Draft force prediction for a high-speed disc implement using discrete element modelling
Bibliographic record
Abstract
Draft force is an important dynamic parameter for agricultural soil-engaging tools and implements. Soil-disc interaction dynamics has changed enormously in modern tillage practice because of the use of larger machines, faster speeds, and different tool arrangements. Most existing studies on draft force prediction are for conventional tillage. In this study, a discrete element model (DEM) was developed to predict draft forces for high-speed (12 km h−1 or higher) tillage. Draft forces were measured for an individually mounted disc using soil bin tests at low speeds in a sandy loam soil. Results were compared for the accuracy with the predicted values from the ASABE standard equation. Results showed that relative error ranged from 8% to 14%. Due to the limitations of the testing facility, the DEM model was calibrated using 6 km h−1 tillage speed and verified with other test speed measurement. The calibrated model was able to predict draft force with a minimum relative error of 1%. The calibrated particle stiffness, was found to be 20 kN m−1. The calibrated model was then used to examine the effects of operating parameters including gang angle, tilt angle, operating speed, disc diameter and tillage depth on draft forces at 16 km h−1. From these simulations, it was shown that draft increased with an increase in gang angle, tillage depth, and speed. The draft force was reduced with an increase in tilt angle and remained relatively constant with the change of disc diameter. Finally, the simulated data were used to develop a multivariate regression equation to predict the draft for a high-speed tillage operation. This equation is suitable for the soil conditions studied and further validation is required to verify this equation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".