Parameters influencing dilute‐phase pneumatic conveying through pipe systems: A computational study by the Euler/Lagrange approach
Bibliographic record
Abstract
Abstract The present contribution summarizes research related to the numerical computation of pneumatic conveying systems applying the Euler/Lagrange approach. For that purpose, a rigorous modelling of the particulate phase was aspired, including the relevant fluid dynamic forces, particle‐wall collisions with wall roughness and inter‐particle collisions. For the validation of the computations, experiments of Huber and Sommerfeld were selected for the conveying through a 80 mm stainless steel pipe with 5 m horizontal pipe, bend and 5 m vertical pipe. The majority of the computations were done for the same pipe system; however, in this instance, consisting of 150 mm stainless steel pipes. In these cases the average conveying velocity was 27 m/s and the particle mass loading was 0.3 (mass flow rate of particles/mass flow rate of air). For this configuration the influence of wall roughness, inter‐particle collisions, particle size, and mass loading on the resulting particle concentration distribution, the secondary flow as well as the pressure drop in the different pipe elements was analyzed. Moreover, a segregation parameter was defined which describes the location of the maximum particle concentration throughout the pipe system. The secondary flow intensity (SFI) was used to characterize the influence of the particle phase on the developing structure of the secondary flow.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".