A hybrid-grid compressible flow solver for large-scale supersonic jet noise simulations on multi-GPU clusters

by Andrew Corrigan

Laboratory for Propulsion, Energetics, and Dynamic Systems, Naval Research Laboratory


A compressible flow solver for multi-GPU clusters has been developed for performing large-scale supersonic jet noise and other high-speed compressible flow simulations over hybrid grids. While supersonic jet noise simulations require the accurate representation of complex nozzle geometry and thus the use of unstructured grids, much of the domain geometry can be represented sufficiently with structured grids, which drastically reduces memory bandwidth consumption and storage. Therefore, hybrid grids are employed, which combine an unstructured grid representation in the vicinity of the nozzle with a structured grid representation in the wake region of the flow field. Performance benchmarks are drawn from large-scale runs performed using this solver.

Speaker Bio

Andrew Corrigan received his Ph.D. in Computational Science from George Mason University in 2009. He now now works as a Research Mathematician at the U.S. Naval Research Lab in the Laboratories for Computational Physics & Fluid Dynamics, developing the Jet Engine Noise Reduction Code