An iteration solver for the Poisson–Nernst–Planck system and its convergence analysis

Chun Liu, Cheng Wang, Steven M. Wise, Xingye Yue, Shenggao Zhou

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


In this paper, we provide a theoretical analysis for an iteration solver to implement a finite difference numerical scheme for the Poisson-Nernst–Planck (PNP) system, based on the Energetic Variational Approach (EnVarA), in which a non-constant mobility H−1 gradient flow is formulated. In particular, the nonlinear and singular nature of the logarithmic energy potentials has always been the essential difficulty. In the numerical design, the mobility function is explicitly updated, for the sake of unique solvability analysis. The logarithmic and the electric potential diffusion terms, which come from the gradient of convex energy functional parts, are implicitly computed. The positivity-preserving property for all the concentrations, an unconditional energy stability, and the optimal rate error estimate have been established in a recent work. A modified Newton iteration for the nonlinear and logarithmic part, combined with a linear iteration for the electric potential part, is proposed to implement the given numerical scheme, in which a non-constant linear elliptic equation needs to be solved at each iteration stage. A theoretical analysis is presented in this article, and a linear convergence is proved for such an iteration, with an asymptotic error constant in the same order of the time step size. A numerical test is also presented in this article, which demonstrates the linear convergence rate of the proposed iteration solver.

Original languageEnglish (US)
Article number114017
JournalJournal of Computational and Applied Mathematics
StatePublished - May 1 2022

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics


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