scholarly journals Inferring incompressible two-phase flow fields from the interface motion using physics-informed neural networks

2021 ◽  
pp. 100029
Author(s):  
Aaron B. Buhendwa ◽  
Stefan Adami ◽  
Nikolaus A. Adams
Keyword(s):  
Neural Networks ◽  
Two Phase Flow ◽  
Flow Fields ◽  
Phase Flow ◽  
Two Phase ◽  
Interface Motion

Dynamic mesh simulation on the effect of sloshing on concurrent two-phase flow fields for two-dimensionally packed columns simulations

Cryogenics ◽  
2021 ◽  
Vol 113 ◽  
pp. 103231
Author(s):  
Lingya Meng ◽  
Chongzheng Sun ◽  
Mengxian Zhang ◽  
Hui Han ◽  
Yuxing Li
Keyword(s):  
Two Phase Flow ◽  
Flow Fields ◽  
Packed Columns ◽  
Dynamic Mesh ◽  
Phase Flow ◽  
Two Phase

New void fraction equations for two-phase flow in helical heat exchangers using artificial neural networks

2018 ◽  
Vol 130 ◽  
pp. 149-160 ◽  
Author(s):  
A. Parrales ◽  
D. Colorado ◽  
J.A. Díaz-Gómez ◽  
A. Huicochea ◽  
A. Álvarez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Void Fraction ◽  
Heat Exchangers ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Artificial Neural

ARTIFICIAL NEURAL NETWORKS IN LIQUID-LIQUID TWO-PHASE FLOW

2012 ◽  
Vol 199 (12) ◽  
pp. 1520-1542 ◽  
Author(s):  
R. Shirley ◽  
D. P. Chakrabarti ◽  
G. Das
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Artificial Neural

Intelligent Regime Recognition in Upward Vertical Gas-Liquid Two Phase Flow Using Neural Network Techniques

2010 ◽  
Author(s):  
Soheil Ghanbarzadeh ◽  
Pedram Hanafizadeh ◽  
Mohammad Hassan Saidi ◽  
Ramin Bozorgmehry Boozarjomehry
Keyword(s):  
Neural Networks ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Data Bank ◽  
Hydrodynamic Characteristics ◽  
Multilayer Perceptrons ◽  
Phase Flow ◽  
Two Phase ◽  
Operational Conditions ◽  
Wide Range

In order to safe design and optimize performance of some industrial systems, it’s often needed to categorize two-phase flow into different regimes. In each flow regime, flow conditions have similar geometric and hydrodynamic characteristics. Traditionally, flow regime identification was carried out by flow visualization or instrumental indicators. In this research3 kind of neural networks have been used to predict system characteristic and flow regime, and results of them were compared: radial basis function neural networks, self organized and Multilayer perceptrons (supervised) neural networks. The data bank contains experimental pressure signalfor a wide range of operational conditions in which upward two phase air/water flows pass to through a vertical pipe of 5cm diameter under adiabatic condition. Two methods of signal processing were applied to these pressure signals, one is FFT (Fast Fourier Transform) analysis and the other is PDF (Probability Density Function) joint with wavelet denoising. In this work, from signals of 15 fast response pressure transducers, 2 have been selected to be used as feed of neural networks. The results show that obtained flow regimes are in good agreement with experimental data and observation.

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