Effects of Core Flow Swirl on the Flow Characteristics of a Scalloped Forced Mixer
Bibliographic record
Abstract
This paper presents a detailed experimental investigation of the influence of core flow swirl on the mixing and performance of a scaled turbofan mixer with 12 scalloped lobes. Measurements were made downstream of the mixer in a co-annular wind tunnel. The core-to-bypass velocity ratio was set to 2:1, temperature ratio to 1.0, and pressure ratio to 1.03, giving a Reynolds number of 5.2 × 105, based on the core flow velocity and equivalent hydraulic diameter. In the core flow, the background turbulence intensity was raised to 5% and the swirl angle was varied using five vane geometries from 0° to 30°. Seven-hole pressure probe measurements and surface oil flow visualization were used to describe the flowfield and the mixer performance. At low swirl angles, additional streamwise vortices were generated by the deformation of normal vortices due to the scalloped lobes. With increased core swirl, greater than 10°, the additional streamwise vortices were generated mainly due to radial velocity deflection, rather than stretching and deformation of normal vortices. At high swirl angles, stronger streamwise vortices and rapid interaction between various vortices promoted downstream mixing. Mixing was enhanced with minimal or no total pressure and thrust losses for the inlet swirl angles less than 10°. However, the reversed flow downstream of the center-body was a dominant contributor to the loss of thrust at the maximum core flow swirl angle of 30°.
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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 | 0.001 | 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) | 0.001 | 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".