Discrete element simulation optimization design and testing of low-damage flexible drum threshing elements suitable for high-quality seed harvesting
Bibliographic record
Abstract
In this paper, a low-damage flexible drum-shaped threshing element is designed to address the stringent requirements for seed harvesting, specifically targeting the issues of high seed breakage rates, low threshing rates, and elevated entrainment loss rates during the mechanized harvesting process of rice seed propagation. Initially, a mathematical model was developed to determine the maximum normal impact force exerted by the threshing elements on rice seeds throughout the threshing process, derived from a comprehensive mechanical analysis. Subsequently, experimental research was conducted to investigate the physical properties of rice, leading to the establishment of a flexible, multi-level hollow stem discrete element rice model. This model facilitated an examination of the normal and tangential threshing forces from a microscopic perspective, thereby validating the performance of the flexible drum-shaped threshing element. Optimization simulation tests were then performed, with drum speed, feeding amount, and threshing gap serving as test factors, while the crushing rate and loss rate were used as test indexes. The results indicate that, under the optimal structural parameters of the threshing element, the ideal configuration includes a drum speed of 900 rmp, a feeding amount of 3.734 kg/s, and a threshing gap of 23.214 mm, resulting in a normal force of 18.05 N and a tangential force of 12.96 N, with a loss rate of 0.929%. Finally, a field harvest verification test was conducted based on these optimization results. Under identical working parameters, the breakage rate of the newly designed flexible threshing element was reduced by 55.9% compared to the traditional steel nail teeth, while the loss rate decreased by 15.3%, thereby fulfilling the high-quality harvesting requirements for rice seeds.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".