A Multi-Scale Homogenization Procedure for the Estimation of Young’s Modulus of Porous Materials by a Multi-Void Shape Model
Bibliographic record
Abstract
Transversal cross-section pores of lotus-type porous materials are generally considered circular; however, they exhibit various pore geometries, which affect their effective properties. The main objective of this work is to develop a generalized model which allows estimating the effective Young’s modulus of multi-void shape porous microstructures by exploiting a relationship developed to evaluate the effective Young’s modulus of porous materials with single-void shape. A procedure based on free software is then proposed to allow the application of the proposed generalized model on real lotus-type porous material images to estimate the effective Young’s modulus. The free tool allows the processing of real porous materials images to obtain multi-void shape microstructures and their pores parameters data. The validation of the generalized model has been established by confronting the obtained results with experimental data taken from literature; an excellent agreement was observed. Therefore, it can be concluded that the proposed procedure associated with the generalized model can be used efficiently for predicting the effective Young’s modulus of the multi-void porous materials, particularly lotus-type porous materials.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".