Simulation and probabilistic failure prediction of grafts for aortic aneurysm

Abstract

Purpose

The use of stent‐grafts to canalize aortic blood flow for patients with aortic aneurysms is subject to serious failure mechanisms such as a leak between the stent‐graft and the aorta (Type I endoleak). The purpose of this paper is to describe a novel computational approach to understand the influence of relevant variables on the occurrence of stent‐graft failure and quantify the probability of failure for aneurysm patients.

Design/methodology/approach

A parameterized fluid‐structure interaction finite element model of aortic aneurysm is built based on a multi‐material formulation available in LS‐DYNA. Probabilities of failure are assessed using an explicit construction of limit state functions with support vector machines (SVM) and uniform designs of experiments. The probabilistic approach is applied to two aneurysm geometries to provide a map of probabilities of failure for various design parameter values.

Findings

Parametric studies conducted in the course of this research successfully identified intuitive failure regions in the parameter space, and failure probabilities were calculated using both a simplified and more complex aneurysmal geometry.

Originality/value

This research introduces the use of SVM‐based explicit design space decomposition for probabilistic assessment applied to bioengineering problems. This technique allows one to efficiently calculate probabilities of failure. It is particularly suited for problems where outcomes can only be classified as safe or failed (e.g. leak or no leak). Finally, the proposed fluid‐structure interaction simulation accounts for the initiation of Type I endoleak between the graft and the aneurysm due to simultaneous fluid and solid forces.

Citation

Layman, R., Missoum, S. and Vande Geest, J. (2010), "Simulation and probabilistic failure prediction of grafts for aortic aneurysm", Engineering Computations, Vol. 27 No. 1, pp. 84-105. https://doi.org/10.1108/02644401011008531