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评估一种新方法,通过纳入制造应变来模拟完整的环形支架束。

Evaluation of a New Approach for Modeling Full Ring Stent Bundles with the Inclusion of Manufacturing Strains.

机构信息

Department of Mechanical and Aerospace Engineering, University of Strathclyde, Glasgow, G1 1XJ, UK.

MedAlliance, Glasgow, G52 4GA, UK.

出版信息

Ann Biomed Eng. 2020 Jan;48(1):144-156. doi: 10.1007/s10439-019-02322-0. Epub 2019 Jul 17.

Abstract

Ring stent bundles have been used in several biomedical stent-graft devices for decades, yet in the published literature, the numerical models of these structures always present significant simplifications. In this paper, a finite element (FE) ring stent bundle has been developed and evaluated with a combination of beam and surface elements. With this approach, the shape, the global stiffness and the strains of the structure can all be well predicted at a low computational cost while the approach is suitable for application to non-symmetrical, patient-specific implant simulations. The model has been validated against analytical and experimental data showing that the manufacturing strains can be predicted to a 0.1% accuracy and the structural stiffness with 0-7% precision. The model has also been compared with a more computationally expensive FE model of higher fidelity, revealing a discrepancy of 0-5% of the strain value. Finally, it has been shown that the exclusion of the manufacturing process from the simulation, a technique used in the literature, quadruples the analysis error. This is the first model that can capture the mechanical state of a full ring stent bundle, suitable for complex implant geometry simulations, with such accuracy.

摘要

环形支架束已在数十年来在几种生物医学支架移植物装置中使用,但在已发表的文献中,这些结构的数值模型始终存在显著的简化。在本文中,使用梁和表面单元的组合开发并评估了有限元(FE)环形支架束。通过这种方法,可以以较低的计算成本很好地预测结构的形状、整体刚度和应变,并且该方法适用于非对称、针对特定患者的植入物模拟。该模型已通过分析和实验数据进行了验证,表明可以将制造应变的预测精度提高到 0.1%,结构刚度的预测精度提高到 0-7%。该模型还与具有更高保真度的更计算密集型 FE 模型进行了比较,显示应变值的差异在 0-5%之间。最后,结果表明,从模拟中排除制造过程(文献中使用的技术)会将分析误差增加四倍。这是第一个能够以如此精度捕获完整环形支架束机械状态的模型,适用于复杂的植入物几何形状模拟。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8210/6927927/efc92475015b/10439_2019_2322_Fig1_HTML.jpg

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