A robust monolithic solver for phase-field fracture integrated with fracture energy based arc-length method and under-relaxation

TL;DR

This paper introduces a robust monolithic solver for phase-field fracture that integrates an arc-length method and adaptive under-relaxation, significantly improving convergence and stability over traditional methods.

Contribution

A novel monolithic solver for phase-field fracture is developed, combining an arc-length method, under-relaxation, and adaptive mesh refinement for enhanced robustness and efficiency.

Findings

Solver successfully overcomes critical points during loading.

Adaptive mesh refinement improves computational efficiency.

Codes and datasets will be publicly available on GitHub.

Abstract

The phase-field fracture free-energy functional is non-convex with respect to the displacement and the phase field. This results in a poor performance of the conventional monolithic solvers like the Newton-Raphson method. In order to circumvent this issue, researchers opt for the alternate minimization (staggered) solvers. Staggered solvers are robust for the phase-field based fracture simulations as the displacement and the phase-field sub-problems are convex in nature. Nevertheless, the staggered solver requires very large number of iterations (of the order of thousands) to converge. In this work, a robust monolithic solver is presented for the phase-field fracture problem. The solver adopts a fracture energy-based arc-length method and an adaptive under-relaxation scheme. The arc-length method enables the simulation to overcome critical points (snap-back, snap-through instabilities) during the loading of a specimen. The use of an under-relaxation scheme stabilizes the solver by preventing the divergence due to an ill-behaving stiffness matrix. The efficiency of the proposed solver is further amplified with an adaptive mesh refinement scheme based on PHT-splines within the framework of isogeometric analysis. The numerical examples presented in the manuscript demonstrates the efficacy of the solver. All the codes and data-sets accompanying this work will be made available on GitHub (https://github.com/rbharali/IGAFrac).