TY - GEN
T1 - Accelerating numerical simulations of supercritical fluid flows using deep neural networks
AU - Milan, Petro Junior
AU - Wang, Xingjian
AU - Hickey, Jean Pierre
AU - Li, Yixing
AU - Yang, Vigor
N1 - Funding Information:
Support for this research was provided jointly by the Air Force Office of Scientific Research (AFOSR), under Grant No. FA9550-18-1-0216, and the Natural Sciences and Engineering Research Council of Canada (NSERC).
Publisher Copyright:
© 2020, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2020
Y1 - 2020
N2 - We present a machine learning framework for accelerated simulation of supercritical fluid flows by training a deep feedforward neural network (DFNN) to estimate the real-fluid thermophysical properties at the projected conditions of interest. This DFNN model, which replaces the compute-intensive, real-fluid state equations, is coupled to a density-based, finite-volume solver with a preconditioned dual-time-stepping technique in which the primitive variables are adopted as the independent variables in the pseudo-time iteration loop. The DFNN is designed to take in, at each time step, the updated temperature and chemical composition and output (while assuming a constant background pressure) all relevant thermophysical properties that are used by the subsequent routines in the solver. The proposed methodology is implemented and tested in a fully-compressible, large-eddy simulation of supercritical turbulent mixing between gaseous oxygen and liquid kerosene in a swirl coaxial rocket injector. The accuracy and efficiency of the neural network are examined in both the a priori and a posteriori settings.
AB - We present a machine learning framework for accelerated simulation of supercritical fluid flows by training a deep feedforward neural network (DFNN) to estimate the real-fluid thermophysical properties at the projected conditions of interest. This DFNN model, which replaces the compute-intensive, real-fluid state equations, is coupled to a density-based, finite-volume solver with a preconditioned dual-time-stepping technique in which the primitive variables are adopted as the independent variables in the pseudo-time iteration loop. The DFNN is designed to take in, at each time step, the updated temperature and chemical composition and output (while assuming a constant background pressure) all relevant thermophysical properties that are used by the subsequent routines in the solver. The proposed methodology is implemented and tested in a fully-compressible, large-eddy simulation of supercritical turbulent mixing between gaseous oxygen and liquid kerosene in a swirl coaxial rocket injector. The accuracy and efficiency of the neural network are examined in both the a priori and a posteriori settings.
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U2 - 10.2514/6.2020-1157
DO - 10.2514/6.2020-1157
M3 - Conference contribution
AN - SCOPUS:85087871248
SN - 9781624105951
T3 - AIAA Scitech 2020 Forum
BT - AIAA Scitech 2020 Forum
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA Scitech Forum, 2020
Y2 - 6 January 2020 through 10 January 2020
ER -