Session: DAC-22-01-Multi-fidelity Modeling Under Uncertainty

Paper Number: 90163

90163 - Multi-Fidelity Reduced-Order Models for Multiscale Damage Analyses With Automatic Calibration 

Predicting the fracture behaviors 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 clustering the degrees of freedom. Since clustering affects local strain fields and increases the damage tolerance of a microstructure, 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. Additionanlly, the learned latent space of the trained LMGP provides a diagnostic tool that helps in selecting the coarsening degree of the ROM. We demonstrate the application of our framework on predicting the damage behavior of a multiscale metallic component with spatially varying porosity. Our results indicate that microstructural porosity can significantly affect the performance of macro components and hence must be considered in the design process. 

Presenting Author: Shiguang Deng University of California, Irvine

Presenting Author Biography: Dr. Deng is a post-doctoral scholar at the University of California, Irvine.

Authors:

Shiguang Deng University of California, Irvine
Carlos Mora University of California, Irvine
Diran Apelian University of California, Irvine
Ramin Bostanabad University of California, Irvine