Strategies for integration of 3-D experimental data with modeling and simulation |
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Authors: | A C Lewis S M Qidwai M Jackson A B Geltmacher |
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Affiliation: | (1) Multifunctional Materials Branch, Code 6350, United States Naval Research Laboratory, Washington, DC, USA;(2) Science Applications International Corporation, c/o United States Naval Research Laboratory, Washington, DC 20375, USA |
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Abstract: | For the most comprehensive modeling and prediction of materials behavior at the microscale, experimentally measured three-dimensional
(3-D) microstructural datasets must be incorporated as initial input into computational models. Although the capability to
collect and store large amounts of 3-D microstructural data is advancing continuously, computational resources for the processing
and simulation can limit the amount of data that can be analyzed. Depending on the features and properties of interest, several
approaches can be applied to optimize processing, reduce the amount of data that needs to be simulated, and increase the efficiency
of simulations to maximize the statistical significance of microstructure analyses. This paper presents examples of four such
approaches to efficient integration of large 3-D datasets into modeling and simulations of mechanical behavior in an efficient
yet statistically significant manner. |
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