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PyCAC: The concurrent atomistic-continuum simulation environment

Published online by Cambridge University Press:  30 January 2018

Shuozhi Xu Open the ORCID record for Shuozhi Xu [Opens in a new window] ,
Thomas G. Payne ,
Hao Chen ,
Yongchao Liu ,
Liming Xiong ,
Youping Chen  and
David L. McDowell
Shuozhi Xu*
Affiliation:
California NanoSystems Institute, University of California, Santa Barbara, Santa Barbara, California 93106-6105, USA
Thomas G. Payne
Affiliation:
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0245, USA
Hao Chen
Affiliation:
Department of Aerospace Engineering, Iowa State University, Ames, Iowa 50011, USA
Yongchao Liu
Affiliation:
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
Liming Xiong
Affiliation:
Department of Aerospace Engineering, Iowa State University, Ames, Iowa 50011, USA
Youping Chen
Affiliation:
Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida 32611-6250, USA
David L. McDowell
Affiliation:
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0245, USA; and GWW School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0405, USA
*
a)Address all correspondence to this author. e-mail: [email protected]
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Abstract

We present a novel distributed-memory parallel implementation of the concurrent atomistic-continuum (CAC) method. Written mostly in Fortran 2008 and wrapped with a Python scripting interface, the CAC simulator in PyCAC runs in parallel using Message Passing Interface with a spatial decomposition algorithm. Built upon the underlying Fortran code, the Python interface provides a robust and versatile way for users to build system configurations, run CAC simulations, and analyze results. In this paper, following a brief introduction to the theoretical background of the CAC method, we discuss the serial algorithms of dynamic, quasistatic, and hybrid CAC, along with some programming techniques used in the code. We then illustrate the parallel algorithm, quantify the parallel scalability, and discuss some software specifications of PyCAC; more information can be found in the PyCAC user’s manual that is hosted on www.pycac.org.

Keywords

metaldislocationssimulation
Type
Article
Information
Journal of Materials Research , Volume 33 , Issue 7: Focus Issue: Advanced Atomistic Algorithms in Materials Science , 13 April 2018 , pp. 857 - 871
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Copyright © Materials Research Society 2018 

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Footnotes

Contributing Editor: Vikram Gavini

References

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