Asymptotic homogenization model for three-dimensional network reinforced composite structures
Bibliographic record
Abstract
The method of asymptotic homogenization is used to develop a comprehensive micromechanical model pertaining to three-dimensional composite structures with an embedded periodic network of isotropic reinforcements, the spatial arrangement of which renders the behavior of the given structures macroscopically anisotropic.The model developed in this paper allows the transformation of the original boundary value problem into a simpler one that is characterized by some effective elastic coefficients.These coefficients are calculated from a so-called unit cell or periodicity problem, and are shown to depend solely on the geometric and material characteristics of the unit cell and are completely independent of the global formulation of the boundary-value problem.As such, the effective elastic coefficients are universal in nature and can be used to study a wide variety of boundary value problems.The model is illustrated by means of several examples of a practical importance and it is shown that the effective properties of a given composite structure can be tailored to satisfy the requirements of a particular application by changing certain geometric parameters such as the size or relative orientation of the reinforcements.For the special case in which the reinforcements form only a two-dimensional (in-plane) network, the results converge to those of previous models obtained either by means of asymptotic homogenization or by stress-strain relationships in the reinforcements.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".