Computational Modeling of Coupled Dynamic Phase Transformations in Shape Memory Alloys
Bibliographic record
Abstract
In spite of several modeling approaches to understand the shape memory alloy (SMA) behaviour, many difficulties exist because of the various limitations of the existing free energy models. Associated phase kinetics coupled with the thermoelastodynamics is still not fully tractable. A new dynamic model of 3D SMA is developed in this paper, which employs an improved version of the microscopic Landau theory. Essential properties of the single and multi-variant martensitic phase transformations are recovered using consistent steps, which eliminates the problem of non-uniqueness of energy partitioning and relaxes the over-sensitivity of the free energy due to many unknown material constants in previously reported models. The newly developed microscopic model is incorporate in a variationally formulated finite element framework. Newmark's time integration scheme is adopted and the condition for consistent iteration to solve the strongly nonlinear system at each time step is highlighted. Banded assembly of the discretized system matrix is performed in parallel. A parallel PCG method with Jacobi preconditioner is employed to solve the system at each time step.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".