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The Biomedical Device Laboratory is working on developing computational tools to better predict the response of and optimize the design of biomedical devices which incorporate shape memory polymers. The computational tools include heat transfer analysis, computational fluid dynamics (CFD), and finite element analysis (FEA).

Heat transfer and CFD analyses are performed to better understand the impact of proposed devices on the arterial walls, aneurysm walls, and patient blood flow. The simulations model SMP foam, the aneurysm walls, and the non-Newtonian viscosity of blood. The analyses often consider the effects of varying the Reynolds number and device geometry (e.g., SMP foam diameter) to evaluate the difference in flow characteristics before and after device deployment. Further, the analyses provide a means for estimating wall shear stresses and thermal damage as a result of thermal activation of the SMP devices. Particle image velocimetry (PIV) is currently being used to obtain velocity and temperature data which can be used to validate additional modeling efforts.

A comparison of pathlines for particles in a basilar aneurysm as treated with a 5.3 mm diameter (left) SMP foam and a 8.5 mm diameter (right) SMP foam [source: Ortega, et al, 2007]
A comparison of pathlines for particles in a basilar aneurysm as treated with a 5.3 mm diameter (left) SMP foam and a 8.5 mm diameter (right) SMP foam [source: J.M. Ortega, et al, 2007]

In addition, constitutive models are being developed to better capture the large deformation (>10% strain) response of shape memory polymers. The material properties of the models are calibrated using experimentally obtained data. After being calibrated, the models are validated using additional experiments. Then, the models are implemented in 1-D using MATLAB as well as in 3-D with ABAQUS, a finite element software, through a user material subroutine (UMAT). The implementation into ABAQUS allows for modeling SMP biomedical devices with complex geometries as subjected to complex loading conditions. Such analyses not only assist in predicting the performance of these devices, but also provide insight for the optimization of device design.



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