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NASA SLS Flight Condition Analyses Time Reduced from Days to Hours

The NASA Space Launch System (SLS) is a key element of the Artemis program that will provide the capabilities required for manned deep-space exploration. As currently planned, this family of heavy-lift vehicles will enable exploration of the Moon and Mars. Three classes of the SLS configuration, each of which includes both a crew and cargo version, are shown in Fig. 1. The large propulsive force needed to carry the targeted payloads is provided by two solid rocket boosters side-mounted onto a centerbody that embodies four RS-25 engines. The payload, whether the Orion crew capsule or a cargo compartment, is mounted atop the centerbody.

A new parallel processing toolbox for PyTecplot has been developed … reducing the postprocessing time by a factor of 12 …

SLS Configurations

Figure 1. SLS Configurations

Analyzing Wide Range of SLS Flight Conditions

Aerodynamic support for the SLS requires the use of both wind-tunnel tests and computational simulations to develop aerodynamic databases across the flight mission profile, seen in Fig. 2. These data are generated for a range of flight regimes including launch, liftoff, ascent, and booster separation. Flight conditions for the SLS vary from low-speed conditions on or near the launchpad to supersonic speeds during ascent. Because of this wide range of flight conditions, numerous tools are required to accurately capture the properties of the complex flowfields that evolve over time. While experimental results are useful and necessary, computational simulations yield results at flight conditions not easily tested in a wind tunnel facility, and these results include some fine-scale details that are not measurable in a wind tunnel.

SLS mission profile.

Figure 2: SLS mission profile.

In order to accurately capture the massively-separated flowfields that arise during launch of the vehicles, an unsteady CFD solver was utilized. These unsteady IDDES (improved delayed detached eddy simulation) CFD calculations yield a wealth of information that can be interrogated to provide visualizations of the evolving flowfield. As an example, a solution animation in which isosurfaces of constant Q criterion are colored by the magnitude of vorticity is shown in Fig. 3.

Figure 3: Animation showing isosurfaces of constant Q criterion flooded by the magnitude of vorticity.

Unfortunately, the data files from which the animation was extracted are extremely large, often being in excess of 10 terabytes of data, even when saving just a subset of the simulation data. Consequently, reduction of these data is time intensive from both human and computational perspectives, to the point where it is prohibitive to employ these techniques as an everyday tool for database-level analyses.

12x Reduction in Postprocessing Time with PyTecplot

In collaboration with Tecplot developers, a new parallel processing toolbox for PyTecplot has been developed and implemented to reduce the aforementioned datasets. Use of these new methods has reduced the postprocessing time by a factor of 12 relative to the baseline reduction methods. These newly-developed parallel data reduction routines reduced the time to make a movie, such as this one, from days to hours, thus enabling analyses across a much wider range of flight conditions.

More information on Artemis and SLS can be found on the NASA website.

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