Method of lines transpose: Energy gradient flows using direct operator inversion for phase field models

TL;DR

This paper introduces an efficient, stable, and high-order numerical method for solving phase field models like the Cahn-Hilliard equations in 1D and 2D, using a novel operator inversion technique that is flexible beyond periodic boundaries.

Contribution

It develops an $ ext{O}(N)$ implicit solver based on the Method of Lines Transpose, incorporating a novel factorization for high-order derivatives and extending to various time-stepping schemes for improved accuracy.

Findings

The method is gradient stable in the $H^{-1}$ norm.

It achieves high-order temporal accuracy with various implicit schemes.

Numerical results confirm efficiency and stability in 1D and 2D simulations.

Abstract

In this work, we develop an $\mathcal{O}(N)$ implicit real space method in 1D and 2D for the Cahn Hilliard (CH) and vector Cahn Hilliard (VCH) equations, based on the Method Of Lines Transpose (MOL$^\text{T}$) formulation. This formulation results in a semi-discrete time stepping algorithm, which we prove is gradient stable in the $H^{-1}$ norm. The spatial discretization follows from dimensional splitting, and an $\mathcal{O}(N)$ matrix-free solver, which applies fast convolution to the modified Helmholtz equation. We propose a novel factorization technique, in which fourth order spatial derivatives are incorporated into the solver. The splitting error is included in the nonlinear fixed point iteration, resulting in a high order, logically Cartesian (line-by-line) update. Our method is fast, but not restricted to periodic boundaries like the fast Fourier transform (FFT). The basic solver is implemented using the Backward Euler formulation, and we extend this to both backward difference (BDF) stencils, implicit Runge Kutta (SDIRK) and spectral deferred correction (SDC) frameworks to achieve high orders of temporal accuracy. We demonstrate with numerical results that the CH, and VCH equations maintain gradient stability in one and two spatial dimensions. We also explore time-adaptivity, so that meta-stable states and ripening events can be simulated both quickly and efficiently.