Reduced-order models for microstructure-sensitive effective thermal conductivity of woven ceramic matrix composites with residual porosity. (15th October 2021)

• Record Type: Journal Article
• Authors: Generale, Adam P.; Kalidindi, Surya R.
• Is Part Of: Composite structures. Volume 274(2021)
• Publication Date: 2021-10-15
• Subjects: Ceramic matrix composites -- Chemical vapor infiltration -- Effective thermal conductivity -- Materials knowledge system -- Reduced-order models -- Sequential design of experiments -- Active learning; Composite construction -- Periodicals; Composites -- Périodiques; 624.18
• DOI: 10.1016/j.compstruct.2021.114399
• Languages: English
• ISSNs: 0263-8223

Abstract

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.