Identification of microstructural descriptors characterizing the macro-behavior of heterogeneous random fibrous media
Bibliographic record
Abstract
This work is concerned with the multiscale prediction of the transport properties associated with thermocompressed materials as recycled cotton felts bonded with petro-sourced fibers (Co-PET/PET).First, a geometric characterization is performed on the studied sample using scanning electron microscopy to identify the main microstructural descriptors (fiber angular orientation, fiber diameter polydiversity).Second, two representative volume elements (RVEs) of the sample are built: one for estimating the low-frequency transport parameters and one for estimating the the high-frequency transport parameters.Each RVE is built with rectilinear fibers parameterized by the probability density function of the fiber orientation and an appropriate weighted diameter.For the low-frequency RVE, a volume-weighted mean diameter is used, and an inverse volume-weighted mean diameter is used for the high-frequency RVE.These two RVEs make it possible to estimate the transport parameters in low and high frequency asymptotic behaviors using numerical homogenization methods.Finally, the estimated transport parameters are successfully compared to experimental measurements.The results demonstrate the role of the diameter polydispersity on the transport properties of random fibrous structures.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".