Multiscale modeling of rock fracture in comminution — A comparative study of FEM accuracy and DEM scalability
Bibliographic record
Abstract
The growing global demand for minerals and metals, coupled with the need for improved energy and water efficiency in resource extraction, has led to the use of numerical modeling, particularly the discrete element method (DEM), to evaluate and optimize comminution processes that account for a significant portion of the energy consumption in mineral and metal extraction. Despite advancements, a significant challenge remains in balancing the local resolution of fractures at the rock particle level, where physics-based material models using the finite element method (FEM) have excelled, with the resolution of industrial-scale total particle interactions within the machine system. This work explores the high-resolution fracture of rock particles using an established material model implemented within FEM as a valuable reference for fractures with a balanced mid-level resolution achieved through a bonded discrete element method applicable to industrial-scale systems. Brazilian tests were performed on two rock types to calibrate the models. Single particle breakage (SPB) experiments employing digital image correlation (DIC) were conducted to evaluate the performance of the models. Finally, the DEM model was demonstrated in an industrial-scale cone crusher application. The results show good agreement for the highly resolved FEM approach (requiring only two material parameters to be determined, which is particularly advantageous for generating virtual particle breakage data across various rock materials, shapes, and sizes) and reasonable agreement for the DEM fracture response, which is attributed to the much coarser mesh used that does not capture the crumbling mechanism (as revealed by the comparison between the two numerical approaches). Despite these discrepancies, the cone crusher predictions fall within the expected ranges for the system response at the machine level.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".