Density Dependence of the Macroscale Superelastic Behavior of Porous Shape Memory Alloys: A Two-Dimensional Approach
Bibliographic record
Abstract
Porous Shape Memory Alloys (SMAs) are of particular interest for many industrial applications, as they combine intrinsic SMA (shape memory effect and superelasticity) and foam characteristics. The computational cost of direct porous material modeling is however extremely high, and so designing porous SMA structure poses a considerable challenge. In this study, an attempt is made to simulate the superelastic behavior of porous materials via the modeling of fully dense structures with material properties modified using a porous/bulk density ratio scaling relation. Using this approach, direct modeling of the porous microstructure is avoided, and only the macroscale response of the model is considered which contributes to a drastic reduction of the computational cost. Foam structures with a gradient of porosity are also studied, and the prediction made using the fully dense material model is shown to be in agreement with the mesoscale porous material model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".