Project Highlights
GE Global Research Enables Next-Generation Energy and Propulsion
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Aerodynamic noise is a barrier technology to the viability of next-generation “green” low-emission aircraft propulsion (jet and fan noise) and energy systems (wind turbine blade noise). Scientists at GE Global Research are actively developing design technologies to understand and reduce such noise sources. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. |
Aerodynamic noise is a barrier technology to the viability of next-generation “green” low-emission aircraft propulsion (jet and fan noise) and energy systems (wind turbine blade noise). Scientists at GE Global Research (GEGR) are actively developing design technologies to understand and reduce such noise sources. For aircraft engine noise, jet noise is the dominant noise source during take-off, and the complex turbulence flows that drive its generation are not fully understood. Hence, accurate and detailed multi-scale numerical simulations for realistic jet noise prediction can prove to be a game-changer in future development efforts.
Approach
There are two approaches to predicting jet noise. In the Reynolds Averaged Navier-Stokes (RANS) methodology, the total effect of all the turbulence scales is represented by a single scale. Since the generation of noise is a multi-scale phenomenon, RANS’s success has been limited. On the other hand, Large Eddy Simulation (LES), where the filtered (small) scales are modeled while the key large scales that dominate the noise sources are evolved, can simulate the large-scale turbulence of the jet and the generated acoustic field accurately. However, LES simulations are quite expensive with regard to memory and processor requirements. Such simulations, therefore, require scalable efficient solvers and large computational resources.
Results/Accomplishments
Applying an allocation of computer processor hours from the Argonne Leadership Computing Facility (ALCF) to GEGR, researchers have ported, tuned, and demonstrated both the scalability and accuracy of a Large Eddy Simulation (LES) solver on Argonne’s IBM Blue Gene/P system for massively parallel jet noise simulation. The LES code solves the three-dimensional unsteady Navier-Stokes equations, using a compact sixth-order finite difference scheme for spatial discretization and fourth-order Runge-Kutta or second-order Beam-Warming algorithms for time integration. The Message Passing Interface (MPI) is used for parallel operation of multiple grid blocks. As part of this effort, the memory and I/O bottlenecks have been successfully mitigated to enable parallel computation, using the large number of processors needed for realistic, non-academic jet noise simulations. Excellent solver scalability performance, up to 4,096 cores, for both large and small problems has been demonstrated. In addition, a LES jet noise simulation for the NASA acoustic reference nozzle, using 4,077 cores, has been completed in just 13 hours. The turbulent jet flow has reached a statistically stationary state, and the sampled flow (jet centerline velocity and potential core decay) and acoustic (far-field noise spectra) fields agree very well with the experimental data.
Future Efforts
With this successful proof-of-concept study, future work will focus on further developing and demonstrating the viability and potential impact of massively parallel, multi-scale LES simulations for noise prediction. Fundamental questions on noise mechanisms and turbulence scaling, as well as accuracy of more realistic/complex configurations, need to be investigated. Other applications of LES, now showing great promise in overcoming limitations of RANS methods (such as boundary layer noise and turbulent mixing for heat transfer), are of great significance to GE as well. The team plans to pursue an INCITE award from the U.S. Department of Energy to enable such an effort.
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Contacts
Dr. Chingwei Shieh (PI)
Energy and Propulsion Technologies
GE Global Research
Dr. Hao Shen (co-PI)
Energy and Propulsion Technologies
GE Global Research
Dr. Ramesh Balakrishnan
Argonne Leadership Computing Facility
Argonne National Laboratory
