Stochastic Finite Element Analysis Framework for Modelling Mechanical Properties of Particulate Modified Polymer Composites
Bibliographic record
Abstract
Polymers have become indispensable in many engineering applications because of their attractive properties, including low volumetric mass density and excellent resistance to corrosion. However, polymers typically lack in mechanical, thermal, and electrical properties that may be required for certain engineering applications. Therefore, researchers have been seeking to improve properties by modifying polymers with particulate fillers. In the research presented herein, a numerical modeling framework was employed that is capable of predicting the properties of binary or higher order composites with randomly distributed fillers in a polymer matrix. Specifically, mechanical properties, i.e., elastic modulus, Poisson's ratio, and thermal expansion coefficient, were herein explored for the case of size-distributed spherical filler particles. The modeling framework, employing stochastic finite element analysis, reduces efforts for assessing material properties compared to experimental work, while increasing the level of accuracy compared to other available approaches, such as analytical methods. Results from the modeling framework are presented and contrasted with findings from experimental works available in the technical literature. Numerical predictions agree well with the non-linear trends observed in the experiments, i.e., elastic modulus predictions are within the experimental data scatter, while numerical data deviate from experimental Poisson's ratio data for filler volume fractions greater than 0.15. The latter may be the result of morphology changes in specimens at higher filler volume fractions that do not comply with modelling assumptions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".