The Efficient Variable Time-stepping DLN Algorithms for the Allen-Cahn Model

TL;DR

This paper introduces efficient, unconditionally stable variable time-stepping DLN algorithms for the Allen-Cahn equation, combining finite element spatial discretization with novel temporal schemes and adaptive algorithms, validated through numerical tests.

Contribution

The paper develops and analyzes new variable time-stepping DLN schemes with proven stability and error estimates for the Allen-Cahn model, incorporating efficient temporal algorithms and adaptive strategies.

Findings

Unconditional long-term stability under arbitrary time steps.

Rigorous error analysis for the partially implicit modified algorithm.

Numerical tests confirm the effectiveness of the adaptive DLN methods.

Abstract

We consider a family of variable time-stepping Dahlquist-Liniger-Nevanlinna (DLN) schemes, which is unconditional non-linear stable and second order accurate, for the Allen-Cahn equation. The finite element methods are used for the spatial discretization. For the non-linear term, we combine the DLN scheme with two efficient temporal algorithms: partially implicit modified algorithm and scalar auxiliary variable algorithm. For both approaches, we prove the unconditional, long-term stability of the model energy under any arbitrary time step sequence. Moreover, we provide rigorous error analysis for the partially implicit modified algorithm with variable time-stepping. Efficient time adaptive algorithms based on these schemes are also proposed. Several one- and two-dimensional numerical tests are presented to verify the properties of the proposed time adaptive DLN methods.