Reynolds-averaged Navier–Stokes modelling in transonic S-ducts with passive flow control
Bibliographic record
Abstract
S-duct diffusers are used in aircraft with embedded engines to route ambient air to the fan face. Sizing and stealth considerations drive a need for high curvature ducts, but the curvature causes complex secondary flows that lead to total pressure distortion and swirl velocities at the engine face. These must be controlled for stable engine operation. In this paper, tubercles, a novel bio-inspired passive flow control method, are analysed numerically in a duct with transonic flow. The results are compared to experimental data obtained as part of a campaign at the Royal Military College, Canada to investigate the effects of S-duct geometry and novel passive flow control devices on the performance of transonic S-ducts. The performance of Reynolds-averaged Navier–Stokes turbulence models in the S-ducts is assessed – Menter's shear stress transport model predicts excessive losses due to the overactivity of its stress limiter. The realisable k–ɛ model gives a significant improvement in the prediction of static pressure distributions, but losses and distortion characteristics are predicted poorly due to the model's inability to resolve the effects of unsteadiness in separated regions. Large tubercle geometries are found to trigger earlier separation in the centre of the duct by concentrating low momentum fluid in valleys, but they also act as boundary layer fences away from the duct centre. Smaller geometries are found to generate vortices that re-energise the boundary layer, delaying flow separation. Methods are recommended for future computational analyses of S-ducts and new designs of tubercles.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".