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

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.