A Constitutive Model for Progressive Compressive Failure of Composites
Bibliographic record
Abstract
A continuum damage mechanics model previously developed to model the tensile response of composites has been further enhanced to simulate their nonlinear constitutive behavior under compressive loading. The refinements were based on the behavior of an analog model, which was constructed to represent the complete force—displacement response of a representative volume element of the material. The updated constitutive model accounts for mechanisms considered to be characteristic of compressive damage growth in laminated composites, such as matrix cracking, fiber kinking, and delamination. The constitutive model was implemented in the commercial explicit finite element code, LS-DYNA, and is used here to predict the quasi-static compressive response of open-hole laminates as well as the dynamic axial crushing of braided composite tubes. It is shown that the model adequately captures: (1) the damage growth and local strain fields in open-hole specimens, and (2) the failure characteristics and energy absorption of braided composite tubes. This investigation demonstrated that the new model is capable of predicting the experimentally observed compressive response in these two rather different applications, and that it potentially offers an effective means of simulating the nonlinear damaging behavior of composite materials under a variety of loading conditions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".