Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-28T11:06:11.307Z Has data issue: false hasContentIssue false
  • English
  • Français

Nondeterministic Multiscale Modeling of Biomimetic Crack Self-Healing in Nanocrystalline Solids under Mechanical Loading

Published online by Cambridge University Press:  24 March 2011

Eduard G. Karpov  and
Mykhailo Grankin
Eduard G. Karpov
Affiliation:
Civil & Materials Engineering, University of Illinois, Chicago, IL 60607, U.S.A.
Mykhailo Grankin
Affiliation:
Civil & Materials Engineering, University of Illinois, Chicago, IL 60607, U.S.A.
Get access

Abstract

A nondeterministic multiple scale approach based on numerical solution of the Monte-Carlo master equation coupled with a standard finite-element formulation of material mechanics is presented. The approach is illustrated in application to the long-term evolutionary processes of self-diffusion, precipitation and crack/void healing in nanocrystalline fcc and bcc solids. Effect of static and dynamic loading patterns on the crack healing rates are investigated. The approach is widely applicable to the modeling and characterization of advanced functional materials with evolutionary internal structure, as well as emerging behavior in materials systems.

Keywords

self-assemblysteelnanostructure
Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 1297: Symposium P – Deformation Mechanisms, Microstructure Evolution and Mechanical Properties of Nanoscale Materials , 2011 , mrsf10-1297-p03-64
Copyright
Copyright © Materials Research Society 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1] Liu, W.K., Karpov, E.G. and Park, H. Nano Mechanics and Materials, Wiley, 2006.Google Scholar
[2] Bauman, P. et al. , Computational Mechanics 42 (2008) p.511.Google Scholar
[3] Karpov, E., Park, H. and Liu, W., IJNME 70 (2007) p.351.Google Scholar
[4] To, A. and Li, S., Phys. Rev. B. 72 (2005) p.035414.Google Scholar
[5] Park, H., Karpov, E.G., Klein, P.A., Liu, W.K., Phil. Mag. 85 (2005) p.79.Google Scholar
[6] Rudd, R. and Broughton, J., Phys. Rev. B 72(14) (2005) p.144104.Google Scholar
[7] Ben Dhia, H. and Rateau, G., IJNME 62 (2005) p.1442.Google Scholar
[8] Liu, W.K., Karpov, E.G., Zhang, S., Park, H., CMAME 193 (2004) p.1529.Google Scholar
[9] Xiao, S. and Belytschko, T., CMAME 193 (2004) p.1645.Google Scholar
[10] Qian, D., Wagner, G. and Liu, W., CMAME 193 (2004) p.1603.Google Scholar
[11] Wagner, G. and Liu, W., J. Comp. Phys. 190 (2003) p.249.Google Scholar
[12] Gardiner, C. Handbook of Stochastic Methods, 2nd Ed., Springer, 1985.Google Scholar
[13] Fichthorn, K. and Weinberg, W.H., JCP 95 (1991) p.1090.Google Scholar
[14] Shinya, N., et al. , Material Sci. Forum 426 (2003), p.1107.Google Scholar
[15] Shinya, N., et al. . J. Intell. Mater. Syst. Str. 17 (2006), p.1127.Google Scholar
[16] Karpov, E.G., et al. . Phil. Magazine Lett. (7), 471–480, 2010.Google Scholar
[17] Wigner, E., Trans. of the Faraday Society 34 (1938) p.29.Google Scholar
[18] Kramers, H., Physica 7 (1940) p.284.Google Scholar