scholarly journals Multi-phase Flow Simulations in Direct Iron Ore Smelting Reduction Process

2006 ◽  
Vol 46 (8) ◽  
pp. 1158-1164 ◽  
Author(s):  
Hsin-Chien Chuang ◽  
Jer-Haur Kuo ◽  
Chien-Chia Huang ◽  
Shih-Hsien Liu ◽  
Weng-Sing Hwang
Keyword(s):  
Iron Ore ◽  
Reduction Process ◽  
Phase Flow ◽  
Smelting Reduction ◽  
Flow Simulations ◽  
Multi Phase Flow ◽  
Multi Phase

Towards multi-phase flow simulations in the PDE framework Peano

2011 ◽  
Vol 48 (3) ◽  
pp. 365-376 ◽  
Author(s):  
Hans-Joachim Bungartz ◽  
Bernhard Gatzhammer ◽  
Michael Lieb ◽  
Miriam Mehl ◽  
Tobias Neckel
Keyword(s):  
Phase Flow ◽  
Flow Simulations ◽  
Multi Phase Flow ◽  
Pde Framework ◽  
Multi Phase

Interface Reconstruction in Multi-fluid, Multi-phase Flow Simulations

2006 ◽  
pp. 19-32 ◽  
Author(s):  
Rao V. Garimella ◽  
Vadim Dyadechko ◽  
Blair K. Swartz ◽  
Mikhail J. Shashkov
Keyword(s):  
Phase Flow ◽  
Flow Simulations ◽  
Interface Reconstruction ◽  
Multi Phase Flow ◽  
Multi Phase

scholarly journals Thermodynamics-Informed Neural Network (TINN) for Phase Equilibrium Calculations Considering Capillary Pressure

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7724
Author(s):  
Tao Zhang ◽  
Shuyu Sun
Keyword(s):  
Neural Network ◽  
Phase Equilibrium ◽  
Phase Flow ◽  
Fluid Mixture ◽  
Robust Algorithms ◽  
Flow Simulations ◽  
Crucial Component ◽  
Reservoir Conditions ◽  
Multi Phase Flow ◽  
Multi Phase

The thermodynamic properties of fluid mixtures play a crucial role in designing physically meaningful models and robust algorithms for simulating multi-component multi-phase flow in subsurface, which is needed for many subsurface applications. In this context, the equation-of-state-based flash calculation used to predict the equilibrium properties of each phase for a given fluid mixture going through phase splitting is a crucial component, and often a bottleneck, of multi-phase flow simulations. In this paper, a capillarity-wise Thermodynamics-Informed Neural Network is developed for the first time to propose a fast, accurate and robust approach calculating phase equilibrium properties for unconventional reservoirs. The trained model performs well in both phase stability tests and phase splitting calculations in a large range of reservoir conditions, which enables further multi-component multi-phase flow simulations with a strong thermodynamic basis.

Parallelizing and optimizing large-scale 3D multi-phase flow simulations on the Tianhe-2 supercomputer

2015 ◽  
Vol 28 (5) ◽  
pp. 1678-1692 ◽  
Author(s):  
Dali Li ◽  
Chuanfu Xu ◽  
Yongxian Wang ◽  
Zhifang Song ◽  
Min Xiong ◽  
...  
Keyword(s):  
Large Scale ◽  
Phase Flow ◽  
Flow Simulations ◽  
Multi Phase Flow ◽  
Multi Phase
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