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In order to reduce the risk of intracranial aneurysm rupture after implantation of stent with trapezoidal cross-section wire, attentions are paid to the optimization of cross-section of stent wire. 38 models of different extents of baseline of stent with trapezoidal cross-section were created in SolidWorks 2008. Then ANSYS 12.0 with fluid-solid inter action method and Generalized Regression Neural Network(GRNN) were used to map the nonlinear relationship between the maximal pressure gradient on aneurysm wall and the extent of baseline of the trapezoidal cross-section of stent wire. Genetic algorithm (GA) was employed to obtain the optimum value of baseline of the stent by minimizing the maximal pressure gradient. The results indicate that the maximal pressure gradient was reduced by 7.86% after optimization compared with the traditional stent with rectangular cross-section wire. The combination method of GRNN and GA was an effective approach for stent optimization.
In order to reduce the risk of intracranial aneurysm rupture after implantation of stent with trapezoidal cross-section wire, attentions are paid to the optimization of cross-section of stent wire. 38 models of different extents of baseline of stent with trapezoidal cross-section were created in SolidWorks 2008. Then ANSYS 12.0 with fluid-solid inter action method and Generalized Regression Neural Network (GRNN) were used to map the nonlinear relationship between the maximal pressure gradient on aneurysm wall and the extent of baseline of the trapezoidal cross-section of Genetic algorithm (GA) was employed to obtain the optimum value of baseline of the stent by minimizing the maximal pressure gradient. The results that that the maximal pressure gradient was reduced by 7.86% after optimization compared with the traditional stent with rectangular cross -section wire. The combination method of GRNN and GA was an effective approach for stent optimization.