Influence of weak electrostatic charges and secondary flows on pneumatic powder transport
Bibliographic record
Abstract
Abstract The dynamics of the transported powder determines the functionality and safety of pneumatic conveying systems. The relation between the carrier gas flow, induced by the flown‐through geometry, and the powder flow pattern is not clear yet for electrostatically charged particles. This paper highlights the influence of relatively minor cross‐sectional secondary flows and electrostatic forces on the concentration and dynamics of the particles. To this end, direct numerical simulations (DNS) capture the interaction of the continuous and dispersed phases using a four‐way coupled Eulerian–Lagrangian strategy. The transport of weakly charged particles in channel flows, where turbopheresis defines the particle concentration, is compared to duct flows, where additional cross‐sectional vortices form. For both geometries, the Stokes number () and the electrical Stokes number () are varied, and the turbulent carrier flow was fixed to . The presented simulations demonstrate that secondary flows, for the same , , and , dampen the effect of particle charge. In a duct flow, vortical secondary flows enhance the cross‐sectional particle mobility against the direction of electrostatic forces. Compared to a duct flow, in a channel, the wall‐normal aerodynamic forces are weaker. Thus, electrical forces dominate their transport; the local particle concentration at the walls increases. Further, electrostatic charges cause a stronger correlation between the gas and particle velocities. In conclusion, despite being weak compared to the primary flow forces, secondary flow and electrostatic forces drive particle dynamics during pneumatic transport.
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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) | 0.002 | 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".