Long-time stability of implicit-explicit Runge-Kutta methods for two-dimensional incompressible flows
Abstract
High-order adaptive time-stepping algorithms are of significant practical value and theoretical interest for accelerating long-time fluid-flow simulations and resolving complex dynamical behaviors. While several high-order implicit-explicit schemes have been proposed in the literature, their long-time stability properties remain largely unexplored. We develop a family of long-time stable implicit-explicit Runge-Kutta (IERK) methods, up to fourth-order temporal accuracy, for the two-dimensional incompressible Navier-Stokes equations in vorticity-stream function formulation. By combining a convolution-type H\"{o}lder inequality with a damping-type multistage Gr\"{o}nwall inequality, we establish a unified analytical framework that proves long-time stability in both the and norms. A key component of the analysis is a mathematical-induction argument that ensures stage-wise boundedness of the vorticity in the norm for some . To the best of our knowledge, this is the first work to establish large-time stability results for high-order IERK algorithms for the two-dimensional incompressible Navier-Stokes equations. Our IERK schemes employ stiffly accurate diagonally implicit Runge-Kutta approximations for the linear diffusive term together with explicit Runge-Kutta approximations for the nonlinear advection term. By exploiting the specific structure of the Navier-Stokes model, we derive a reduced set of order conditions-requiring only 5 and 11 conditions for the third- and fourth-order methods, respectively, in contrast to the classical 6 and 18-allowing the construction of a parameterized family of efficient schemes. These IERK methods are particularly well suited for adaptive time-stepping, as they permit significantly enlarged step sizes in actual computations.
Cite
@article{arxiv.2605.05645,
title = {Long-time stability of implicit-explicit Runge-Kutta methods for two-dimensional incompressible flows},
author = {Hong-lin Liao and Xiaoming Wang and Xuping Wang and Cao Wen},
journal= {arXiv preprint arXiv:2605.05645},
year = {2026}
}
Comments
24 pages, 35 figures, 3 tables