On the superlinear convergence in computational elasto-plasticity
We consider the convergence properties of return algorithms for a large class of rate-independent plasticity models. Based on recent results for semismooth functions, we can analyze these algorithms in the context of semismooth Newton methods guaranteeing local superlinear convergence. This recovers results for classical models but also extends to general hardening laws, multi-yield plasticity, and to several non-associated models. The superlinear convergence is also numerically shown for a large-scale parallel simulation of DruckerPrager elasto-plasticity and an example for the modified Cam-clay model.
semismooth newton methods computational plasticity return mapping algorithms non-associated plasticity