Coupled CFD-DEM Simulation of Seed Flow in an Air Seeder Distributor Tube
Bibliographic record
Abstract
Air seeding equipment consists of various machine components that rely on pneumatic conveying of seeds (granular material) for its operation. However, studying air seeder dynamic features in detail is difficult through experimental measurements. A simulation was performed to study seed motion in a horizontal tube section of an air seeder distributor system. The simulation incorporated two-way coupling between discrete element modeling (DEM) and computational fluid dynamics (CFD). Simulated particles were assigned material properties corresponding to field peas. Air velocity was assigned values of 10, 15, 20, and 25 m/s. The solid loading ratio (SLR) in this study included values between 0.5 and 3 to describe typical seed metering rates used in air seeding. The different pneumatic conveying conditions were studied to determine their overall effect on the average seed velocity and seed contact force. The simulation was validated through the comparison of average seed velocity data from the literature and current pneumatic conveying theory. The effect of SLR on the average seed velocity was found to be not significant for the simulated SLR values. The CFD-DEM simulation was able to capture seed collisions between seeds and the surrounding boundaries. The seed contact force increased with the air velocity, and the number of seed collisions increased with the SLR.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".