Predicting Packing Characteristics of Particles of Arbitrary Shapes
Why this work is in the frame
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Bibliographic record
Abstract
A computer model for particle packing is of importance in both theories and applications. By taking a very different approach from existing packing algorithms, our digital packing algorithm – called DigiPac – is able to avoid many of the difficulties normally encountered by the conventional algorithms in dealing with non-spherical particles. Using the digital approach, it is easy to pack particles of arbitrary shapes and sizes into a container of any geometry. This paper briefly describes the digital packing algorithm, but the focus is on validation of the DigiPac model through several case studies involving mono-sized non-spherical particles and also powders with different size distributions. Packing densities from DigiPac simulations are compared with those measured experimentally by ourselves in some cases and in others with data published in the literature using other models. The results show a good agreement in all the cases, which enhances our confidence in DigiPac that despite being a geometrical packing algorithm with no explicit consideration of particle interactions, it is able to predict quite accurately the packing structure of particulates whose shapes are commonly encountered in both industry and everyday life.
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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 it