Michael C Hillman
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Generalized Reproducing Kernel Peridynamics, in press!

6/25/2019

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My new paper on the connection between peridynamics and RKPM (and high-order peridynamics) is accepted without revision, and will be online shortly. Stay tuned!

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<Download preprint>

Title
Generalized Reproducing Kernel Peridynamics: Unification of Local and Non-Local Meshfree Methods, and an Arbitrary-order Accurate Generalized State-Based Peridynamic Formulation 

Abstract
            State-based peridynamics is a non-local reformulation of solid mechanics that replaces the force density of the divergence of stress with an integral of the action of force states on bonds local to a given position, which precludes differentiation with the aim to model strong discontinuities effortlessly. A popular implementation is a meshfree formulation where the integral is discretized by quadrature points, which results in a series of unknowns at the points under the strong-form collocation framework. In this work, the meshfree discretization of state-based peridynamics under the correspondence principle is examined and compared to traditional meshfree methods based on the classical local formulation of solid mechanics. It is first shown that the way in which the peridynamic formulation approximates differentiation can be unified with the implicit gradient approximation, and is this is termed the generalized reproducing kernel peridynamic approximation. This allows the construction of non-local deformation gradients with arbitrary-order accuracy, as well as non-local approximations of higher-order derivatives. A high-order accurate non-local divergence of stress is then proposed to replace the force density in the original state-based peridynamics, in order to obtain global arbitrary-order accuracy in the numerical solution. These two operators used in conjunction with one another is termed the generalized reproducing kernel peridynamic method. The strong-form collocation version of the method is tested against benchmark solutions to examine and verify the high-order accuracy and convergence properties of the method. 

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