High-Mach-Number Turbulence Modeling using Machine Learning and Direct Numerical Simulation Database


In this paper, a physics-informed machine learning approach is applied to improve the accuracy of the Reynolds stresses modeled by Reynolds-averaged Navier-Stokes (RANS) for high-speed at-plate turbulent boundary layers using an existing DNS database. In the machine-learning technique, the DNS dataset of a Mach 2:5 adiabatic turbulent boundary layer is used as the training flow to construct the invariant basis for learning the functional form of the discrepancy in RANS modeled Reynolds stresses. The functional thus constructed is in turn used to correct the RANS prediction of Reynolds stresses for turbulent boundary layers under two cold-wall hypersonic conditions with nominal freestream Mach numbers of 6 and 8. The study shows that the RANS-modeled Reynolds normal stresses, the turbulent kinetic energy, and the Reynolds-stress anisotropy can be significantly improved using the machine-learning technique. Such a study lays the foundation towards better physics-based turbulence modeling for high-Mach-number turbulent flows.

Meeting Name

55th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum (2017: Jan. 9-13, Grapevine, TX)


Mechanical and Aerospace Engineering


The DNS database of high-speed turbulent boundary layers used in the paper is generated based on the work supported by the Air Force Office of Scientific Research under award number FA9550-14-1-0170, managed by Dr. Ivett Leyva.

Keywords and Phrases

Turbulence models; Models; Reynolds stresses

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Document Type

Article - Conference proceedings

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© 2017 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.

Publication Date

01 Jan 2017

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