arrow
Volume 10, Issue 3
Thermal Response Variability of Random Polycrystalline Microstructures

Bin Wen, Zheng Li & Nicholas Zabaras

Commun. Comput. Phys., 10 (2011), pp. 607-634.

Published online: 2011-10

Export citation
  • Abstract

A data-driven model reduction strategy is presented for the representation of random polycrystal microstructures. Given a set of microstructure snapshots that satisfy certain statistical constraints such as given low-order moments of the grain size distribution, using a non-linear manifold learning approach, we identify the intrinsic low-dimensionality of the microstructure manifold. In addition to grain size, a linear dimensionality reduction technique (Karhunun-Loéve Expansion) is used to reduce the texture representation. The space of viable microstructures is mapped to a low-dimensional region thus facilitating the analysis and design of polycrystal microstructures. This methodology allows us to sample microstructure features in the reduced-order space thus making it a highly efficient, low-dimensional surrogate for representing microstructures (grain size and texture). We demonstrate the model reduction approach by computing the variability of homogenized thermal properties using sparse grid collocation in the reduced-order space that describes the grain size and orientation variability.

  • Keywords

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{CiCP-10-607, author = {Bin Wen, Zheng Li and Nicholas Zabaras}, title = {Thermal Response Variability of Random Polycrystalline Microstructures}, journal = {Communications in Computational Physics}, year = {2011}, volume = {10}, number = {3}, pages = {607--634}, abstract = {

A data-driven model reduction strategy is presented for the representation of random polycrystal microstructures. Given a set of microstructure snapshots that satisfy certain statistical constraints such as given low-order moments of the grain size distribution, using a non-linear manifold learning approach, we identify the intrinsic low-dimensionality of the microstructure manifold. In addition to grain size, a linear dimensionality reduction technique (Karhunun-Loéve Expansion) is used to reduce the texture representation. The space of viable microstructures is mapped to a low-dimensional region thus facilitating the analysis and design of polycrystal microstructures. This methodology allows us to sample microstructure features in the reduced-order space thus making it a highly efficient, low-dimensional surrogate for representing microstructures (grain size and texture). We demonstrate the model reduction approach by computing the variability of homogenized thermal properties using sparse grid collocation in the reduced-order space that describes the grain size and orientation variability.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.200510.061210a}, url = {http://global-sci.org/intro/article_detail/cicp/7454.html} }
TY - JOUR T1 - Thermal Response Variability of Random Polycrystalline Microstructures AU - Bin Wen, Zheng Li & Nicholas Zabaras JO - Communications in Computational Physics VL - 3 SP - 607 EP - 634 PY - 2011 DA - 2011/10 SN - 10 DO - http://doi.org/10.4208/cicp.200510.061210a UR - https://global-sci.org/intro/article_detail/cicp/7454.html KW - AB -

A data-driven model reduction strategy is presented for the representation of random polycrystal microstructures. Given a set of microstructure snapshots that satisfy certain statistical constraints such as given low-order moments of the grain size distribution, using a non-linear manifold learning approach, we identify the intrinsic low-dimensionality of the microstructure manifold. In addition to grain size, a linear dimensionality reduction technique (Karhunun-Loéve Expansion) is used to reduce the texture representation. The space of viable microstructures is mapped to a low-dimensional region thus facilitating the analysis and design of polycrystal microstructures. This methodology allows us to sample microstructure features in the reduced-order space thus making it a highly efficient, low-dimensional surrogate for representing microstructures (grain size and texture). We demonstrate the model reduction approach by computing the variability of homogenized thermal properties using sparse grid collocation in the reduced-order space that describes the grain size and orientation variability.

Bin Wen, Zheng Li and Nicholas Zabaras. (2011). Thermal Response Variability of Random Polycrystalline Microstructures. Communications in Computational Physics. 10 (3). 607-634. doi:10.4208/cicp.200510.061210a
Copy to clipboard
The citation has been copied to your clipboard