3D reconstruction of Carbon nanotube composite microstructure using correlation functions

D. S. Li, M. Baniassadi, H. Garmestani, S. Ahzi, M. M. Reda Taha, D. Ruch

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

Microstructure reconstruction from statistical microstructure descriptors attracts strong research interest due to its importance in materials design. A new methodology is presented in this paper to reconstruct microstructure with a large number of representative volume elements which may provide a stable input for a deterministic method to simulate performance and effective properties. Carbon nanotube composite are used to demonstrate the capability of this methodology to reconstruct robust microstructures while incorporating statistical correlation functions, which gives information of not only volume fractions, but also component size, geometry, shape and spatial distribution. The Monte Carlo technique was the basis for the reconstruction methodology in this work. Instead of using a discrete image matrix, the information of geometric distribution of the nano- tubes in composite is stored in a database of node locations of unit cylinder segments and the corresponding waviness. In this way, robust microstructures with a large number of representative volume elements were reconstructed for the future evaluation.

Original languageEnglish
Pages (from-to)1462-1468
Number of pages7
JournalJournal of Computational and Theoretical Nanoscience
Volume7
Issue number8
DOIs
Publication statusPublished - 1 Aug 2010

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Keywords

  • Carbon nanotube
  • Microstructure reconstruction
  • Robust microstructure
  • Statistical correlation
  • Two-point function

ASJC Scopus subject areas

  • Chemistry(all)
  • Materials Science(all)
  • Condensed Matter Physics
  • Computational Mathematics
  • Electrical and Electronic Engineering

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