The effect of stiffening tabs on the performance of lobed mixers at off-design conditions
Bibliographic record
Abstract
This paper presents a computational investigation of scaled turbofan lobed mixer stiffening tabs at low-speed off-design conditions. Stiffening tabs provide rigidity to the thin lobed mixer by connecting the mixer valley to the more rigid centrebody. Evidence shows that the tabs affect the flow structures of turbofan exhaust systems at off-design core inlet swirl conditions. Observations were made downstream of the mixer in simulations that were carried out with an unstructured RANS solver and the k-ω SST turbulence model. To model off-design conditions, the core flow swirl was increased from axial flow to 10° at the moderate case and 30° at the high swirl case. The tab geometry was shown to perturb some of the less involved flow mixing structures, streamwise vortices near the lobe valley. Simulations of geometries with the tabs displayed more uniform flow throughout the common nozzle with higher thrust outputs; however, these minor improvements are negated by higher total pressure losses.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".