Multiscale modeling of the effective thermal conductivity of 2D woven composites by mechanics of structure genome and neural networks

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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A data-driven multiscale modeling approach is developed to predict the effective thermal conductivity of 2D woven composites using the mechanics of structure genome (MSG). Neural network models trained with simulation data generated by the MSG model enable the prediction of thermal conductivity of different woven composites. The developed models provide an efficient and accurate method for thermal design and analysis of 2D woven composites.