Development and application of ANN model for property prediction of supercritical kerosene

DOI10.1016/J.COMPFLUID.2020.104665OpenAlexW3039237936MaRDI QIDQ2019935

Yanyan Li

Publication date: 22 April 2021

Published in: Computers and Fluids (Search for Journal in Brave)

Full work available at URL: http://dspace.imech.ac.cn/handle/311007/84807

zbMATH Keywords

OpenFOAMartificial neural network (ANN)principle of extended corresponding state (ECS)RP-3 kerosenesuperitical pressure

Mathematics Subject Classification ID

Fluid mechanics (76-XX)

Uses Software

• OpenFOAM

Cites Work

• A neural network approach for prediction of critical submergence of an intake in still water and open channel flow for permeable and impermeable bottom
• Consistent look-up table interpolation method for real-gas flow simulations
• Simulation of real gas effects in supersonic methane jets using a tabulated equation of state with a discontinuous Galerkin spectral element method
• New high-resolution central schemes for nonlinear conservation laws and convection-diffusion equations
• Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network
• Algebraic zero-equation versus complex two-equation turbulence modeling in supercritical fluid flows
• A low-dissipative, scale-selective discretization scheme for the Navier-Stokes equations
• Non-iterative estimation of heat transfer coefficients using artificial neural network models
• Learning representations by back-propagating errors

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