the numerical approaches resulted in reasonable agreement with the experiments.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".