scholarly journals Multiscale modeling of thermal conductivity of polycrystalline graphene sheets

Nanoscale ◽  
2014 ◽  
Vol 6 (6) ◽  
pp. 3344-3352 ◽  
Author(s):  
Bohayra Mortazavi ◽  
Markus Pötschke ◽  
Gianaurelio Cuniberti

We developed a multiscale approach to explore the effective thermal conductivity of polycrystalline graphene sheets.

2021 ◽  
Author(s):  
XIN LIU ◽  
BO PENG ◽  
WENBIN YU

A data-driven multiscale modeling approach is developed to predict the effective thermal conductivity of two-dimensional (2D) woven composites. First, a two-step homogenization approach based on mechanics of structure genome (MSG) is developed to predict effective thermal conductivity. The accuracy and efficiency of the MSG model are compared with the representative volume element (RVE) model based on three-dimensional (3D) finite element analysis (FEA). Then, the simulation data is generated by the MSG model to train neural network models to predict the effective thermal conductivity of three 2D woven composites. The neural network models have mixed input features: continuous input (e.g., fiber volume fraction and yarn geometries) and discrete input (e.g., weave patterns). Moreover, the neural network models are trained with the normalized features to enable reusability. The results show that the developed data-driven models provide an ultra-efficient yet accurate approach for the thermal design and analysis of 2D woven composites.


RSC Advances ◽  
2017 ◽  
Vol 7 (18) ◽  
pp. 11135-11141 ◽  
Author(s):  
Bohayra Mortazavi ◽  
Timon Rabczuk

We developed a combined atomistic-continuum multiscale modeling to explore the effective thermal conductivity of all-MoS2 single-layer heterostructures.


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