3D Reconstruction of Carbon Nanotube Composite Microstructure Using Correlation Functions
Abstract
Reconstruction of simulated microstructure 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 robust microstructure with large number of representative volume elements which may acts as a stable input for deterministic method to simulate performance and effective properties. It is applied in carbon nanotube composite to demonstrate the capability of this method to generate robust microstructure while incorporating more statistical information on geometry, shape, anisotropy and spatial arrangement. Not only one point based statistical information, such as size, volume fraction, is taken into consideration, but correlation function is incorporated to cover information from geometry, shape and spatial correlation. Monte Carlo method was applied in reconstruction. Instead of using discrete image matrix, the information of geometric distribution of the nanotube composite is stored with the information of location of nanotubes. In this way, robust micrographs with large number of representative volume elements were generated for the future evaluation using finite element methods.
- Authors:
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1001112
- Report Number(s):
- PNNL-SA-75381
Journal ID: ISSN 1546-1955; TRN: US201101%%846
- DOE Contract Number:
- AC05-76RL01830
- Resource Type:
- Journal Article
- Journal Name:
- Journal of Computational and Theoretical Nanoscience, 7(8):1462-1468
- Additional Journal Information:
- Journal Volume: 7; Journal Issue: 8; Journal ID: ISSN 1546-1955
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 77 NANOSCIENCE AND NANOTECHNOLOGY; ANISOTROPY; CARBON; CORRELATION FUNCTIONS; DESIGN; DISTRIBUTION; EVALUATION; FINITE ELEMENT METHOD; GEOMETRY; MICROSTRUCTURE; MONTE CARLO METHOD; NANOTUBES; PERFORMANCE; SHAPE; statistical correlation, two point function, microstructure reconstruction, robust microstructure, carbon nanotube
Citation Formats
Li, Dongsheng, Baniassadi, Majid, Garmestani, Hamid, Ahzi, Said, Reda Taha, M M, and Ruch, David. 3D Reconstruction of Carbon Nanotube Composite Microstructure Using Correlation Functions. United States: N. p., 2010.
Web. doi:10.1166/jctn.2010.1504.
Li, Dongsheng, Baniassadi, Majid, Garmestani, Hamid, Ahzi, Said, Reda Taha, M M, & Ruch, David. 3D Reconstruction of Carbon Nanotube Composite Microstructure Using Correlation Functions. United States. https://doi.org/10.1166/jctn.2010.1504
Li, Dongsheng, Baniassadi, Majid, Garmestani, Hamid, Ahzi, Said, Reda Taha, M M, and Ruch, David. 2010.
"3D Reconstruction of Carbon Nanotube Composite Microstructure Using Correlation Functions". United States. https://doi.org/10.1166/jctn.2010.1504.
@article{osti_1001112,
title = {3D Reconstruction of Carbon Nanotube Composite Microstructure Using Correlation Functions},
author = {Li, Dongsheng and Baniassadi, Majid and Garmestani, Hamid and Ahzi, Said and Reda Taha, M M and Ruch, David},
abstractNote = {Reconstruction of simulated microstructure 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 robust microstructure with large number of representative volume elements which may acts as a stable input for deterministic method to simulate performance and effective properties. It is applied in carbon nanotube composite to demonstrate the capability of this method to generate robust microstructure while incorporating more statistical information on geometry, shape, anisotropy and spatial arrangement. Not only one point based statistical information, such as size, volume fraction, is taken into consideration, but correlation function is incorporated to cover information from geometry, shape and spatial correlation. Monte Carlo method was applied in reconstruction. Instead of using discrete image matrix, the information of geometric distribution of the nanotube composite is stored with the information of location of nanotubes. In this way, robust micrographs with large number of representative volume elements were generated for the future evaluation using finite element methods.},
doi = {10.1166/jctn.2010.1504},
url = {https://www.osti.gov/biblio/1001112},
journal = {Journal of Computational and Theoretical Nanoscience, 7(8):1462-1468},
issn = {1546-1955},
number = 8,
volume = 7,
place = {United States},
year = {Sun Aug 01 00:00:00 EDT 2010},
month = {Sun Aug 01 00:00:00 EDT 2010}
}