Abstract
Many of the physical properties of materials are critically dependent on their microstructure. In recent years, there has been increasing interest in using computer simulations based on phase-field models for the spatial and temporal evolution of microstructures. Although such simulations are computationally expensive, the generated set of microstructures can be stored in a repository and used for further analysis in materials design. However, such an approach requires a substantial amount of storage, for example, approximately 1 Terabyte for a single binary alloy. In this paper, we develop fast data compression and regeneration schemes for two-dimensional microstructures that can reduce storage requirements without compromising the accuracy of computed values, such as stress fields used in analysis. Our main contribution is the development and evaluation of a sparse skeletal representation scheme which outperforms traditional compression schemes. Our results indicate that our scheme can reduce microstructure data size by more than two orders of magnitude while achieving better accuracies for the computed stress fields and order parameters.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 368-379 |
| Number of pages | 12 |
| Journal | Computational Materials Science |
| Volume | 42 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 1 2008 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Chemistry(all)
- Materials Science(all)
- Mechanics of Materials
- Physics and Astronomy(all)
- Computational Mathematics
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Readily regenerable reduced microstructure representations. / Teranishi, Keita; Raghavan, Padma; Zhang, Jingxian; Wang, Tao; Chen, Long Qing; Liu, Zi Kui.
In: Computational Materials Science, Vol. 42, No. 2, 01.04.2008, p. 368-379.Research output: Contribution to journal › Article
TY - JOUR
T1 - Readily regenerable reduced microstructure representations
AU - Teranishi, Keita
AU - Raghavan, Padma
AU - Zhang, Jingxian
AU - Wang, Tao
AU - Chen, Long Qing
AU - Liu, Zi Kui
PY - 2008/4/1
Y1 - 2008/4/1
N2 - Many of the physical properties of materials are critically dependent on their microstructure. In recent years, there has been increasing interest in using computer simulations based on phase-field models for the spatial and temporal evolution of microstructures. Although such simulations are computationally expensive, the generated set of microstructures can be stored in a repository and used for further analysis in materials design. However, such an approach requires a substantial amount of storage, for example, approximately 1 Terabyte for a single binary alloy. In this paper, we develop fast data compression and regeneration schemes for two-dimensional microstructures that can reduce storage requirements without compromising the accuracy of computed values, such as stress fields used in analysis. Our main contribution is the development and evaluation of a sparse skeletal representation scheme which outperforms traditional compression schemes. Our results indicate that our scheme can reduce microstructure data size by more than two orders of magnitude while achieving better accuracies for the computed stress fields and order parameters.
AB - Many of the physical properties of materials are critically dependent on their microstructure. In recent years, there has been increasing interest in using computer simulations based on phase-field models for the spatial and temporal evolution of microstructures. Although such simulations are computationally expensive, the generated set of microstructures can be stored in a repository and used for further analysis in materials design. However, such an approach requires a substantial amount of storage, for example, approximately 1 Terabyte for a single binary alloy. In this paper, we develop fast data compression and regeneration schemes for two-dimensional microstructures that can reduce storage requirements without compromising the accuracy of computed values, such as stress fields used in analysis. Our main contribution is the development and evaluation of a sparse skeletal representation scheme which outperforms traditional compression schemes. Our results indicate that our scheme can reduce microstructure data size by more than two orders of magnitude while achieving better accuracies for the computed stress fields and order parameters.
UR - http://www.scopus.com/inward/record.url?scp=40249092807&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=40249092807&partnerID=8YFLogxK
U2 - 10.1016/j.commatsci.2007.07.015
DO - 10.1016/j.commatsci.2007.07.015
M3 - Article
AN - SCOPUS:40249092807
VL - 42
SP - 368
EP - 379
JO - Computational Materials Science
JF - Computational Materials Science
SN - 0927-0256
IS - 2
ER -