Computational and Experimental Study of the Effect of Inlet Swirl on Mixing Mechanisms in an Axisymmetric Lobed Mixer
Bibliographic record
Abstract
The effect of circumferential inflow swirl on the instability of the shear layer formed between the core and bypass flows discharged from an axisymmetric twelve-lobed mixer is studied through a combined experimental and computational investigation. A series of unsteady Navier-Stokes simulations are performed with 0 and 31 degrees of circumferential swirl specified in the core stream of the lobed mixer. Comparison of the axial- and swirling-inflow cases highlights the effect of swirl on the instability-driven transient flow structures that develop within and downstream of the lobed mixer. Medium- and large-scale unsteady motions are captured by the fine spatial and temporal resolution of the unsteady Navier-Stokes simulations. The simulations are validated against four-wire thermal anemometry measurements in a scaled lobed-mixer wind-tunnel model with turbulent, swirling inflow conditions. The simulation results illustrate that while the axial-inflow case develops layers of streamwise vorticity uniformly along the lobe walls, the core flow in the swirling-inflow case separates from the suction side of the lobe wall near the lobe trough. Roll-up and axial stretching of the separated flow produces Λ-shaped vortical structures upstream of the discharge plane. The Λ-shaped structures interact with the shear layers discharged from the lobe trailing edge and accelerate the breakdown of the shear layer in the swirling-inflow case relative to the axial-inflow case. The extent of this interaction is shown to strongly affect the streamwise mixing rate of the flow downstream of the discharge plane.
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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.
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".