Pressure Equalization Method for Passive Flow Control of an S-Duct Intake for High Subsonic Speeds
Bibliographic record
Abstract
High subsonic aircraft with fuselage-embedded engines often employ inlet ducts with multiple bends in order to induct ambient air into the propulsion system while also diffusing it to engine-acceptable Mach numbers. Engine performance, stability margin, and safety of the integrated aircraft-engine system can be negatively affected by separated, swirling and distorted flow that often characterizes S-ducts. This paper reports the investigation of a flow control strategy aimed at the improvement of the aerodynamic performance of S-duct diffusers. Passive pressure equalization was employed to reduce the size and intensity of the separated flow downstream of curved duct sections, utilizing naturally occurring pressure differences. Characteristic secondary flows promote instability and contribute to flow separation and losses in the inner radius region of a duct bend. In the present scheme, boundary layer flow upstream of the separation point on the inner radius of the first bend is energized by re-injecting higher momentum air, drawn from the higher pressure region at the outer radius of the same bend. The flow control effectiveness of this passive pressure equalization was evaluated by test-rig measurements of the flow in an S-duct at an inlet Mach number of 0.80. Static surface pressure was measured along the length of the S-duct and the total pressure was measured at the aerodynamic interface plane using a pressure rake with five high performance pressure transducers. It was possible to reveal pressure recovery, total pressure loss, and the general nature of flow distortion at the AIP.
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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 | 0.000 | 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".