Discrete Element Simulations of Vibration Characteristics of Bulk Grain in Storage Bins
Bibliographic record
Abstract
A discrete element model (DEM) (Particle Flow Code, PFC3D) was constructed to simulate vibration of bulk solids in a bin filled with soybeans. The vibration frequencies and amplitudes of individual particles were determined from simulated particle velocities and displacements at different excitation frequencies and amplitudes. The simulated results were compared with experimental data obtained from model bin tests. The model bin of 0.28 m height and 0.15 m diameter was made of Plexiglas and filled with soybeans. The bin was vibrated at frequencies from 5 to 30 Hz and amplitudes from 0.2 to 5.0 mm. A high-speed digital imaging system was used to record particle movement along the bin wall during vibration. Simulated vibration of particles was in good agreement with the experimental data. Simulations revealed that particle vibration was simple harmonic and had the same frequency and amplitude as the excitation at low excitation frequencies and amplitudes. Particles vibrated at their own frequencies and amplitudes when the excitation frequency and amplitude were high. Both the DEM model simulations and the experimental data showed that particles not only vibrated locally but also moved globally.
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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 | 0.000 |
| 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 | 0.001 | 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".