Multi-scale Analyses of 3D Woven Composite Based On Periodicity Boundary Conditions
Bibliographic record
Abstract
In this article, two-scale 3D finite element (FE) models, the microscopic repeated unit cell (RUC) model for yarn, and mesoscopic-repeated unit cell model for woven composite, are presented to predict the effective stiffness properties of 3D woven orthogonal interlock composites. The micro-RUC model for the yarn is based on a hexagonal array of fibers. Undulation of the yarns in the novel 3-D meso-RUC for the woven composite is described by Hermit-spline function. The periodic boundary conditions are applied to the two-scale models during the 3D FE analysis in order to ensure that both the displacement and stress are continuous on the boundary surfaces. Specimens are manufactured with house-made resin transfer molding (RTM) equipment and simple tensile experiment is performed. It is found that the predicted yarn properties by the micro-RUC agree well with data computed by equations with a suitable parameter determined by experiment and the predicted effective stiffness properties of 3-D woven composites by the meso-RUC are also in good agreement with the test data. Thus the correctness of the established multi-scale models and analysis method are verified.
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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 | 0.000 |
| 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) | 0.001 | 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".