Multi-Fidelity Reduced-Order Models for Multiscale Damage Analyses With Automatic Calibration

Abstract

Abstract Predicting the fracture behavior of macroscale components containing microscopic porosity relies on multiscale damage models which typically ignore the manufacturing-induced spatial variabilities in porosity. This simplification is made due to the prohibitive computational costs associated with explicitly modeling spatially varying microstructures in a macroscopic component. To address this challenge, we propose a data-driven framework that integrates a mechanistic reduced-order model (ROM) with a calibration scheme based on latent map Gaussian processes (LMGPs). Our ROM drastically accelerates direct numerical simulations (DNS) by using a stabilized damage algorithm and systematically reducing the degrees of freedom via clustering. Since clustering affects local strain fields and hence the fracture response, we construct a multi-fidelity LMGP to inversely estimate the damage parameters of an ROM as a function of microstructure and clustering level such that the ROM faithfully surrogates DNS. We demonstrate the application of our framework in predicting the damage behavior of a multiscale metallic component with spatially varying porosity.