Investigation of a Passive Flow Control Device in an S-Duct Inlet at High Subsonic Flow
Bibliographic record
Abstract
Abstract This paper reports the investigation of a flow control strategy for an S-duct diffusers. The method incorporates stream-wise tubercles, and aims to enhance the performance of S-duct inlets by reducing the size and intensity of separated flow. These devices, bioinspired from humpback whale flippers’ leading edge protuberances, have been shown to be effective in increasing post-stall coefficients of lift of airfoils. In S-duct diffusers, the presence of convex curvature next to the separated region provides an ideal location for the installation of a tubercle-like device. The flow control effectiveness was evaluated by test-rig measurements and computational fluid dynamics (CFD) simulations of the flow in an S-duct at high subsonic flow conditions (Ma = 0.80). The S-ducts were rapid prototyped in plastic using 3D printing. Static surface pressure along the length and total pressure at the exit revealed pressure recovery, total pressure loss, swirl, and the nature of flow distortion at the S-duct exit. CFD simulations used ANSYS FLUENT with a RANS solver closed with the RKE turbulence model. The CFD simulation compared well with the test-rig data and provided useful information on flow mechanism and for understanding flow features. The performance of the baseline and variant with the flow control device was compared and flow control strategy was evaluated.
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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 | 0.001 |
| 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 | 0.000 |
| 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".