In this paper, we establish a novel linear relaxation method with regularized energy reformulation for phase field models, which we name the RRER method. We employ the molecular beam epitaxy (MBE) model and the phase-field crystal (PFC) model as test beds, along with several coupled phase field models, to illustrate the concept. Our proposed RRER strategy is applicable to a broad class of phase field models that can be derived through energy variation. The RRER method consists of two major steps. First, we introduce regularized auxiliary variables to reformulate the original phase field models into equivalent forms where the free energy is transformed under the auxiliary variables. Then, we discretize the reformulated PDE model under these variables on a staggered time mesh for the phase field models. We incorporate the energy reformulation idea in the first step and the linear relaxation concept in the second step to derive a general numerical algorithm for phase field models that is linear and second-order accurate. Our approach differs from the classical invariant energy quadratization (IEQ) and scalar auxiliary variable (SAV) approaches, as we don't need to take time derivatives for the auxiliary variables. The resulting schemes are linear, i.e., only linear algebraic systems need to be solved at each time step. Rigorous theoretical analysis demonstrates that these resulting schemes satisfy the modified discrete energy dissipation laws and preserve the discrete mass conservation for the PFC and MBE models. Furthermore, we present numerical results to demonstrate the effectiveness of our method in solving phase field models.

Publication:

Journal of Computational Physics Volume 515, 15 October 2024

https://doi.org/10.1016/j.jcp.2024.113225

Author:

Jiansong Zhang

Department of Applied Mathematics, China University of Petroleum, Qingdao 266580, PR China

Xinxin Guo

Department of Applied Mathematics, China University of Petroleum, Qingdao 266580, PR China

Maosheng Jiang

School of Mathematics and Statistics, Qingdao University, Qingdao 266071, PR China

Tao Zhou

Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, PR China

Jia Zhao

Department of Mathematics and Statistics, Binghamton University, Binghamton, NY, 13902, USA

Email: jiazhao@binghamton.edu