Abstract
Multiscale modeling of material systems demands novel solution strategies to simulating physical phenomena that occur in a hierarchy of length scales. Majority of the current approaches involve one way coupling such that the information is transferred from a lower length scale to a higher length scale. To enable bi-directional scale-bridging, a new data-driven framework called Materials Knowledge System (MKS) has been developed recently. The remarkable advantages of MKS in establishing computationally efficient localization linkages (e.g., spatial distribution of a field in lower length scale for an imposed loading condition in higher length scale) have been demonstrated in prior work. In these prior MKS studies, the effort was focused on composite materials that had a finite number of discrete local states. As a major extension, in this work, the MKS framework has been extended for polycrystalline aggregates which need to incorporate crystal lattice orientation as a continuous local state. This extension of the MKS framework for elastic deformation of polycrystals is achieved by employing compact Fourier representations of functions defined in the crystal orientation space. The viability of this new formulation will be presented for case studies involving single and multi-phase polycrystals.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
McDowell, D.L., “A perspective on trends in multiscale plasticity,” International Journal of Plasticity, 2010. 26(9): p. 1280–1309.
Olson, G.B., “Computational design of hierarchically structured materials,” Science, 1997. 277(5330): p. 1237–1242.
Panchal, J.H., S.R. Kalidindi, and D.L. McDowell, “Key computational modeling issues in Integrated Computational Materials Engineering,” Computer-Aided Design, 2013. 45(1): p. 4–25.
Fullwood, D.T., et al., “Microstructure sensitive design for performance optimization,” Progress in Materials Science, 2010. 55(6): p. 477–562.
Niezgoda, S.R., Y.C. Yabansu, and S.R. Kalidindi, “Understanding and visualizing microstructure and microstructure variance as a stochastic process,” Acta Materialia, 2011. 59(16): p. 6387–6400.
Kröner, E., Statistical Modelling in Modelling Small Deformations of Poly crystals, J. Gittus and J. Zarka, Editors. 1986, Springer Netherlands. p. 229–291.
Kröner, E., “Bounds for effective elastic moduli of disordered materials,” Journal of the Mechanics and Physics of Solids 1977. 25(2): p. 137–155.
Fast, T. and S.R. Kalidindi, “Formulation and calibration of higher-order elastic localization relationships using the MKS approach,” Acta Materialia, 2011. 59(11): p. 4595–4605.
Kalidindi, S.R., et al., “A novel framework for building materials knowledge systems,” Computers, Materials, & Continua, 2010. 17(2): p. 103–125.
Landi, G. and S.R. Kalidindi, “Thermo-elastic localization relationships for multi-phase composites,” Computers, Materials, & Continua, 2010. 16(3): p. 273–293.
Landi, G., S.R. Niezgoda, and S.R. Kalidindi, “Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems,” Acta Materialia, 2010. 58(7): p. 2716–2725.
Adams, B.L., X. Gao, and S.R. Kalidindi, “Finite approximations to the second-order properties closure in single phase polycrystals,” Acta Materialia, 2005. 53(13): p. 3563–3577.
Fullwood, D.T., S.R. Niezgoda, and S.R. Kalidindi, “Microstructure reconstructions from 2-point statistics using phase-recovery algorithms,” Acta Materialia, 2008. 56(5): p. 942–948.
Fast, T., S.R. Niezgoda, and S.R. Kalidindi, “A new framework for computationally efficient structure-structure evolution linkages to facilitate high-fidelity scale bridging in multi-scale materials models,” Acta Materialia, 2011. 59(2): p. 699–707.
Adams, B.L., S.R. Kalidindi, and D.T. Fullwood, Microstructure Sensitive Design for Performance Optimization. 2012: Elsevier Science.
Bunge, H.J., Texture analysis in materials science: mathematical methods. 1982: Butterworths.
Yabansu, Y.C, D.K. Patel, and S.R. Kalidindi, “Calibrated localization relationships for elastic response of polycrystalline aggregates,” Acta Materialia, 2014. 81: p. 151–160.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 TMS (The Minerals, Metals & Materials Society)
About this paper
Cite this paper
Yabansu, Y.C., Kalidindi, S.R. (2015). Calibrated Localization Relationships for Polycrystalline Aggregates by Using Materials Knowledge System. In: Poole, W., et al. Proceedings of the 3rd World Congress on Integrated Computational Materials Engineering (ICME 2015). Springer, Cham. https://doi.org/10.1007/978-3-319-48170-8_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-48170-8_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48612-3
Online ISBN: 978-3-319-48170-8
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)