Drag coefficient prediction for non-spherical particles in dense gas–solid two-phase flow using artificial neural network

2019 ◽  
Vol 354 ◽  
pp. 115-124 ◽  
Author(s):  
Shengnan Yan ◽  
Yurong He ◽  
Tianqi Tang ◽  
Tianyu Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Drag Coefficient ◽  
Two Phase Flow ◽  
Spherical Particles ◽  
Phase Flow ◽  
Dense Gas ◽  
Two Phase ◽  
Artificial Neural

Two-phase Flow Regime Identification Combining Conductivity Probe Signals and Artificial Neural Network

2007 ◽  
Author(s):  
Leonor Hernández ◽  
José Enrique Juliá ◽  
Sergio Chiva ◽  
Sidharth Paranjape ◽  
Mamoru Ishii
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Conductivity Probe ◽  
Flow Regime Identification ◽  
Artificial Neural

Pipeline Leak Detection in Two Phase Flow Based on Fluctuation Pressure Difference and Artificial Neural Network (ANN)

2014 ◽  
Vol 493 ◽  
pp. 186-191 ◽  
Author(s):  
Budi Santoso ◽  
Indarto ◽  
Deendarlianto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pressure Difference ◽  
Two Phase Flow ◽  
Leak Detection ◽  
Transport Infrastructure ◽  
Phase Flow ◽  
Two Phase ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Pipe network was an important part of the fluid transport infrastructure. On the other hand, the pipeline leak detection in two-phase flow using the flow and pressure parameters is very rarely studied. A system on the basis of the Artificial Neural Network (ANN) was proposed for detecting the pipeline leak for the two-phase plug flow by using the pressure difference measurement. In the present research, water-air mixture flows in pipe horizontal of 24 mm inner diameter. Artificial pipeline leak was modeled with the leak of solenoid valve on the bottom and top of pipe. Differential Pressure Transducer (DPT) was placed after the leak position and connected by the high-speed data acquisition. The fluctuations of the pressure difference signals were recorded as a time series of random data. The data of the combinations of the input flow rate, the pressure difference can be used to identify the pipeline leak in two-phase flow plug by using ANN. The results demonstrated a very good ability to the pipeline leak on two-phase flow.

Prediction of two-phase flow distribution in microchannel heat exchangers using artificial neural network

2020 ◽  
Vol 111 ◽  
pp. 53-62 ◽  
Author(s):  
Niccolo Giannetti ◽  
Mark Anthony Redo ◽  
Sholahudin ◽  
Jongsoo Jeong ◽  
Seiichi Yamaguchi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Exchangers ◽  
Two Phase Flow ◽  
Flow Distribution ◽  
Phase Flow ◽  
Two Phase ◽  
Artificial Neural
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