Fragmentation analysis of a bar with the Lip-field approach

TL;DR

This paper extends the Lip-field regularization approach to 1D dynamic fragmentation, demonstrating its effectiveness through comparison with analytical, numerical, and experimental results in a challenging brittle fragmentation problem.

Contribution

It introduces a dynamic Lip-field formulation for fragmentation, including a new damage model and a staggered convex optimization algorithm, validated against various references.

Findings

Lip-field approach effectively models 1D dynamic fragmentation.

The method shows good agreement with analytical and experimental data.

Staggered convex optimization improves computational efficiency.

Abstract

The Lip-field approach is a new way to regularize softening material models. It has already been tested in 1D quasistatic and 2D quasistatic: this paper extends it to 1D dynamics, on the challenging problem of dynamic fragmentation. The Lip-field approach formulates the mechanical problem to be solved as an optimization problem, where the incremental potential to be minimized is the non-regularized one. Spurious localization is prevented by imposing a Lipschitz constraint on the damage field. The displacement and damage field at each time step are obtained by a staggered algorithm, that is the displacement field is computed for a fixed damage field, then the damage field is computed for a fixed displacement field. Indeed, these two problems are convex, which is not the case of the global problem where the displacement and damage fields are sought at the same time. The incremental potential is obtained by equivalence with a cohesive zone model, which makes material parameters calibration simple. A non-regularized local damage equivalent to a cohesive zone model is also proposed. It is used as a reference for the Lip-field approach, without the need to implement displacement jumps. These approaches are applied to the brittle fragmentation of a 1D bar with randomly perturbed material properties to accelerate spatial convergence. Both explicit and implicit dynamic implementations are compared. Favorable comparison to several analytical, numerical and experimental references serves to validate the modeling approach.