Accelerating numerical simulations of supercritical fluid flows using deep neural networks

Petro Junior Milan, Xingjian Wang, Jean Pierre Hickey, Yixing Li, Vigor Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationAIAA Scitech 2020 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105951
Publication statusPublished - 2020
EventAIAA Scitech Forum, 2020 - Orlando, United States
Duration: 2020 Jan 62020 Jan 10

Publication series

NameAIAA Scitech 2020 Forum
Volume1 PartF


ConferenceAIAA Scitech Forum, 2020
CountryUnited States

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

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