Performance optimization of GJK collision detection in discrete element simulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a comprehensive performance analysis of the Gilbert-Johnson-Keerthi (GJK) algorithm and its variants in the context of Discrete Element Method (DEM) simulations. Various optimization techniques, including bounding volumes, different distance sub-algorithms, Nesterov acceleration, and temporal coherence are investigated to evaluate their impact on computational efficiency for different particle shapes and aspect ratios. The study considers both static packing and rotating drum benchmarks, covering a wide range of particle geometries such as cubes, icosahedrons, cylinders, and superquadrics. Our findings indicate that the choice of bounding volume technique significantly affects performance, with oriented bounding cylinder outperforming oriented bounding boxes for elongated particles. Nesterov acceleration, although theoretically promising, generally shows limited performance improvements except for highly spherical particles. Temporal coherence, while beneficial for certain particle shapes and moderate aspect ratios, is less effective when particles are highly elongated or distant from each other. These results offer valuable insights for optimizing DEM simulations involving complex particle shapes and varying elongation levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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