On Compressive Brittle Fragmentation
Bibliographic record
Abstract
Dynamic brittle fragmentation is typically described using analytical and computational approaches for tensile stress‐states. However, most fragmentation applications (e.g., impact, blast) involve very large initial compressive stresses and deformations. In this study, the compressive fragmentation of brittle materials is investigated experimentally across a range of materials: silicon carbide, boron carbide, spinel, basalt and a stony meteorite. Analysis of our experimental results suggests that there exists two different regimes in the fragment size distributions, based on two brittle fragmentation mechanisms. The first is a mechanism that produces larger fragments and is associated with the structural failure of the sample being tested. This mechanism is influenced by the loading conditions (rate, stress state) and sample geometry. The second fragmentation mechanism produces comparatively smaller fragments and arises from the coalescence of fractures initiating and coalescence between defects in regions of large stresses and contact forces (e.g., between two fractured surfaces from the larger fragments). A framework is developed for comparing experimental compressive fragmentation results with tensile fragmentation theories. The compressive experimental results are shown to be adequately described by the theories using the new framework.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".