Accelerating numerical simulations of supercritical fluid flows using deep neural networks

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

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題AIAA Scitech 2020 Forum
發行者American Institute of Aeronautics and Astronautics Inc, AIAA
ISBN(列印)9781624105951
DOIs
出版狀態Published - 2020
事件AIAA Scitech Forum, 2020 - Orlando, United States
持續時間: 2020 一月 62020 一月 10

出版系列

名字AIAA Scitech 2020 Forum
1 PartF

Conference

ConferenceAIAA Scitech Forum, 2020
國家United States
城市Orlando
期間20-01-0620-01-10

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

指紋 深入研究「Accelerating numerical simulations of supercritical fluid flows using deep neural networks」主題。共同形成了獨特的指紋。

引用此