DEM simulation of subsoiling in tropical sugarcane fields: Effects of opposing subsoiler design and model parameters
Bibliographic record
Abstract
• The first work to analyze subsoiling operations in red clay soils in sugar cane growing areas. • The optimal ranges of backsweep angle, opposing plow spacing and penetration angle were determined. • A regression model between tillage resistance and soil disturbance was established. • The optimized model significantly reduced tillage resistance and soil disturbance by 34.42% and 30.50% respectively. During the subsoiling operation of clayey soil in sugarcane fields in tropical areas, there are key limitations such as high resistance caused by high adhesion and unsatisfactory subsoiling effects of machines and tools. To address these issues, this study first explored the machine's structural characteristics using a mechanism analysis method to identify key optimization parameters. Then, simulation tests were conducted based on the root-soil complex model. Single factor analysis was conducted to evaluate the effects of backsweep angle, plow spacing, and penetration angle on tillage resistance and soil disturbance, determining their optimal ranges. Finally, a multi-factor orthogonal rotation combination experiment was designed using Box-Behnken theory to optimize the subsoiler's structural parameters. The regression analysis results show that the optimized model significantly improves the reliability of simulation results. Field verification test results show that under the conditions of a sweep angle of 50°, a plow spacing of 60 cm, and a soil penetration angle of 45°, the average tillage resistance of subsoiling operations is significantly reduced to 76.2 kN, and the amount of soil disturbance is reduced to 1780cm². The optimization reduced tillage resistance and soil disturbance by 34.42% and 30.50% respectively, significantly improving the subsoiling performance. To our knowledge, this is the first work applying discrete element methods to subsoiling operations in red clay soils in sugarcane growing areas. The proposed machine structure has higher efficiency and better performance, providing an effective reference for the design and application of sugarcane field subsoiling machinery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".