Numerical investigation of magneto-nanoparticles for unsteady 3D generalized Newtonian liquid flow

2017 ◽  
Vol 132 (9) ◽  
Author(s):  
Latif Ahmad ◽  
Masood Khan ◽  
Waqar Azeem Khan
Author(s):  
Suman Debnath ◽  
Anirban Banik ◽  
Tarun Kanti Bandyopadhyay ◽  
Mrinmoy Majumder ◽  
Apu Kumar Saha

2017 ◽  
Vol 57 (1) ◽  
pp. 380-391 ◽  
Author(s):  
Ruibing Li ◽  
Shibo Kuang ◽  
Tingan Zhang ◽  
Yan Liu ◽  
Aibing Yu

1998 ◽  
Vol 167 (1) ◽  
pp. 133-146 ◽  
Author(s):  
TAPAN KUMAR BANERJEE ◽  
SUDIP KUMAR DAS

Author(s):  
Erfan Niazi ◽  
Mehrzad Shams ◽  
Arash Elahi ◽  
Goodarz Ahmadi

In this article a CFD model of a three-dimensional Eulerian-Lagrangian is developed for a gas - non-Newtonian liquid flow in a rectangular column. The model resolves the time-dependent, three-dimensional motion of gas bubbles in a liquid to simulate the trajectory of bubbles. Our model incorporates drag, gravity, buoyancy, lift, pressure gradient and virtual mass forces acting on a bubble rising in a liquid, and accounts for two-way momentum coupling between the phases. Population balance equation is solved to model bubble coalescence and break up. In bubble coalescence, Prince and Blanch model is used which can consider the effect of fluid rheology. Luo and Svendosen model was selected for bubble break up. The standard k-e turbulence model is selected for calculating turbulent flow properties. Power-law non-Newtonian liquid is selected for analysis of effect of different solutions of carboxy methyl cellulose in water. The effect of changing fluid to non-Newtonian is discussed in terms of velocity profile and gas hold up.


2014 ◽  
Vol 917 ◽  
pp. 244-256 ◽  
Author(s):  
Nirjhar Bar ◽  
Sudip Kumar Das

This paper is an attempt to compare the the performance of the three different Multilayer Perceptron training algorithms namely Backpropagation, Scaled Conjugate Gradient and Levenberg-Marquardt for the prediction of the gas hold up and frictional pressure drop across the vertical pipe for gas non-Newtonian liquid flow from our earlier experimental data. The Multilayer Perceptron consists of a single hidden layer. Four different transfer functions were used in the hidden layer. All three algorithms were useful to predict the gas holdup and frictional pressure drop across the vertical pipe. Statistical analysis using Chi-square test (χ2) confirms that the Backpropagation training algorithm gives the best predictability for both cases.


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