Compressible Flows. A Jacobi Polynomials. Evaluation of the Zeros of Jacobi Polynomials. Continuous Expansion Basis. E Characteristic Flux Decomposition. Launder , N.
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Spencer J. Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
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Already on GitHub? Sign in to your account. My research interests are mainly on the direct numerical simulation DNS and large eddy simulation LES of incompressible turbulent flows. Writing a high-order FEM code for turbulent simulation has been one of my interests for a long time. However, I found them too bulky and difficult to learn.
Spectral/hp Element Methods for Computational Fluid Dynamics
It appears that MFEM can be a good starting point for me. Because I am intended to simulate transitional turbulent flows, computational efficiency is of crucial importance for my research.
I have written a FVM code using Hypre, which works like magic. Using cores for 10 million cells, it takes about 1 second for each time step, which solves the Poisson equation twice. When I tried to run your ex4p with different cores in my personal computer, the parallel efficiency seems to be not so good.
Could you please give me some information about the speed of this library based on your own experience? Like, what is the most massive case you have run?
George Karniadakis and Spencer Sherwin
How many degree of freedom? How many cores were you using? How long does it take to solve one Poisson equation?
Large-scale parallel scalability is absolutely one of our main goals, and that's the reason why we are so tightly integrated with the hypre library. For example, in parallel we directly assemble in hypre 's parallel matrix format, so we can naively call its algebraic multigrid AMG solvers with minimal overhead.
Another advantage is that we can also provide hypre with discretization-specific information as in the AMS and ADS solvers. The effects of polynomial order p-refinement , spanwise domain extent and spanwise Fourier planes are considered, looking at flow statistics, convergence and sensitivity of the results. Thanks to the reduced computational cost, the proposed methods will have a strong impact in turbomachinery, paving the way to its use for design purposes and also allowing for a deeper understanding of the flow physics. Sign In or Create an Account.
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