Generation of non-overlapping fiber architecture
Bibliographic record
Abstract
Numerical models generating actual fiber architecture by including parameters such as the fiber geometry and arrangement are a powerful tool to explore the relation between the fiber architecture and mechanical properties.The generation of virtual architectures of fibrous materials is the first step toward the computation of their physical properties.In this work, a realistic 3D model is developed to describe the architecture of a complex fiber structure.The domain of application of the model could include natural fibers composites, wood fibers materials, papers, mineral and steel wools and polymer networks.The model takes into account the complex geometry of the fiber arrangement in which a fiber can be modeled with a certain degree of bending while keeping a main fiber orientation.The model is built in two steps.First, fibers are generated as a chain of overlapping spheres or as a chain of overlapping sphero-cylinders.At the end of the first step, a system of overlapping fibers is obtained.In order to obtain a hard-core configuration where fibers cannot overlap other fibers, we use an iterative method called the force-biased algorithm.It applies virtual forces on each point of the fiber: a repulsion force to suppress the overlap between two fibers and a bending and stretching force to ensure that the fiber structure is kept unchanged.The model can be used as the geometrical basis for further finite-element modelling.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".