Discrete Element Modeling of Soil Displacement Resulting from Hoe Openers
Bibliographic record
Abstract
Abstract. Soil displacement is the most important performance indicator for seed openers, as it affects the uniformity of seeding depth. In this study, a hoe opener was modeled using Particle Flow Code in 3 Dimensions (PFC3D), a discrete element modeling software program. The objective was to simulate soil displacement in terms of soil throws. To validate the model, an air drill with hoe openers was tested in a field with clay soil at a working depth of 38 mm and travel speed of 8 km h-1. Soil throw resulting from the hoe opener was measured. To calibrate the model, a virtual soil shear test was created within PFC3D, and the output soil shear torque was compared to the torque measured in the same field. The result showed that the calibrated effective modulus, a critical micro-parameter of model particles, was 5.692 × 107 Pa. With this calibrated value, the simulated soil throws agreed well with the measured throws, with a relative error of 15%. The model was used to compare different hoe opener designs: single-shoot spread, double-shoot side-band, double-shoot paired-row, and triple-shoot openers. Among all these openers, the side-band opener resulted in the least lateral soil throw, and the paired-row opener resulted in the lowest vertical soil throw but the highest lateral throw. The developed model was effective for examining the effects of opener geometry on soil displacement. Keywords: DEM, Hoe, Opener, PFC3D, Soil displacement.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".