Predictions of Thermal Conductivity and Degradation of Irradiated SiC/SiC Composites by Materials-Genome-Based Multiscale Modeling
Abstract
A new materials-genome-based multiscale modeling method is proposed to predict the thermal conductivity of SiC/SiC composites subjected to neutron irradiation. The modeling method bridges different scales from the atomic scale to the macro scale of a 2D SiC/SiC composite. The irradiation-induced point defects in the interphase of SiC/SiC composites are first studied using molecular dynamics simulations to compute the degradation of thermal conductivity as a function of irradiation dose and temperature. The thermal conductivities of SiC fibers, matrices and interphase are then used as input for the new materials genome model to compute the thermal conductivities of 2D SiC/SiC composites subject to neutron irradiation. The predicted thermal conductivities for both unirradiated and irradiated SiC/SiC composites are found to decrease with an increase of temperature while the irradiation effect on interphase needs to be considered for irradiated SiC/SiC composites.
Recommended Citation
X. Dong and Y. C. Shin, "Predictions of Thermal Conductivity and Degradation of Irradiated SiC/SiC Composites by Materials-Genome-Based Multiscale Modeling," Journal of Nuclear Materials, vol. 512, pp. 268 - 275, Elsevier, Dec 2018.
The definitive version is available at https://doi.org/10.1016/j.jnucmat.2018.10.021
Department(s)
Mechanical and Aerospace Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Ceramic matrix composites; Genes; Molecular dynamics; Neutron irradiation; Point defects; Radiation; Silicon carbide; Silicon compounds; Atomic scale; Irradiation dose; Irradiation effects; Macro scale; Model method; Molecular dynamics simulations; Multi-scale Modeling; SiC/SiC composites; Thermal conductivity; Materials genome; Multiscale model
International Standard Serial Number (ISSN)
0022-3115
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Elsevier, All rights reserved.
Publication Date
01 Dec 2018