Nekouzadeh Ali, Pryse Kenneth M, Elson Elliot L, Genin Guy M
Department of Mechanical and Aerospace Engineering, Washington University, St. Louis, MO, USA.
J Biomech. 2007;40(14):3070-8. doi: 10.1016/j.jbiomech.2007.03.019. Epub 2007 May 17.
The fitting of quasi-linear viscoelastic (QLV) constitutive models to material data often involves somewhat cumbersome numerical convolution. A new approach to treating quasi-linearity in 1-D is described and applied to characterize the behavior of reconstituted collagen. This approach is based on a new principle for including nonlinearity and requires considerably less computation than other comparable models for both model calibration and response prediction, especially for smoothly applied stretching. Additionally, the approach allows relaxation to adapt with the strain history. The modeling approach is demonstrated through tests on pure reconstituted collagen. Sequences of "ramp-and-hold" stretching tests were applied to rectangular collagen specimens. The relaxation force data from the "hold" was used to calibrate a new "adaptive QLV model" and several models from literature, and the force data from the "ramp" was used to check the accuracy of model predictions. Additionally, the ability of the models to predict the force response on a reloading of the specimen was assessed. The "adaptive QLV model" based on this new approach predicts collagen behavior comparably to or better than existing models, with much less computation.
将准线性粘弹性(QLV)本构模型与材料数据进行拟合通常涉及有些繁琐的数值卷积。本文描述了一种处理一维准线性的新方法,并将其应用于表征重组胶原蛋白的行为。该方法基于一种包含非线性的新原理,与其他可比模型相比,在模型校准和响应预测方面所需的计算量要少得多,特别是对于平稳施加的拉伸。此外,该方法允许松弛随应变历史进行调整。通过对纯重组胶原蛋白的测试展示了该建模方法。对矩形胶原蛋白样本进行了“斜坡-保持”拉伸测试序列。来自“保持”阶段的松弛力数据用于校准一个新的“自适应QLV模型”以及文献中的几个模型,来自“斜坡”阶段的力数据用于检验模型预测的准确性。此外,还评估了模型预测样本重新加载时力响应的能力。基于这种新方法的“自适应QLV模型”预测胶原蛋白行为的能力与现有模型相当或更好,且计算量要少得多。