Neural networks meet phase-field: A hybrid fracture model

IF 7.3 1区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-03-30 DOI:10.1016/j.cma.2025.117937
Franz Dammaß , Karl A. Kalina , Markus Kästner
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Abstract

We present a hybrid phase-field model of fracture at finite deformation and its application to quasi-incompressible, hyperelastic rubber. The key idea is to combine the predictive capability of the well-established phase-field approach to fracture with a physics-augmented neural network (PANN) that serves as a flexible, high-fidelity model of the response of the bulk material. To this end, recently developed neural network approaches are developed further to better meet specific requirements of the phase-field framework. In particular, a novel architecture for a hyperelastic PANN is presented, that enables a decoupled description of the volumetric and the isochoric response based on a corresponding additive decomposition of the Helmholtz free energy. This is of particular interest when modelling fracture of soft quasi-incompressible solids with the phase-field approach, where a weakening of the incompressibility constraint in fracturing material is required. In addition, such an additive decomposition of the free energy is a prerequisite for the application of several split methods, i.e. decompositions of free energy into degraded and non-degraded portions, which can improve model behaviour under multiaxial stress states. For the formulation of the hybrid model, we define a pseudo-potential, in which the phase-field ansatz for fracture dissipation is combined with a polyconvex PANN model of the isochoric response. The PANN is formulated in polyconvex isochoric invariants. As a result, it can be shown that the PANN fulfils all desirable properties of hyperelastic potentials by construction. In particular, it is proven to be zero and take a global minimum for the undeformed state, which does also hold in case of deviations away from incompressibility. Moreover, a classical mixed displacement-pressure formulation of incompressibility based on the perturbed Lagrangian approach is included. Thereby, a relaxation of the incompressibility constraint in fracturing material is applied. This weakening of incompressibility is shown in to be essential in order to prevent numerical issues in the simulation, which would otherwise arise from the presence of zones showing both negligible isochoric stiffness and very high resistance against volume changes. The model is implemented in the finite element framework FEniCSx and studied by means of several examples. To this end, training and validation of the PANN are performed based on experimental data from the literature, and the hybrid fracture model is then verified against results of fracture experiments.
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神经网络满足相场:一种混合裂缝模型
提出了一种有限变形时断裂的混合相场模型,并将其应用于拟不可压缩超弹性橡胶。关键思想是将成熟的相场断裂方法的预测能力与物理增强神经网络(PANN)相结合,后者可作为块体材料响应的灵活、高保真模型。为此,最近开发的神经网络方法进一步发展,以更好地满足相场框架的特定要求。特别地,提出了一种新的超弹性PANN结构,它能够基于相应的亥姆霍兹自由能的加性分解来解耦描述体积响应和等时响应。当用相场方法模拟软质准不可压缩固体的断裂时,这是特别有趣的,因为需要削弱压裂材料的不可压缩性约束。此外,这种自由能的加性分解是几种分裂方法应用的先决条件,即将自由能分解为退化和非退化部分,这可以改善模型在多轴应力状态下的行为。对于混合模型的公式,我们定义了一个伪势,将断裂耗散的相场分析与等时响应的多凸PANN模型相结合。PANN是用多凸等时不变量表示的。结果表明,通过构造,该泛神经网络满足超弹性势的所有期望性质。特别地,它被证明为零,并在未变形状态下取全局最小值,这也适用于偏离不可压缩的情况。此外,基于摄动拉格朗日方法,给出了经典的不可压缩性混合位移-压力公式。因此,对压裂材料的不可压缩性约束进行了松弛。不可压缩性的减弱是必要的,以防止模拟中的数值问题,否则,这些问题将由显示可忽略不计的等弦刚度和对体积变化的非常高的阻力的区域的存在而引起。该模型在有限元框架FEniCSx中实现,并通过实例进行了研究。为此,基于文献中的实验数据对PANN进行训练和验证,并根据断裂实验结果对混合断裂模型进行验证。
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来源期刊
CiteScore
12.70
自引率
15.30%
发文量
719
审稿时长
44 days
期刊介绍: Computer Methods in Applied Mechanics and Engineering stands as a cornerstone in the realm of computational science and engineering. With a history spanning over five decades, the journal has been a key platform for disseminating papers on advanced mathematical modeling and numerical solutions. Interdisciplinary in nature, these contributions encompass mechanics, mathematics, computer science, and various scientific disciplines. The journal welcomes a broad range of computational methods addressing the simulation, analysis, and design of complex physical problems, making it a vital resource for researchers in the field.
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