Simulation tools show clear advantages when it comes to design and optimization. They can not only provide a comprehensive evaluation of design but also help engineers understand the significance of input parameters through parametric design.

In this lecture, we will explore parametric study, optimization, and how simulation can improve the performance of a stent and its fatigue life.

The model starting point can be found **here**.

*Please utilize the mm, kg, N unit system when solving the Ansys simulation models.**Please also note that this starting point model has stabilization and has already been set up. Recall that stabilization is used such that the deployment of the stent can be performed in a static analysis, as the stabilization prevents the stent from springing open instantaneously, which will likely cause convergence issues. The settings used in this current model are similar to what was illustrated in the Interaction of Stent with Vessel and Plaque Walk Through Workshop video, but it is worth pointing out a subtle difference. In this current model, stabilization is set to “constant” using the energy method, which is perfectly acceptable since we are not concerned with the results during the stent deployment. We may wish to set this to “reduce” so that by the end of the load step the effects of stabilization are removed. In addition, in this current model, the final two load steps with the pulsatile pressure and stabilization are set to “program controlled.” This will actually copy the same stabilization settings from the prior load step, so in this case we get constant stabilization with the same energy method and numerical values for the last two load steps that follow stent deployment. In actual production usage of Ansys software, be aware of this, and if the effects of stabilization are not desired for the end of a particular load step, either set stabilization to “reduce” or “off” to deactivate stabilization completely. Stabilization energy should be a small fraction of the strain energy at the time when evaluating results, as otherwise the stabilization is artificially influencing the results.**Finally, please also note that the results you obtain in these nonlinear analyses may differ slightly from those shown in the videos. Numerical round-off due to finite machine precision can be affected by the choice of operating system, number of cores, and type of parallel processing (shared memory vs. distributed memory). Moreover, nonlinear contact and solution algorithms are often improved in each version of our software, so some changes are expected when comparing results between different releases. Thus, your results may differ slightly (within typical engineering tolerances) from the presented results, but this is to be expected for nonlinear analyses, especially for numerically unstable (e.g., underconstrained) models that may be utilized in this course.*

Completed simulation files for the above example can be found **here**.