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Peridigm summary report : lessons learned in development with agile components

Parks, Michael L.; Littlewood, David J.; Salinger, Andrew G.

This report details efforts to deploy Agile Components for rapid development of a peridynamics code, Peridigm. The goal of Agile Components is to enable the efficient development of production-quality software by providing a well-defined, unifying interface to a powerful set of component-based software. Specifically, Agile Components facilitate interoperability among packages within the Trilinos Project, including data management, time integration, uncertainty quantification, and optimization. Development of the Peridigm code served as a testbed for Agile Components and resulted in a number of recommendations for future development. Agile Components successfully enabled rapid integration of Trilinos packages into Peridigm. A cost of this approach, however, was a set of restrictions on Peridigm's architecture which impacted the ability to track history-dependent material data, dynamically modify the model discretization, and interject user-defined routines into the time integration algorithm. These restrictions resulted in modifications to the Agile Components approach, as implemented in Peridigm, and in a set of recommendations for future Agile Components development. Specific recommendations include improved handling of material states, a more flexible flow control model, and improved documentation. A demonstration mini-application, SimpleODE, was developed at the onset of this project and is offered as a potential supplement to Agile Components documentation.

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Peridynamics as a rigorous coarse-graining of atomistics for multiscale materials design

Aidun, John B.; Kamm, James R.; Lehoucq, Richard B.; Parks, Michael L.; Sears, Mark P.; Silling, Stewart A.

This report summarizes activities undertaken during FY08-FY10 for the LDRD Peridynamics as a Rigorous Coarse-Graining of Atomistics for Multiscale Materials Design. The goal of our project was to develop a coarse-graining of finite temperature molecular dynamics (MD) that successfully transitions from statistical mechanics to continuum mechanics. The goal of our project is to develop a coarse-graining of finite temperature molecular dynamics (MD) that successfully transitions from statistical mechanics to continuum mechanics. Our coarse-graining overcomes the intrinsic limitation of coupling atomistics with classical continuum mechanics via the FEM (finite element method), SPH (smoothed particle hydrodynamics), or MPM (material point method); namely, that classical continuum mechanics assumes a local force interaction that is incompatible with the nonlocal force model of atomistic methods. Therefore FEM, SPH, and MPM inherit this limitation. This seemingly innocuous dichotomy has far reaching consequences; for example, classical continuum mechanics cannot resolve the short wavelength behavior associated with atomistics. Other consequences include spurious forces, invalid phonon dispersion relationships, and irreconcilable descriptions/treatments of temperature. We propose a statistically based coarse-graining of atomistics via peridynamics and so develop a first of a kind mesoscopic capability to enable consistent, thermodynamically sound, atomistic-to-continuum (AtC) multiscale material simulation. Peridynamics (PD) is a microcontinuum theory that assumes nonlocal forces for describing long-range material interaction. The force interactions occurring at finite distances are naturally accounted for in PD. Moreover, PDs nonlocal force model is entirely consistent with those used by atomistics methods, in stark contrast to classical continuum mechanics. Hence, PD can be employed for mesoscopic phenomena that are beyond the realms of classical continuum mechanics and atomistic simulations, e.g., molecular dynamics and density functional theory (DFT). The latter two atomistic techniques are handicapped by the onerous length and time scales associated with simulating mesoscopic materials. Simulating such mesoscopic materials is likely to require, and greatly benefit from multiscale simulations coupling DFT, MD, PD, and explicit transient dynamic finite element methods FEM (e.g., Presto). The proposed work fills the gap needed to enable multiscale materials simulations.

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On the two-domain equations for gas chromatography

Romero, L.A.; Parks, Michael L.

We present an analysis of gas chromatographic columns where the stationary phase is not assumed to be a thin uniform coating along the walls of the cross section. We also give an asymptotic analysis assuming that the parameter {beta} = KD{sup II}{rho}{sup II}/D{sup I}{rho}{sup I} is small. Here K is the partition coefficient, and D{sup i} and {rho}{sup i}, i = I, II are the diffusivity and density in the mobile (i = I) and stationary (i = II) regions.

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Implementing peridynamics within a molecular dynamics code

Computer Physics Communications

Parks, Michael L.; Lehoucq, Richard B.; Plimpton, Steven J.; Silling, Stewart A.

Peridynamics (PD) is a continuum theory that employs a nonlocal model to describe material properties. In this context, nonlocal means that continuum points separated by a finite distance may exert force upon each other. A meshless method results when PD is discretized with material behavior approximated as a collection of interacting particles. This paper describes how PD can be implemented within a molecular dynamics (MD) framework, and provides details of an efficient implementation. This adds a computational mechanics capability to an MD code, enabling simulations at mesoscopic or even macroscopic length and time scales. © 2008 Elsevier B.V.

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Peridynamics with LAMMPS : a user guide

Parks, Michael L.; Plimpton, Steven J.; Lehoucq, Richard B.; Silling, Stewart A.

Peridynamics is a nonlocal formulation of continuum mechanics. The discrete peridynamic model has the same computational structure as a molecular dynamic model. This document details the implementation of a discrete peridynamic model within the LAMMPS molecular dynamic code. This document provides a brief overview of the peridynamic model of a continuum, then discusses how the peridynamic model is discretized, and overviews the LAMMPS implementation. A nontrivial example problem is also included.

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Results 76–100 of 116
Results 76–100 of 116