Year: 2024
Type: preprint
Abstract: This paper develops a general data-driven approach to stochastic elastoplastic modelling that leverages atomistic simulation data directly rather than by fitting parameters. The approach is developed in the context of metallic glasses, which present inherent complexities due to their disordered structure. By harvesting statistics from simulated metallic glass shear response histories, the material state is mapped onto a two-dimensional state space consisting of the shear stress and the inelastic contribution to the potential energy. The resulting elastoplastic model is intrinsically stochastic and represented as a non-deterministic dynamical map. The state space statistics provide insights into the deformation physics of metallic glasses, revealing that two state variables are sufficient to describe the main features of the elastoplastic response. In this two-dimensional state space, the gradually quenched metallic glass rejuvenates during the initial quasi-elastic shearing, ultimately reaching a steady state that fluctuates about a fixed point in the state space as rejuvenation and aging balance.
Source: arXiv (Cornell University)
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: MRI: Acquisition of an Advanced Computing Instrument to Integrate Data-Driven Research and Data intensive computing at Johns Hopkins University, Variationelle Modellreduktion für die Vorhersage von plastischen Ereignissen in ungeordneten Materialien, Collaborative Research: DMREF: Simulation-Informed Models for Amorphous Metal Additive Manufacturing, Collaborative Research: DMREF: Simulation-Informed Models for Amorphous Metal Additive Manufacturing, Collaborative Research: DMREF: Simulation-Informed Models for Amorphous Metal Additive Manufacturing
OpenAlex ID: W4403821398