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Details

Autor(en) / Beteiligte
Titel
Using porous random fields to predict the elastic modulus of unoxidized and oxidized superfine graphite
Ist Teil von
  • Materials & design, 2022-08, Vol.220 (1), p.110840, Article 110840
Ort / Verlag
United States: Elsevier Ltd
Erscheinungsjahr
2022
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
  • [Display omitted] •Random finite element method simulations predicted the elastic properties of superfine graphite grades IG-110, 2114 and ETU-10.•Random finite element method elastic simulations are in agreement with experimental data for oxidized graphite up to 30% weight loss.•The minimum volume required to simulate the elastic properties of superfine graphite grades was estimated to be about (50 × 50 × 50) μm3.•Random finite element simulations can be incorporated into graphite component lifetime predictions for the design of Generation IV reactors. 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.
Sprache
Englisch
Identifikatoren
ISSN: 0264-1275
eISSN: 1873-4197
DOI: 10.1016/j.matdes.2022.110840
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_072c630b44f8460994318bb4fd00bdf4

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