Experimental and Computational Study of Mixing Mechanisms in an Axisymmetric Lobed Mixer
Bibliographic record
Abstract
The mixing mechanisms downstream of an axisymmetric 12-lobed mixer are studied through a combined experimental and computational investigation. A series of simulations based on the unsteady Navier–Stokes equations are used to identify the relative roles of large-scale, instability-driven transient flow structures and smaller-scale turbulence on the flow development 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 Reynolds-averaged Navier–Stokes simulations, and small-scale turbulence is captured using shear-stress transport and scale-adaptive shear-stress transport turbulence models. The simulations are validated against four-wire thermal anemometry measurements in a scaled lobed-mixer wind-tunnel model with turbulent, axial inflow conditions. Favorable agreement between the measured and simulated flowfields demonstrates the predictive capability of the simulations. The simulation results illustrate the creation of streamwise vorticity in the lobes and its subsequent interaction with the spanwise vorticity shed from the trailing edge of the lobed mixer. The extent of this interaction is shown to strongly affect the mixing of the flow downstream of the lobed-mixer discharge plane. The results also illustrate that the large-scale transient flow structures are significantly weakened by smaller-scale turbulence present in the flow, establishing the role of smaller-scale turbulence in mixing the flow downstream of the lobed mixer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".