Using porous random fields to predict the elastic modulus of unoxidized and oxidized superfine graphite

Arregui-Mena, José David; Griffiths, D. V.; Worth, Robert N.; Torrence, Christa E.; Selby, Aaron; Contescu, Cristian; Gallego, Nidia; Edmondson, Philip D.; Mummery, Paul M.; Margetts, Lee

doi:10.1016/j.matdes.2022.110840

DOE OSTI · 1874664

Using porous random fields to predict the elastic modulus of unoxidized and oxidized superfine graphite

Arregui-Mena, José David·Griffiths, D. V.·Worth, Robert N.·Torrence, Christa E.·Selby, Aaron·Contescu, Cristian·Gallego, Nidia·Edmondson, Philip D.·Mummery, Paul M.·Margetts, Lee

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Abstract

Nuclear graphite is a candidate material for Generation IV nuclear power plants. Porous materials such as graphite can contain complex networks of pores that influence the material's mechanical and irradiation response. A methodology known as the random finite element method (RFEM) was adapted to create synthetic microstructures and predict the influence of porosity on the elastic properties of graphite during oxidation. RFEM combines random field theory and the finite element method in a Monte Carlo framework to estimate the mechanical response of a given grade of graphite. In this research, the random fields were verified through experimental characterization to predict the elastic response of three nuclear graphite grades, ETU-10, IG-110, and 2114. Finite element models (FEM) were generated using segmentations of x-ray computed tomography (XCT) data known as image-based models (IBMs) to validate and compare with the RFEM results and better understand the effects of uniform oxidation in these graphite grades. The RFEM predictions appear to correlate well with the experimental values of the measured Young’s modulus of the three graphite grades and display the same trends as IBMs.

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Arregui-Mena, José David, Griffiths, D. V., Worth, Robert N., Torrence, Christa E., Selby, Aaron, Contescu, Cristian, Gallego, Nidia, Edmondson, Philip D., Mummery, Paul M., Margetts, Lee. 2022-06-17. Using porous random fields to predict the elastic modulus of unoxidized and oxidized superfine graphite. https://doi.org/10.1016/j.matdes.2022.110840

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