Discrete Empirical Interpolation Method for nonlinear softening problems involving damage and plasticity
Steffen Kastian, Jannick Kehls, Tim Brepols, Stefanie Reese

TL;DR
This paper introduces a reduced order modeling approach using POD-based DEIM combined with an arc-length method to efficiently simulate complex damage and plasticity behaviors in nonlinear softening problems, significantly reducing computational effort.
Contribution
It presents a novel integration of DEIM and arc-length methods for reduced order modeling of damage-plasticity models with softening, validated through complex numerical examples.
Findings
High accuracy reduced models achieved
Significant speedup in simulations
Effective handling of damage and plasticity behaviors
Abstract
Accurate simulations are essential for engineering applications, and intricate continuum mechanical material models are constructed to achieve this goal. However, the increasing complexity of the material models and geometrical properties lead to a significant increase in computational effort. Model order reduction aims to implement efficient methods for accelerating the simulation process while preserving a high degree of accuracy. Numerous studies have already demonstrated the benefits of this method for linear elastic material modeling. However, in the present work, we investigate a two-surface gradient-extended damage-plasticity model. We conducted complex simulations with this model, demonstrating both damage behavior and softening. The POD-based discrete empirical interpolation method (DEIM) is introduced and implemented. To accomplish simulations with DEIM and softening…
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Taxonomy
TopicsModel Reduction and Neural Networks · Numerical methods in engineering · Metal and Thin Film Mechanics
