Hi Vishal, Yes this behaviour is expected and has been reported by other DG and FR research groups . As you coarsen the mesh while keeping the Reynolds number constant you are relying more on numerical dissipation to dissipate energy, rather than physical dissipation. If the mesh is extremely coarse, as it looks like may be the case in your simulations, it can allow aliasing instabilities to grow and ultimately give negative density or pressure. The lower-order schemes tend to be less prone to this in my experience, since they are more dissipative.
As a starting point, you may want to read the Taylor-Green section in paper [1] where we explored different FR schemes in exactly this configuration. This has also been studied by other groups in the DG and FR community. For example [2] shows plots very similar to yours. You should be able to find other Taylor-Green papers that discuss it in more detail. [1] B.C. Vermeire, P.E. Vincent, On the properties of energy stable flux reconstruction schemes for implicit large eddy simulation, Journal of Computational Physics, Volume 327, 2016 [2] J.R. Bull, A. Jameson, Simulation of the Taylor–Green Vortex Using High-Order Flux Reconstruction Schemes, AIAA Journal, Vol. 53, No. 9, 2015. On Monday, 30 April 2018 06:56:36 UTC-4, Vishal Saini wrote: > > Hi all, > > I’m a PhD student comparing PyFR to standard CFD tools in a hope to > transferring high-order technology to industrial applications. > > As a start, I’ve recently tried to run 3D Taylor-Green vortex case > (Re=1600, M=0.1 Ref:[1]) using compressible PyFR solver (PyFR 1.7.5). I’m > running the case using coarser grids (uniform hexahedra, 16^3 and 32^3 > using polynomial order 4) with respect to what would be required by a DNS. > I observe a matching kinetic-energy (E) vs. time curve to the reference > DNS. However, the time derivative of E (-dissipation rate) is wiggly, the > magnitude of wiggles being significant for 16^3 P4 case. Figures are > attached for clarity. Has anyone seen similar things before? > > Things I’ve tried to ensure the wiggles are not originating from how often > I save the data but the simulation itself: > > 1. Saving of two very close instances in time (5 time steps apart) > and taking time derivative based on these. > > 2. Reducing the time step by 1/8th of the one shown in the config > file attached. > > The above treatment did not change the result. > > I also tried over integration in element volume and interfaces using 2x > integration points, but in vain. On the other hand the exponential filter > helped a bit, although a lot of tweaking in strength and cut-off number was > required. Any comments on their usage? > Thank you for your time. > > Attachments: config file (also showing over integration approach), mesh > file, E vs. time and -dE/dt vs. time curves obtained. The attachments > correspond to 16^3 P4 simulation. > [1] B. C. Vermeire, F. D. Witherden, and P. E. Vincent, “On the utility of > GPU accelerated high-order methods for unsteady flow simulations: A > comparison with industry-standard tools,” J. Comput. Phys., vol. 334, 2017. > > Cheers, > Vishal > -- You received this message because you are subscribed to the Google Groups "PyFR Mailing List" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send an email to [email protected]. Visit this group at https://groups.google.com/group/pyfrmailinglist. For more options, visit https://groups.google.com/d/optout.
