Readily regenerable reduced microstructure representations

Keita Teranishi, Padma Raghavan, Jingxian Zhang, Tao Wang, Long Qing Chen, Zi Kui Liu

Research output: Contribution to journalArticle

2 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)368-379
Number of pages12
JournalComputational Materials Science
Volume42
Issue number2
DOIs
StatePublished - Apr 1 2008

Fingerprint

Microstructure
microstructure
Stress Field
stress distribution
Material Design
Binary Alloys
Phase Field Model
data compression
Binary alloys
Data compression
Data Compression
Regeneration
binary alloys
regeneration
Physical property
Order Parameter
Repository
Computer Simulation
Compression
Physical properties

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Chemistry(all)
  • Materials Science(all)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Computational Mathematics

Cite this

Teranishi, Keita ; Raghavan, Padma ; Zhang, Jingxian ; Wang, Tao ; Chen, Long Qing ; Liu, Zi Kui. / Readily regenerable reduced microstructure representations. In: Computational Materials Science. 2008 ; Vol. 42, No. 2. pp. 368-379.
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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 journalArticle

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