TY - GEN
T1 - Machine-Learning Based Amplification Factor Transport Equation for Transition Modeling
AU - Barraza, B.
AU - Castillo, P.
AU - Tena, A.
AU - Gross, A.
AU - Leinemann, M.
AU - Tsakagiannis, V.
AU - Fasel, H. F.
N1 - Funding Information:
This work was funded by the Hypersonic Vehicle Simulation Institute (HVSI) under grant number FA7000-20-2-0004 with Dr. Russ Cummings serving as program manager. The undergraduate student that participated in the project was supported by the New Mexico Alliance for Minority Participation Undergraduate Research Scholars (URS) program.
Publisher Copyright:
© 2021, American Institute of Aeronautics and Astronautics Inc.. All rights reserved.
PY - 2021
Y1 - 2021
N2 - The accurate prediction of laminar-turbulent transition is critically important for many aeronautical applications. Transition models that perform satisfactorily at low-Mach numbers were shown not to be suitable for supersonic and hypersonic flows as the transition mechanisms are different and substantially more complex. As a first step towards the development of a hypersonic transition model, a methodology for deriving amplification factor transport equations based on neural networks that are trained with results from linear stability theory computations is proposed. As an example, an incompressible amplification factor transport equation is developed and integrated into the three-equation 2019 Coder transition model (AIAA-2019-0039). The new neural network model is validated for an incompressible zero-pressure-gradient flat-plate boundary layer with a Tollmien-Schlichting primary instability. The model is also validated for an S809 airfoil and a linear low-pressure turbine cascade that develop laminar separation bubbles. Finally, to demonstrate that the model is also suited for three-dimensional problems, the flow through a linear cascade with endwall effects is considered.
AB - The accurate prediction of laminar-turbulent transition is critically important for many aeronautical applications. Transition models that perform satisfactorily at low-Mach numbers were shown not to be suitable for supersonic and hypersonic flows as the transition mechanisms are different and substantially more complex. As a first step towards the development of a hypersonic transition model, a methodology for deriving amplification factor transport equations based on neural networks that are trained with results from linear stability theory computations is proposed. As an example, an incompressible amplification factor transport equation is developed and integrated into the three-equation 2019 Coder transition model (AIAA-2019-0039). The new neural network model is validated for an incompressible zero-pressure-gradient flat-plate boundary layer with a Tollmien-Schlichting primary instability. The model is also validated for an S809 airfoil and a linear low-pressure turbine cascade that develop laminar separation bubbles. Finally, to demonstrate that the model is also suited for three-dimensional problems, the flow through a linear cascade with endwall effects is considered.
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U2 - 10.2514/6.2021-2706
DO - 10.2514/6.2021-2706
M3 - Conference contribution
AN - SCOPUS:85126816994
SN - 9781624106101
T3 - AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021
BT - AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021
Y2 - 2 August 2021 through 6 August 2021
ER -