Petascale large eddy simulation of jet engine noise based on the truncated SPIKE algorithm

With the emergence of petascale computing platforms, high-fidelity computational aeroacoustics (CAA) simulation has become a feasible, robust and accurate tool that complements theoretical and empirical approaches in the prediction of sound levels generated by aircraft airframes and engines. Differentiating itself from the broader discipline of computational fluid dynamics, CAA is particularly challenging as it demands high accuracy, good spectral resolution, and low dispersion and diffusion errors from the underlying numerical methods. Large eddy simulation based on space-implicit high-order compact finite difference schemes has been shown to meet such stringent requirements. In this paper, we discuss a new, scalable parallelization scheme with a three-dimensional computational space partitioning. Unlike many traditional multiblock computational fluid dynamics (CFD) methods, our partitioning is non-overlapping. We use the truncated SPIKE algorithm to solve the governing equations accurately and limit one-sided biased differentiation to just the physical boundaries. We present experimental performance data collected on Kraken and Ranger, two near-petascale computing platforms.