AI & ML
An immersed peridynamics method for fluid-driven damage and failure of anisotropic materials
arXiv cs.NA0 views
arXiv:2603.16046v2 Announce Type: replace Abstract: The immersed peridynamics (IPD) method is a fluid-structure interaction (FSI) model to simulate fluid-driven material damage and failure of an immersed structure, in which a peridynamic (PD) constitutive correspondence model is employed within a classical immersed boundary (IB)-type framework to describe stresses, forces, and structural deformations of a structural body, instead of classical continuum mechanics. This paper introduces an extension of the IPD method to simulate fluid-driven structural deformation, damage, and failure of anisotropic materials with complex geometries. We use quadrature rules attached to finite element (FE) meshes to generate both the PD points and their associated weights, which are used to approximate the PD integrals. We demonstrate that non-uniform discretizations improve both accuracy and volume conservation of hyperelastic materials along with accurately represented boundaries. To capture realistic biomaterial behaviors, we incorporate hyperelastic constitutive models including both isotropy and anisotropy into the proposed IPD method. In addition, a ductile failure model is adopted to simulate progressive damage and failure in anisotropic materials. For non-failure cases, our numerical simulations demonstrate that the extended IPD method yields comparable accuracy with similar numbers of structural degrees of freedom for different choices of peridynamic horizon sizes. For failure tests, we investigate the effect of a fiber orientation on deformations and failure processes using realistic biomaterial models with varying fiber directions. We further demonstrate that the developed method generates grid-converged simulations of damage growth, crack formation and propagation, and rupture under large deformations, including purely fluid-driven failure processes.
Source:https://arxiv.org/abs/2603.16046PreviousOn the choice of viscous discontinuous Galerkin discretization for entropy correction artificial viscosity methodsNextAccelerating an ensemble of variational data assimilations with randomized preconditioning
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