Reduced-order models for microstructure-sensitive effective thermal conductivity of woven ceramic matrix composites with residual porosity

Abstract

This paper presents a data-driven framework for the development of reduced-order models to predict microstructure-sensitive effective thermal conductivity of woven ceramic matrix composites (CMCs) with residual porosity. The main components of the proposed framework include (i) digital generation of representative volume elements (RVEs), (ii) estimation of the effective thermal conductivities of the RVEs using finite element (FE) models, (iii) low dimensional representation of the microstructure in the RVEs using 2-point spatial correlations and principal component analysis (PCA), and (iv) an active learning strategy based on Gaussian process regression (GPR) that minimizes the size of the training dataset through the selection of microstructures with the highest potential for information gain. The reduced-order models are demonstrated to provide high fidelity predictions on new RVEs.