Finite-element modeling of phase transformation in shape memory alloy wires with variable material properties
Bibliographic record
Abstract
In this paper, we address the issue of modeling the temperature distribution in a shape memory alloy (SMA) wire with variable thermal and electrical properties. This is done in the context of a one-dimensional (1D) boundary value problem where an initially martensitic SMA wire is electrically heated under zero-stress conditions. The model accounts for an evolution in the thermal conductivity, electrical resistivity and heat capacity during the phase transformation. The evolution in the 1D temperature field is found by implementing a Galerkin-based finite-element method. This is used in combination with a recursive iteration scheme to accurately determine the change in the material properties during a time step. The numerical approach is validated by comparing it with a known analytical solution with variable thermal properties. A parametric study on the SMA phase transformation indicates that, based on the considered values for the material properties, the heat capacity is the least important factor that needs to be accounted for, whereas the electrical resistivity is the most important. It is also demonstrated that the time required to complete a martensite to austenite transformation for a SMA wire subjected to an adiabatic boundary condition is lower if the model accounts for property variations. In fact, when the cyclic response of a SMA wire actuator subjected to an adiabatic boundary condition is the issue at hand, a model that does not account for property variations will predict a lower frequency of actuation than a model that does account for the property variations, as dealt with in this paper.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".