Engineers performing computational simulations of flow physics are often faced with a trade-off between turn-around time and accuracy. High-fidelity models that can accurately capture small details of flow, such as turbulent mixing, are typically too expensive and are therefore reserved for studying smaller, component level problems. Standard models, like Reynolds-Averaged Navier-Stokes (RANS) and Unsteady-RANS, are used to predict larger interactions without the ability to accurately compute the small scales, at a lower computational cost than high-fidelity models. However, with specific algorithmic choices and access to large-scale GPU systems, we can demonstrate high-fidelity simulations of large engine sections that can be completed within engineering design cycle turn-around times, instead of the typical weeks to months required for high fidelity simulations.In this paper we present the high-order GENESIS code, employed in the simulation of complex turbulent flows inside the high-pressure turbine of a jet engine. The implementation of GENESIS exhibits exceptional suitability for thread-parallel compute architectures. The code efficiently exploits GPU accelerators to execute high-fidelity simulations in a fraction of the time previously required, while also demonstrating extraordinary accuracy validated by experimental data and previous RANS model predictions.The new capability allows for computational studies of phenomena such as laminar to turbulent transition and wake mixing, all applied to relevant three-dimensional geometries present in the high-pressure turbine.

Gpu-Enabled High-Fidelity Les Simulations for Turbomachinery Flows

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2021/volume/84898

Turbo Expo 2021

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