Data-driven turbulence model for flow separation over the Boeing Gaussian Bump

ORAL

Abstract

Existing RANS models have struggled to predict the smooth-body separation on the Boeing Gaussian Bump. We create a new RANS model that is based on a transport equation for the RANS eddy viscosity. We use a neural network to learn the eddy viscosity source terms using training data from DNS and experimental data published in the literature. We further discuss the applicability of this effort to develop models that generalize to predicting flow separation for airfoils at angles of attack near the stall condition.

*This work was funded by the Exascale Computing Project (Grant17-SC-20SC), a collaborative effort of two US Department of Energy organizations (Office of Science and the National Nuclear Security Administration). This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

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Presenters

  • Kevin P Griffin

    • National Renewable Energy Laboratory

Authors

  • Kevin P Griffin

    • National Renewable Energy Laboratory

  • Ashesh Sharma

    • National Renewable Energy Laboratory

  • Ganesh Vijayakumar

    • National Renewable Energy Laboratory

  • Michael A Sprague

    • National Renewable Energy Laboratory