Numerical Prediction of Particulate Flow Over a Backward-Facing Step Followed by a Filter Medium
Bibliographic record
Abstract
Flow in air filter housings often is characterized by separation upstream of the filter. The effect of the separation on the motion of particles and their distribution at the filter is important to filter performance. The current research investigates these effects by applying CFD modeling to turbulent particulate flows over a backward-facing step followed by a porous medium representing a filter. The two-dimensional step flow was selected as it is an archetype for separated flow with many studies in the literature. The flow examined has a step expansion ratio of 1:2, with an entrance length of 30 step heights to the step followed by a length of 60 step heights. Computations were performed at step Reynolds numbers of 6550 and 10,000 for the step without a porous medium and with the medium placed 4.25 and 6.75 step heights downstream of the step. The mesh was developed in ICEM CFD and modeling was done using the Fluent commercial CFD package. The carrier phase turbulence was modeled using the RNG k-epsilon model. The particles were modeled using the discrete phase model with dispersion modeled using stochastic tracking. The boundary conditions are uniform velocity at the inlet, no-slip at the walls, porous jump at the porous medium, and outflow at the outlet. The particle boundary condition is “reflect” at the walls and “trap” at the filter. The numerical results for the no filter case matched experimental results for recirculation zone length and velocity profiles at 3.75 and 6.25 step heights well. The computed velocity profiles at 3.75 step heights do not match experimental profiles for the filter at 4.25 step heights so well, though the results show a profound effect on the recirculation zone length, matching the experiments. Differences are attributed to different velocity profiles at the step. With the medium 6.75 step heights downstream, the effect on the recirculation zone is negligible, again matching experimental results. The discrete phase model tracks injected particles and provides results which are qualitatively similar to the literature. It is observed that particles with lower Stokes number, and thus lower momentum, tend to follow the flow and enter the recirculation zone while particles with higher Stokes number tend to move directly to the porous medium. When the filter is moved downstream to 6.75 step heights, the increased length of the recirculation zone results in more particles entering the recirculation zone. Results for monodispersed and polydispersed particles agree.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".