Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session M34: Convection & Buoyancy Driven Flows: Numerical Simulations
8:00 AM–9:57 AM,
Tuesday, November 20, 2018
Georgia World Congress Center
Room: B406
Chair: Yogesh Jaluria, Rutgers University
Abstract ID: BAPS.2018.DFD.M34.4
Abstract: M34.00004 : Simulating Low-Mach Compressible Flow at Large Prandtl Numbers with a High-Order Fully-Implicit All-Speed Flow Solver*
8:39 AM–8:52 AM
Presenter:
Brian Weston
(Lawrence Livermore National Laboratory)
Authors:
Brian Weston
(Lawrence Livermore National Laboratory)
Amanda Braun
(Purdue)
Robert Nourgaliev
(Lawrence Livermore National Laboratory)
Jean-Pierre Delplanque
(Univ of California - Davis)
We present a high-order, fully-implicit fluid dynamics solver for simulating very low-Mach compressible flow. The work is motivated by the development of large-scale simulations of high-explosive cookoff, which requires modeling multi-species/multi-phase reactive flow at high Peclet numbers.
The governing equations are discretized in space up to 5th-order with a reconstructed Discontinuous Galerkin method and integrated in time with L-stable fully implicit time discretization schemes. The resulting set of non-linear equations is converged using a robust physics-block based preconditioned Newton-Krylov solver, with the Jacobian-free version of the GMRES solver. We implement the low-Mach version of the AUSM+-up Riemann solver, which correctly mimics the pressure fluctuations of an incompressible flow solver in the asymptotic limit of small Mach number.
We demonstrate that our fully-implicit flow solver is able to robustly converge compressible flow calculations with Mach numbers less than 1.e-5. Furthermore, thin thermal boundary layers at high Prandtl numbers are easily resolved with a high-order discretization scheme.*This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.DFD.M34.4
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