Numerical solution of the three-dimensional incompressible Euler equations using the Characteristic Mapping Method

ORAL

Abstract

We present an efficient semi-Lagrangian Characteristic Mapping (CM) method for solving the three-dimensional incompressible Euler equations. This method evolves advected quantities by discretizing the flow map associated with the velocity field. Using the properties of the Lie group of volume preserving diffeomorphisms SDiff, long-time deformations are computed from a composition of short-time submaps which can be accurately evolved on coarse grids. This method is a fundamental extension to the CM method for two-dimensional incompressible Euler equations. We take a geometric approach in the 3D case where the vorticity is not a scalar advected quantity, but can be computed as a differential 2-form through the pullback of the initial condition by the characteristic map. This formulation is based on the Kelvin circulation theorem and gives point-wise a Lagrangian description of the vorticity field. We demonstrate through numerical experiments the validity of the method and show that energy is not dissipated through artificial viscosity and small scales of the solution are preserved. We provide error estimates and numerical convergence tests showing that the method is globally third-order accurate.

Publication: X.-Y. Yin, K. Schneider and J.-C. Nave.
A Characteristic Mapping Method for the three-dimensional incompressible Euler equations.
Preprint, 07/2021. arXiv:2107.03504

X.-Y. Yin, O. Mercier, B. Yadav, K. Schneider and J.-C. Nave.
A Characteristic Mapping Method for the two-dimensional incompressible Euler equations.
J. Comput. Phys., 424, 109781, 2021.

Presenters

  • Kai Schneider

    Institut de Mathématiques de Marseille (I2M), Aix-Marseille Université, CNRS, Marseille, France, Aix-Marseille University

Authors

  • Xi Yuan Yin

    Department of Mathematics and Statistics, McGill University, Montréal, Québec, Canada

  • Jean-Christophe Nave

    Department of Mathematics and Statistics, McGill University, Montréal, Québec, Canada

  • Kai Schneider

    Institut de Mathématiques de Marseille (I2M), Aix-Marseille Université, CNRS, Marseille, France, Aix-Marseille University