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 - <p style="text-align: justify;">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.</p>