Biaxial mechanical response of bioprosthetic heart valve biomaterials to high in-plane shear
Biaxial mechanical response of bioprosthetic heart valve biomaterials to high in-plane shear
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DOI：
10.1115/1.1572518
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发表时间：
2003-06-01
影响因子：
1.7
通讯作者：
Scott, MJ
中科院分区：
文献类型：
--
作者：
Sun, W;Sacks, MS;Scott, MJ
在生物人工心脏瓣膜 (BHV) 中使用新型生物衍生生物材料需要强大的本构模型来预测广义负载状态下的机械行为。因此，有必要对所有功能变形进行严格的实验，以获得应变能量密度函数的形式常数和材料常数。在这项研究中，我们生成了一个全面的实验双轴机械数据集，其中包括使用戊二醛处理的牛心包 (GLBP) 作为代表性 BHV 生物材料的高平面内剪切应力。与我们之前的研究（Sacks，JBME，v.121，第 551-555 页，1999）相比，GLBP 在高剪切应变下表现出截然不同的响应。我们之前的 GLBP 研究中成功应用的标准 Fung 模型无法拟合高剪切数据，凸显了这一发现。为了开发合适的本构模型，我们利用伪弹性响应插值技术来指导最终模型形式的修改。开发了利用附加四次项的八参数修正 Fung 模型，该模型很好地拟合了完整的数据集。模型参数也受到限制以满足应变能函数的物理合理性。这项研究的结果强调了当前软组织模型的预测能力有限，以及需要收集整个功能范围内软组织模拟的实验数据。
Utilization of novel biologically-derived biomaterials in bioprosthetic heart valves (BHV) requires robust constitutive models to predict the mechanical behavior under generalized loading states. Thus, it is necessary to perform rigorous experimentation involving all functional deformations to obtain both the form and material constants of a strain-energy density function. In this study, we generated a comprehensive experimental biaxial mechanical dataset that included high in-plane shear stresses using glutaraldehyde treated bovine pericardium (GLBP) as the representative BHV biomaterial. Compared to our previous study (Sacks, JBME, v.121, pp. 551-555, 1999), GLBP demonstrated a substantially different response under high shear strains. This finding was underscored by the inability of the standard Fung model, applied successfully in our previous GLBP study, to fit the high-shear data. To develop an appropriate constitutive model, we utilized an interpolation technique for the pseudo-elastic response to guide modification of the final model form. An eight parameter modified Fung model utilizing additional quartic terms was developed, which fitted the complete dataset well. Model parameters were also constrained to satisfy physical plausibility of the strain energy function. The results of this study underscore the limited predictive ability of current soft tissue models, and the need to collect experimental data for soft tissue simulations over the complete functional range.