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GTC ON-DEMAND

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Abstract:
Well discuss the plan for the human exploration of Mars, which will require the retropropulsive deceleration of payloads. Conventional computational capabilities dont allow for the simulation of interactions between the atmosphere and retropropulsion exhaust plumes at sufficient spatial resolution to resolve governing phenomena with a high level of confidence. Researchers from NASA Langley and Ames Research Centers, NVIDIA Corporation, and Old Dominion University have developed a GPU-accelerated version of Langley''s FUN3D flow solver. An ongoing campaign on the Summit supercomputer at Oak Ridge National Lab is using this capability to apply detached eddy simulation methods to retropulsion in atmospheric environments for nominal operation of a human-scale Mars lander concept. Well give an overview of the Mars lander fluid dynamics project, the history and details of FUN3D GPU development, and the optimization and performance of the code on emerging HPC architectures.
Well discuss the plan for the human exploration of Mars, which will require the retropropulsive deceleration of payloads. Conventional computational capabilities dont allow for the simulation of interactions between the atmosphere and retropropulsion exhaust plumes at sufficient spatial resolution to resolve governing phenomena with a high level of confidence. Researchers from NASA Langley and Ames Research Centers, NVIDIA Corporation, and Old Dominion University have developed a GPU-accelerated version of Langley''s FUN3D flow solver. An ongoing campaign on the Summit supercomputer at Oak Ridge National Lab is using this capability to apply detached eddy simulation methods to retropulsion in atmospheric environments for nominal operation of a human-scale Mars lander concept. Well give an overview of the Mars lander fluid dynamics project, the history and details of FUN3D GPU development, and the optimization and performance of the code on emerging HPC architectures.  Back
 
Topics:
HPC and AI
Type:
Talk
Event:
GTC Washington D.C.
Year:
2019
Session ID:
DC91220
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