Simulating High-Speed Flows with NASA FUN3D

AIAA Greater Huntsville Section

Join us for a virtual talk on Thursday 2026 JAN 22 as Dr. Gabriel Nastac describes how using GPUs enables closed-loop retro-propulsive CFD

AIAA GHS Speaker Series

Guest lectures hosted by AIAA GHS on a variety of topics, both in-person and/or virtual. All are free to attend.

Abstract:Over the last several years, there have been several enhancements to the NASA Computational Fluid Dynamics (CFD) solver FUN3D in the areas of high-performance computing (HPC), trajectory-CFD coupling, and mesh adaptation. HPC has been trending towards graphics processing units (GPUs) for the last decade; about 90% of the top 100 publicly disclosed supercomputers in the world utilize GPUs for performance. This is in contrast to only a few machines about a decade ago. FUN3D has been rearchitected in recent years to run on multicore CPUs and NVIDIA, AMD, and Intel GPUs with a thin abstraction layer and has been demonstrated at scale on DOE’s recent exascale supercomputers, Frontier and Aurora. FUN3D has also been recently coupled to NASA POST2 to enable closed-loop trajectory CFD simulations. This effort has been demonstrated at scale on the exascale supercomputers for a human-scale Mars lander retropropulsion concept. The POST2 flight controller controls main engine and RCS throttling in the simulation to enable controlled flight. Several 30-second powered descent trajectories have been performed with each spanning a couple days of wall time. Lastly, fully automated hybrid mesh adaptation from CAD has been developed in recent years and demonstrated on high-speed flow applications; the process yields comparable results to structured mesh approaches. The process has been utilized for thousands of simulations to date internally at NASA supporting various efforts with successful comparisons to flight.

Figure 1: Hypersonic flow over the NASA X-38. A hybrid CAD-to-Solution mesh adaptation approach is utilized, where the user supplies a CAD file and flow solver inputs and runs CFD and mesh refinement iteratively. A thin boundary-layer grid is generated to match a target cell Reynolds number of unity. The mesh consists of 5 million points. The right figure plots heat transfer on the surface and Mach number on the centerline y-slice.

Figure 2:Instantaneous solution quantities shown for a static Mach 1.4 CFD solution of powered descent of a human-scale Mars lander concept on a mesh consisting of 33 billion elements using 33,880 GPUs, or 90% of Frontier. From left to right, contours show the mass fractions of the hydroxyl radical and H2O, the temperature in Kelvin, and the local Mach number.

About the Speaker:Dr. Gabriel Nastac is a research aerospace engineer at NASA’s Langley Research Center. He received his B.S. in Mechanical and Aerospace Engineering from Case Western Reserve University, his M.S. in Mechanical Engineering from Stanford University, and his Ph.D. in Mechanical Engineering from the University of Alabama in Huntsville. His research interests include GPU computing, high-speed reacting flows, and mesh adaptation.

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Simulating High-Speed Flows with NASA FUN3D

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Simulating High-Speed Flows with NASA FUN3D

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