Convergence Analysis and Numerical Studies for Linearly Elastic Peridynamics with Dirichlet-Type Boundary Conditions
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Abstract (arXiv)
The nonlocal models of peridynamics have successfully predicted fractures and deformations for a variety of materials. In contrast to local mechanics, peridynamic boundary conditions must be defined on a finite volume region outside the body. Therefore, theoretical and numerical challenges arise in order to properly formulate Dirichlet-type nonlocal boundary conditions, while connecting them to the local counterparts. While a careless imposition of local boundary conditions leads to a smaller effective material stiffness close to the boundary and an artificial softening of the material, several strategies were proposed to avoid this unphysical surface effect. In this work, we study convergence of solutions to nonlocal state-based linear elastic model to their local counterparts as the interaction horizon vanishes, under different formulations and smoothness assumptions for nonlocal Dirichlet-type boundary conditions. Our results provide explicit rates of convergence that are sensitive to the compatibility of the nonlocal boundary data and the extension of the solution for the local model. In particular, under appropriate assumptions, constant extensions yield frac12 order convergence rates and linear extensions yield frac32 order convergence rates. With smooth extensions, these rates are improved to quadratic convergence. We illustrate the theory for any dimension d≥ 2 and numerically verify the convergence rates with a number of two dimensional benchmarks, including linear patch tests, manufactured solutions, and domains with curvilinear surfaces. Numerical results show a first order convergence for constant extensions and second order convergence for linear extensions, which suggests a possible room of improvement in the future convergence analysis.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jun 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2.
4 papers.
- 2024Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responsescited by 18
- 2021A data-driven peridynamic continuum model for upscaling molecular dynamicscited by 17
- 2024Heterogeneous Peridynamic Neural Operators: Discover Biotissue Constitutive Law and Microstructure From Digital Image Correlation Measurementscited by 4
- 2023Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fracturescited by 3
- 2024Heterogeneous Peridynamic Neural Operators: Discover Biotissue Constitutive Law and Microstructure From Digital Image Correlation Measurements27 Mar 2024
- 2024Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responses11 Jan 2024
- 2023Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures11 Jan 2023
- 2021A data-driven peridynamic continuum model for upscaling molecular dynamics4 Aug 2021