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Artificial Compressibility Approaches in Flux Reconstruction for Incompressible Viscous Flow Simulations

Fluid Dynamics 2022-09-22 v1

Abstract

Several competing artificial compressibility methods for the incompressible flow equations are examined using the high-order flux reconstruction method. The established artificial compressibility method (ACM) of \citet{Chorin1967} is compared to the alternative entropically damped (EDAC) method of \citet{Clausen2013}, as well as an ACM formulation with hyperbolised diffusion. While the former requires the solution to be converged to a divergence free state at each physical time step through pseudo iterations, the latter can be applied explicitly. We examine the sensitivity of both methods to the parameterisation for a series of test cases over a range of Reynolds numbers. As the compressibility is reduced, EDAC is found to give linear improvements in divergence whereas ACM yields diminishing returns. For the Taylor--Green vortex, EDAC is found to perform well; however on the more challenging circular cylinder at Re=3900Re=3900, EDAC gives rise to early transition of the free shear-layer and over-production of the turbulence kinetic energy. This is attributed to the spatial pressure fluctuations of the method. Similar behaviour is observed for an aerofoil at Re=60,000Re=60,000 with an attached transitional boundary layer. It is concluded that hyperbolic diffusion of ACM can be beneficial but at the cost of case setup time, and EDAC can be an efficient method for incompressible flow. However, care must be taken as pressure fluctuations can have a significant impact on physics and the remedy causes the governing equation to become overly stiff.

Keywords

Cite

@article{arxiv.2111.07915,
  title  = {Artificial Compressibility Approaches in Flux Reconstruction for Incompressible Viscous Flow Simulations},
  author = {Will Trojak and Nagabhushana Rao Vadlamani and James Tyacke and Freddie Witherden and Antony Jameson},
  journal= {arXiv preprint arXiv:2111.07915},
  year   = {2022}
}