A Comparative Study of Contrasting Machine Learning Frameworks Applied to RANS Modeling of Jets in Crossflow
Jack Weatheritt, Richard D Sandberg, Julia Ling, Gonzalo Saez, Julien Bodart
Proceedings of the ASME Turbo Expo: Turbine Technical Conference and Exposition, 2017, VOL 2B | American Society of Mechanical Engineers | Published : 2017
Classical RANS turbulence models have known deficiencies when applied to jets in crossflow. Identifying the linear Boussinesq stress-strain hypothesis as a major contribution to erroneous prediction, we consider and contrast two machine learning frameworks for turbulence model development. Gene Expression Programming, an evolutionary algorithm that employs a survival of the fittest analogy, and a Deep Neural Network, based on neurological processing, add non-linear terms to the stress-strain relationship. The results are Explicit Algebraic Stress Model-like closures. High fidelity data from an inline jet in crossflow study is used to regress new closures. These models are then tested on a sk..View full abstract
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Awarded by U.S. Department of Energy's National Nuclear Security Administration
The University of Melbourne acknowledges the funding of a veski fellowship. This work was also supported by resources provided by The Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia. Funding for J. Ling's work was provided by the Sandia LDRD program. Sandia National Laboratories is a multiprogram laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.SAND2016-11815 C.