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A Comparative Study of Contrasting Machine Learning Frameworks Applied to RANS Modeling of Jets in Crossflow

Weatheritt, Jack and Sandberg, Richard D. and Ling, Julia and Saez, Gonzalo and Bodart, Julien A Comparative Study of Contrasting Machine Learning Frameworks Applied to RANS Modeling of Jets in Crossflow. (2017) In: ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition, 26 June 2017 - 30 June 2017 (Charlotte, United States).

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Official URL: http://dx.doi.org/10.1115/GT2017-63403

Abstract

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 skewed jet to ascertain their predictive efficacy. For both methodologies, a vast improvement over the linear relationship is observed.

Item Type:Conference or Workshop Item (Paper)
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Other partners > University of Melbourne (AUSTRALIA)
Other partners > Honeywell (USA)
Other partners > National Nuclear Security Administration - NNSA (USA)
Other partners > Sandia (USA)
Laboratory name:
Statistics:download
Deposited By: Julien Bodart
Deposited On:19 Feb 2018 12:14

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