The field of jet noise has become one of most active areas of research due to increasingly stringent aircraft noise regulations. This talk will discuss the design and implementation of an in-house petascalable noise prediction tool based on large eddy simulation (LES) technique to improve the fidelity of subsonic jet noise predictions. Such tools are needed to help drive the design of quieter jets. The focus of the work is to target computational performance and improved noise prediction fidelity through better matching experimental jet conditions and/or inclusion of the nozzle as part of the simulation. The algorithmic choices and optimizations which resulted in efficient scalability tested on up to 91,125 processors (or a theoretical speed of 1 petaflop/s) will be discussed. The second part of the talk will be about the application of the LES tool for subsonic jet noise specifically focusing on improving the numerical fidelity. Production runs with up to first ever one-billion-point grids without the nozzle and 125-million-point grids with the nozzle will be discussed. It is hoped that the predicted noise levels with improved fidelity will help drive the design of quieter nozzles.

Bio:

Chandra Sekhar Martha received his Bachelor’s degree in Aerospace engineering from Indian Institute of Technology, Madras in 2007. He then moved to the United States to purse graduate studies and received his Master’s degree in Aeronautics and Astronautics with a specialization in computational engineering from Purdue University, West Lafayette in 2010. He started his doctoral studies in Aerodynamics at Purdue University in May, 2010 and graduated in January, 2013. His doctoral work focused on computational noise investigation of subsonic jets using high performance computing. His research interests are Computational Fluid Dynamics (CFD), Large Eddy Simulation (LES), Computational Aeroacoustics (CAA) and Parallel Computing.