An accurate higher order plate theory for tailoring the properties of functionally graded porous media
Bibliographic record
Abstract
Optimization of porous material microstructures can lead to the design of lightweight foams that can effectively withstand applied loads and mitigate damage. Functionally graded foams have shown several advantages in experimental studies, including tailorable fracture toughness, ability to withstand over 50% strain without a significant decrease in strength and tailorable density. An important aspect of the use of such functionally graded porous media (FGPM) in structural applications is their weight-saving potential. An accurate analysis tool can help in understanding the parameters that will be best suited for a given application. A higher order plate theory is being developed in this work that accounts for extensibility and parabolic transverse shear strain. The developed theory considers the coupling between principal modes of plate deformation which enables capturing the anisotropic and heterogeneous nature of FGPM. Pores size and shape is assumed to vary through the plate thickness and that being accounted for through homogenization techniques. The accuracy of the theory is being validated against experimental data and existing 3D elasticity solutions. The mechanical response to static stimulations was tested in the scope of local pore size/shape and overall growth rate through the plate thickness.
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) | 0.001 | 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 | 0.001 | 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".