Effect of a Passive Flow Control Device on the Performance of an S-Duct Inlet in High Subsonic Flow
Bibliographic record
Abstract
Abstract Embedded engines requiring S-duct diffusers as inlets have long experienced flow separation and distortion. This paper presents an investigation of passive flow control with the application on S-duct diffusers. The flow control is in the form of stream-wise tubercles aiming to improve performance through increasing boundary layer momentum and keeping flow attached in previous regions of separation. Tubercles have shown to increase post-stall performance for airfoils and are similarly applied to the suction surface when used in internal aerodynamics. This paper compares results from experimental testing and computational fluid dynamics (CFD) simulations. The experimental results measured surface pressure with static surface ports, and at the exit, total and static pressure measurements were recorded with a 5-hole AeroProbe. The implementation of flow control led to decreased or mitigated separation regions developing into a more uniform pressure at the aerodynamic interface plane. Similar results were evident in the CFD simulation. A k-ω SST model was chosen since it has shown to better predict the separation regions. A high y+ model was used since additional improvement in separation modeling. The same trends were seen in simulated pressure recovery and in swirl when comparing the use of flow control to the baseline duct performance.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".