Smoothed Boundary Method for simulating bulk and grain boundary transport in complex polycrystalline microstructures

Yu, Hui-Chia; Choe, Min-Ju; Amatucci, Glenn G.; Chiang, Yet-Ming; Thornton, K.

doi:10.1016/j.commatsci.2016.04.028

Title: Smoothed Boundary Method for simulating bulk and grain boundary transport in complex polycrystalline microstructures

Journal Article · Thu May 05 00:00:00 EDT 2016 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2016.04.028· OSTI ID:1387836

Yu, Hui-Chia ^[1]; Choe, Min-Ju ^[1]; Amatucci, Glenn G. ^[2]; Chiang, Yet-Ming ^[3];

^[1]

Univ. of Michigan, Ann Arbor, MI (United States)
Rutgers Univ., North Brunswick, NJ (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Grain boundaries have a major impact on material properties, but explicit consideration of the complex geometries of grain structures in simulations poses a challenge. In this paper, we present a general method for incorporating the effect of grain boundaries based on the Smoothed Boundary Method (SBM). By using multiple domain parameters to define the domains of different grains, this method circumvents time-consuming mesh generation steps that are associated with finite element calculations involving complex microstructures. To validate the approach, we evaluate the accuracy of the SBM against the sharp interface method. The capabilities of this approach were demonstrated through simulations of surface and grain boundary diffusion, as well as those of electrochemical impedance spectroscopy. We conclude this method is applicable to many material systems in which grain boundaries play a crucial role.

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Research Organization:: Energy Frontier Research Centers (EFRC) (United States). Northeastern Center for Chemical Energy Storage (NECCES)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division

Grant/Contract Number:: SC0001294; SC0012583; CI-1053575; AC02-05CH1123; AC02-05CH11231

OSTI ID:: 1387836

Alternate ID(s):: OSTI ID: 1326424

Journal Information:: Computational Materials Science, Vol. 121, Issue C; Related Information: NECCES partners with Stony Brook University (lead); Argonne National Laboratory; Binghamton University; Brookhaven National University; University of California, San Diego; University of Cambridge, UK; Lawrence Berkeley National Laboratory; Massachusetts Institute of Technology; University of Michigan; Rutgers University; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 8 works

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