Strain-rate-dependent hybrid finite-discrete element modeling of analumina validated with Brazilian disk experiments
Bibliographic record
Abstract
A hybrid finite-discrete element model (HFDEM) coupled with Brazilian disk experiments was applied to study the mode I tensile opening failure of an alumina ceramic. In the HFDEM, a phenomenological rate-dependent cohesive law is developed to simulate the crack nucleation, propagation, and fragmentation processes, and tetrahedral elastic elements were used to represent the intact material without flaws. This model also implicitly considered the Weibull distribution of the flaws inside the material by Monte Carlo simulations while modeling processes of crack interactions and coalescence, and predicted the macroscopic variability in failure strength. The proposed model was evaluated by comparing the numerical strain-rate-dependent mode I tensile opening failure process to the Brazilian disk experiments. The simulation results demonstrated that the proposed method could predict the mechanical response of the alumina ceramic under indirect tensile loading. Once validated, this model was applied to explore the mechanism of "overestimation of the tensile strength" and "underestimation of the tensile strength" phenomena in the Brazilian tests. Overall, this study provides an improved qualitative (e.g., fracture and fragmentation behaviour) and quantitative (e.g., stress-strain response) understanding of the alumina ceramic under strain-rate-dependent tensile loading and provides a new insight for the Brazilian tests.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".