Model reduction methodology for computational simulations of endovascular repair

TL;DR

This paper introduces a systematic model reduction methodology using equivalent shell models to significantly decrease computational costs in finite element simulations of endovascular repair, maintaining accuracy for clinical applications.

Contribution

The authors develop a shell-based model reduction approach that simplifies complex FEA simulations of stent-grafts, reducing computational time by up to tenfold while preserving accuracy.

Findings

Computational time reduced by a factor of 6 to 10.

Shell models show very good agreement with full FEA models.

Methodology effectively predicts stent-graft deployment positions.

Abstract

Endovascular aneurysm repair (EVAR) is a current alternative treatment for thoracic and abdominal aortic aneurysms, but is still sometimes compromised by possible complications such as device migration or endoleaks. In order to assist clinicians in preventing these complications, finite element analysis (FEA) is a promising tool. However, the strong material and geometrical nonlinearities added to the complex multiple contacts result in costly finite-element models. To reduce this computational cost, we establish here an alternative and systematic methodology to simplify the computational simulations of stent-grafts (SG) based on FEA. The model reduction methodology relies on equivalent shell models with appropriate geometrical and mechanical parameters. It simplifies significantly the contact interactions but still shows very good agreement with a complete reference finite-element model. Finally, the computational time for EVAR simulations is reduced of a factor 6 to 10. An application is shown for the deployment of a SG during thoracic endovascular repair, showing that the developed methodology is both effective and accurate to determine the final position of the deployed SG inside the aneurysm.