The goal of this study is to advance the science of subsonic and supersonic jet noise prediction for modern-day turbofan aircraft engines using petascale computing. Jet noise is an important issue due to increased air traffic, penalties for noisier aircraft, future stringent noise regulations and military operational requirements. Previous experiments have shown that a 50% decrease in jet noise power output can be achieved by certain chevron and lobe mixer designs without essentially changing the net thrust of the engine. However, the physical mechanisms for the reduced noise are not well understood. The effect on noise of mixing devices, in particular chevrons and lobed mixers, is the focus of the present work.
The PIs will investigate turbulent mixing by accurately simulating it with advanced computational techniques based on large eddy simulation (LES). Integral acoustic methods will extend the computational fluid dynamics (CFD) results to the far-field. Processing speeds and memory sizes of existing supercomputers limit current simulations to low Reynolds numbers and idealized geometries for the mixing devices. Thus, these simulations do not allow design and optimization of mixing for noise reduction, especially since these mixing devices influence the high frequencies of the noise spectrum, increasing the grid resolution requirements. Modeling at realistic Reynolds numbers and nozzle geometries requires tens of billions of points. The PIs algorithms will be designed to take advantage of multi-level parallelism and, within a node of such an architecture, address the 'memory wall' aspect of multicore architectures where the cost of arithmetic operations is much smaller than memory references. These algorithms will be based on a mixture of the transposition scheme and the multi-block approaches we used in the past. The methodology will be validated by making comparisons with both turbulence and acoustics measurements from high-quality experiments using realistic nozzle geometries.
This project represents a computational-engineering activity integrating modern modeling, parallel algorithm design, and fine-tuned implementations on petascale architectures. The project will help promote interdisciplinary research, teaching, training, and learning by training three Ph.D. graduate students. One undergraduate research assistant will be involved during each summer for the five years of the project. Outreach to inner-city high school students from Indianapolis will be done through Purdue's Science Bound Program. The findings of this research will be shared with the aerospace and computer industries. As appropriate, results will also be shared with the general public through the Purdue News Service.
