Electrical Resistance and Natural Convection Heat Transfer Modeling of Shape Memory Alloy Wires
Bibliographic record
Abstract
Shape memory alloy (SMA) actuators are becoming increasingly popular in recent years due to their properties such as large recovery strain, silent actuation and low weight. Actuation in SMA wires depends strongly on temperature which is difficult to measure directly. Therefore, a reliable model is required to predict wire temperature, in order to control the transformation, and hence the actuation, and to avoid potential degradation due to overheating. The purpose of this investigation is to develop resistance and natural convection heat transfer models to predict temperature of current-carrying SMA wires using indirect temperature measurement methods. Experiments are performed on electrically heated 0.5 mm diameter NiTi SMA wire during phase transformation. Convection heat transfer experiments are performed in an environment of air that allows for control of the ambient pressure and in turn the thermofluid properties, such as density and viscosity. By measuring convective heat loss at a range of pressures, an empirical natural convection heat transfer correlation is determined for inclination angles from horizontal to vertical, in the Rayleigh number range of 2.6 × 10−8 ≤ RaD ≤ 6.0 × 10−1. Later, effect of temperature changes on electrical resistance and other control parameters such as applied external stress, wire inclination angle, wire length and ambient pressure is investigated. Based on experimental results a resistance model is developed for SMA wires that combined with the heat transfer correlation previously derived can be used to predict temperature and natural convection heat transfer coefficient of NiTi SMA wires during phase transformation for different wire lengths and inclination angles under various applied external stresses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".