@Article{CiCP-10-607, author = {}, 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-LoƩve 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} }