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GP-ME-1P4EFT4MechanicalReady

Shape-memory-alloy actuator cycle model

A coupled electrothermal and thermomechanical study of a nickel-titanium wire actuator across current, stress, repeated cycling, model verification, degradation, and parameter uncertainty.

Shape-memory-alloy actuator cycle model project visual
GP-ME-1P4EFT4 · Mechanical
  • Python
  • NumPy
  • SciPy
  • Pandas
  • Matplotlib
  • Jupyter

Project definition

Problem statement

A shape-memory-alloy wire contracts because electrical heating changes temperature and martensitic phase fraction, but the response also depends on stress, heat loss, resistance, transformation temperatures, hysteresis, and repeated cycling.

The engineering problem is to connect those effects in one traceable actuator model and distinguish useful comparative predictions from material calibration, fatigue life, and hardware qualification.

Project objectives

  • Model Joule heating, convection, radiation, temperature-dependent resistance, transformation state, strain, and force-related stress effects.
  • Compare 14, 16, and 18 V operating cases at 100 MPa and selected higher-stress cases.
  • Track peak temperature, contraction, energy, phase fraction, and performance retention through 120 cycles.
  • Verify the thermal solution against an analytical constant-property benchmark and check time-step convergence.
  • Propagate ten thousand seeded parameter samples and rank the inputs associated with final-cycle contraction.

Project structure

Project components

01

Electrothermal model

Calculates current, temperature-dependent resistance, Joule input, convection, radiation, and wire temperature.

02

Transformation model

Tracks heating and cooling transformation windows, stress shifting, phase fraction, and transformation strain.

03

Cycle model

Applies the declared eight-second heating and fourteen-second cooling schedule across repeated cycles.

04

Degradation study

Represents declared functional performance loss separately from structural fatigue or fracture.

05

Verification and uncertainty

Runs analytical, energy, time-step, and ten-thousand-sample parameter studies.

06

Evidence pipeline

Writes complete CSV, JSON, figure, test, document, and container evidence.

Methodology

Project workflow

  1. 01
    Load the study

    The released configuration defines wire geometry, material parameters, heat loss, cycle timing, cases, and uncertainty bounds.

  2. 02
    Run each case

    The coupled model advances temperature, resistance, phase state, strain, and energy through 120 cycles.

  3. 03
    Retain performance

    Cycle summaries and selected histories preserve peak temperature, contraction, phase response, and energy closure.

  4. 04
    Challenge assumptions

    Analytical thermal comparison, time-step refinement, stress cases, degradation, and uncertainty test the baseline interpretation.

  5. 05
    Review the evidence

    Tables, figures, tests, and declared boundaries connect every headline value to retained data.

Demonstration scenario

The selected 16 V and 100 MPa case reaches 74.89 C and 8.38 mm contraction in the first cycle. It retains 7.94 mm, or 94.75 percent, at cycle 120. The analytical thermal benchmark has 0.034 C maximum error, and the seeded uncertainty study identifies transformation strain as the dominant input.

Engineering

Tools and method

Tools
The project uses Python, NumPy, SciPy, Pandas, Matplotlib, Jupyter for subject analysis, simulation, and results.
Physical model
A transparent lumped wire model couples electrical resistance, heat transfer, phase transformation, stress shift, and contraction.
Numerical analysis
Python, NumPy, and SciPy support deterministic integration, analytical comparison, convergence, and uncertainty calculations.
Evidence
Pandas, CSV, JSON, PNG, SVG, and Matplotlib retain the complete result trail.
Verification
Seventeen tests, 92.57 percent branch-aware coverage, dependency audit, repository checks, and a digest-pinned container run.

Testing

Evaluation

Evaluation measures

  • Peak wire temperature and transformation state
  • First-cycle and cycle-120 contraction
  • Performance retention across repeated cycling
  • Analytical thermal benchmark error and cycle energy closure
  • Time-step convergence of the peak temperature
  • Uncertainty distribution and ranked parameter sensitivity

Project boundaries

  • The material and heat-transfer values are representative engineering parameters, not a calibration certificate for a commercial wire batch.
  • The lumped model assumes uniform wire temperature and does not resolve local contact resistance, grip heat sinking, spatial phase fronts, or wire bending.
  • The degradation law describes functional contraction loss and is not a crack-growth, fatigue-life, or fracture model.
  • Results support study and comparative design reasoning, not medical, aerospace, safety-critical, or hardware certification.

Included

  1. 01Complete Python source code
  2. 02Six operating cases with 120 electrothermal cycles per case
  3. 03CSV and JSON results with fifteen labelled project figures
  4. 0471-page project report in PDF and editable Word formats
  5. 0514-page setup and usage guide in PDF and editable Word formats
  6. 0655 annotated references and three attributed literature images
  7. 0717 automated tests with 92.57 percent branch-aware coverage

Project record

No information is collected on this page.

Permanent project ID
GP-ME-1P4EFT4
Catalogued
21 Aug 2026
Completed
26 Aug 2026
Verified
26 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.