Microproperties calibration of discrete element models for soil-tool interaction
Bibliographic record
Abstract
Abstract. PFC3D is a discrete element modeling tool which has been used lately for simulations of soil-tool interaction in agriculture. However, existing studies mainly focused on simulations of soil cutting forces, not soil flow. In this study, a soil-tool model was developed using the parallel bond model (PBM) of PFC3D to demonstrate whether the model could be used to simulate soil flow characteristics resulting from a simple soil engaging tool, while satisfying force simulations. In the simulations, soil was modeled as spherical particles with bond between particles. The model outputs examined were two most important soil dynamic behaviors: thrown-soil and draft force. Through examining effects of model microproperties on the thrown-soil and draft force, one found that the feasible ranges of the model microproperties were: 1e4-5e6 Pa for the modulus of elasticity of particle, 1e5-1e8 Pa for the modulus of elasticity of bond, 1e4-1e5 Pa for bond strengths, 0.3-0.7 for local damping coefficient, and 0-1.0 for viscous damping coefficients. For simulations of soil-tool interaction, the model microproperties should be selected within these feasible ranges. Otherwise, the behaviors of model particles would not reflect the behaviors of real soil. Within these feasible ranges, the model outputs were influenced the most by the modulus of elasticity of particle; the other model microproperties had little impact on the model outputs. Soil cutting tests were conducted in a sandy loam soil to calibrate and evaluate the soil-tool model. The results showed that a modulus of particle elasticity of 2.5e5 Pa resulted in a good match between the simulated and measured raft force. However, with this modulus, the simulated thrown-soil was significantly lower than the measured one. Further investigations showed that it may not be possible to match the simulated and measured thrown-soil using the PBM of PFC3D. Therefore, redefining the constitutive laws of particle contacts would be required to improve the accuracy of the model for simulations of soil flow behavior.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".