Phase-field modelings of fracture investigate the influence of interfacial effects on damage and optimal material distribution in brittle inclusion-matrix structures
Bibliographic record
Abstract
This present work uses the phase-field modelings to investigate the influence of interfacial effects on damage and mechanical behavior, as well as the optimal distribution of the inclusion shape within brittle inclusion-matrix structures in various typical cases. These two constituent phases in the structures are assumed to be either isotropic or anisotropic. To achieve these goals, this work will: (i) use the phase-field modelings either considering or neglecting interfacial debonding, and the anisotropic phase-field modeling; (ii) determine and incorporate the strain tensor orthogonal decompositions into each specific phase-field modeling to enhance the accuracy and effectiveness of the simulation methods; (iii) combine the phase-field modelings with the BESO topology optimization algorithm to analyze the influence of interfacial effects on relationship curves and the optimal distribution of the inclusion shape. Through proposed numerical examples, it is demonstrated that the interfacial effects strongly influence crack paths, behavior curves, and optimal material distribution in structures. When considering interfacial effects, cracks are almost unable to penetrate into the inclusion phase. However, when neglecting interfacial effects, cracks propagate into the inclusion phase. This reason makes the structure more difficult to damage than when considering the interfacial effects, as evidenced by greater peak load values in behavior curves and greater total fracture resistance of the material. Especially in the example of inclusion phase optimization, the total fracture resistance value of the case neglecting interfacial effects is more than 107.9% greater than that considering interfacial effects.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".