Data-Driven Calibration of Multi-Fidelity Multiscale Fracture Models via Latent Map Gaussian Process

TL;DR

This paper introduces a data-driven framework combining reduced-order modeling and latent map Gaussian processes to efficiently calibrate multiscale fracture simulations of porous metallic alloys, accounting for manufacturing variabilities.

Contribution

It develops a novel calibration scheme using LMGPs to accurately link microstructure variations with damage response in a computationally efficient manner.

Findings

Microstructural porosity significantly impacts macro component performance.

The framework accurately predicts damage behavior in multiscale metallic components.

Calibration with LMGPs improves surrogate model fidelity to direct simulations.

Abstract

Fracture modeling of metallic alloys with microscopic pores relies on multiscale damage simulations which typically ignore the manufacturing-induced spatial variabilities in porosity. This simplification is made because of the prohibitive computational expenses of explicitly modeling spatially varying microstructures in a macroscopic part. To address this challenge and open the doors for fracture-aware design of multiscale materials, we propose a data-driven framework that integrates a mechanistic reduced-order model (ROM) with a calibration scheme based on random processes. 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 calibrate the ROM by constructing a multi-fidelity random process based on latent map Gaussian processes (LMGPs). In particular, we use LMGPs to calibrate the damage parameters of an ROM as a function of microstructure and clustering (i.e., fidelity) 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. Our results indicate that microstructural porosity can significantly affect the performance of macro components and hence must be considered in the design process.