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International Journal for Multiscale Computational Engineering
IF: 1.016 5-Year IF: 1.194 SJR: 0.554 SNIP: 0.82 CiteScore™: 2

ISSN Print: 1543-1649
ISSN Online: 1940-4352

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.2017021859
pages 505-523

RECONCILED TOP-DOWN AND BOTTOM-UP HIERARCHICAL MULTISCALE CALIBRATION OF BCC FE CRYSTAL PLASTICITY

Aaron E. Tallman
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
Laura P. Swiler
Optimization and Uncertainty Quantification Department, Sandia National Laboratories, P.O. Box 5800, MS 1318, Albuquerque, New Mexico 87185, USA
Yan Wang
GWW School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
David L. McDowell
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA; GWW School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

ABSTRACT

In this paper, a test for connections between models via parameter sets is developed. A set of parameters from the flow rule of a crystal plasticity model for bcc Fe is identified for connecting top-down and bottom-up information. The top-down calibration is performed using experimental measurements of single-crystal yield strength at multiple temperatures and crystallographic orientations, where a likelihood function in parameter space is informed using second-order regression surrogate modeling. A bottom-up calibration of the same model uses the parameter estimates from atomistic simulations to inform penalty functions. A constrained likelihood function incorporates the top-down and bottom-up information in one calibration of parameters. Decision making within hierarchical multiscale modeling is approached. The benefit to calibration precision brought by incorporating additional data from bottom up is considered against the uncertainty in the requisite multiscale connection. This trade-off is formulated into an empirical test of connections. Hypothetical decision making is demonstrated between multiple alternative bottom-up estimates.


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