Metamodeling of constitutive model using Gaussian process machine learning
Metamodeling of constitutive model using Gaussian process machine learning
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DOI：
10.1016/j.jmps.2021.104532
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发表时间：
2021-09
影响因子：
5.3
通讯作者：
Jikun Wang;Tianjiao Li;Fan Cui;C. Hui;Jingjie Yeo;A. Zehnder
中科院分区：
文献类型：
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作者：
Jikun Wang;Tianjiao Li;Fan Cui;C. Hui;Jingjie Yeo;A. Zehnder
提出了一种基于奇异值分解(SVD)和高斯过程机器学习的方法来建立本构模型的元模型，该模型模拟了时间相关和非线性行为。为了验证这一方法，我们将其应用于一种非线性粘弹性(聚乙烯醇)水凝胶(PVA)的材料参数的测定。使用元模型，我们能够快速生成跨越广泛材料参数的大量数据点的应力历史，而无需显式地求解PVA凝胶的本构模型。为了确定材料参数，我们将元模型预测的应力历史与由单轴拉伸、循环和松弛试验组成的实验室实验的观测应力历史进行了比较。元模型的效率使我们能够在短时间内确定控制PVA凝胶随时间变化的本构模型的材料参数。该方法表明，存在多组能真实再现实验数据的材料参数。此外，我们的方法揭示了本构模型中材料参数之间的重要关系。虽然重点放在PVA凝胶体系上，但该方法可以很容易地转移到为任何材料模型建立元模型。
A method based on Singular Value Decomposition (SVD) and Gaussian process machine learning is proposed to build a metamodel of a constitutive model that models time dependent and nonlinear behavior. To test this method, we apply it to determine the material parameters of a nonlinear viscoelastic (poly(vinylalcohol)) hydrogel (PVA). Using the metamodel, we are able to rapidly generate the stress histories for a large set of data points spanning a wide range of material parameters without solving the constitutive model of the PVA gel explicitly. To determine the material parameters, we compare the stress histories predicted by the metamodel with the observed stress histories from laboratory experiments consisting of uniaxial tension cyclic and relaxation tests. The efficiency of the metamodel allows us to determine the material parameters of the constitutive model governing the time-dependent behavior of the PVA gel in a short time. The proposed method shows that there exist many sets of material parameters that can faithfully reproduce the experimental data. Further, our method reveals important relationships between the material parameters in the constitutive model. Although the focus is on the PVA gel system, the method can be easily transferred to build a metamodel for any material model.