Effects of Scalloping on the Mixing Mechanisms of Forced Mixers With Highly Swirling Core Flow
Bibliographic record
Abstract
This paper presents a detailed experimental and computational investigation of the effects of scalloping on the mixing mechanisms of a scaled 12-lobe turbofan mixer. Scalloping was achieved by eliminating approximately 70% of the lobe sidewall area. Measurements were made downstream of the mixer in a co-annular wind tunnel and the simulations were carried out using an unstructured RANS solver, Numeca FINE/Hexa, with k-ω SST model. In the core flow, the swirl angle was varied from 0° to 30°. At high swirl angles, a three-dimensional separation bubble was formed on the lobe’s suction surface penetration region and resulted in the generation of a vortex at the lobe valley. The valley vortex quickly dissipated downstream. Most of the swirl was removed by the lobes, but scalloping allowed residual swirl to persist downstream of the mixer. The interaction of the swirling flow and the vortices resulted in improved mixing rates for the scalloped mixer. Inlet swirl up to 10° provided improved mixing rates, reduced pressure loss and thrust loss for both mixers. High inlet swirl resulted in improved mixing but produced higher pressure and thrust losses as compared to the zero swirl case. At high swirl, the scalloped mixer resulted in better mixing and lower pressure losses than the unscalloped mixer, but at the expense of reduced thrust.
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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 | 0.000 |
| 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.
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