MODELING OF HIGH-RATE HARDENING OF A POLYMER COMPOSITE MATERIAL UNDER LOADING ALONG THE REINFORCEMENT DIRECTION
Bibliographic record
Abstract
Modeling the high-rate deformation of composite structures is of great interest in the industry. Moreover, some processes such as accidents, explosions and possible impact issues require analysis of composite materials at significantly high deformation rates. The paper considers the possibility of developing a model of deformation of a composite material based on a polymer matrix and carbon fiber taking into account high-rate hardening. A feature of the study is the development of a model that takes into account a wide range of deformation rates from static to several thousand reverse seconds. Thus, tests were carried out with special equipment and samples that allow us to obtain data with such high loading speeds. The model is based on an approach considering the use of damage parameters, the so-called class of models with progressive degradation. The main innovative part of the chosen model is the formalization of the rate of deformation on the material through the damage parameter, that is, the rate of change in damage values is considered. This approach makes it possible to make constitutive relations based only on the damage parameters, which modify the stiffness and strength characteristics of composites, which greatly simplifies the modeling and analysis of material deformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".