English

A fast dynamic smooth adaptive meshing scheme with applications to compressible flow

Computational Physics 2023-07-19 v2 Numerical Analysis Numerical Analysis Fluid Dynamics

Abstract

We develop a fast-running smooth adaptive meshing (SAM) algorithm for dynamic curvilinear mesh generation, which is based on a fast solution strategy of the time-dependent Monge-Amp\`{e}re (MA) equation, detψ(x,t)=Gψ(x,t)\det \nabla \psi(x,t) = \mathsf{G} \circ\psi (x,t). The novelty of our approach is a new so-called perturbation formulation of MA, which constructs the solution map ψ\psi via composition of a sequence of near-identity deformations of a reference mesh. Then, we formulate a new version of the deformation method that results in a simple, fast, and high-order accurate numerical scheme and a dynamic SAM algorithm that is of optimal complexity when applied to time-dependent mesh generation for solutions to hyperbolic systems such as the Euler equations of gas dynamics. We perform a series of challenging 2DD and 3DD mesh generation experiments for grids with large deformations, and demonstrate that SAM is able to produce smooth meshes comparable to state-of-the-art solvers, while running approximately 200 times faster. The SAM algorithm is then coupled to a simple Arbitrary Lagrangian Eulerian (ALE) scheme for 2DD gas dynamics. Specifically, we implement the CC-method and develop a new ALE interface tracking algorithm for contact discontinuities. We perform numerical experiments for both the Noh implosion problem as well as a classical Rayleigh-Taylor instability problem. Results confirm that low-resolution simulations using our SAM-ALE algorithm compare favorably with high-resolution uniform mesh runs.

Keywords

Cite

@article{arxiv.2205.09463,
  title  = {A fast dynamic smooth adaptive meshing scheme with applications to compressible flow},
  author = {Raaghav Ramani and Steve Shkoller},
  journal= {arXiv preprint arXiv:2205.09463},
  year   = {2023}
}

Comments

50 pages, 19 figures. Updated with new version of algorithm

R2 v1 2026-06-24T11:22:07.926Z