Artificial neural networking (ANN) analysis for heat and entropy generation in flow of non‐Newtonian fluid between two rotating disks

2021 ◽  
Author(s):  
Tie‐Hong Zhao ◽  
M. Ijaz Khan ◽  
Yu‐Ming Chu
Keyword(s):  
Newtonian Fluid ◽  
Entropy Generation ◽  
Rotating Disks ◽  
Artificial Neural Networking ◽  
Artificial Neural

Entropy generation and heat transfer analysis for ferrofluid flow between two rotating disks with variable conductivity

2021 ◽  
pp. 095440622199118
Author(s):  
Anupam Bhandari
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Differential Equations ◽  
Entropy Generation ◽  
Heat Transfer Analysis ◽  
Entropy Generation Rate ◽  
Geometrical Parameters ◽  
Rotating Disks ◽  
Coupled Differential Equations ◽  
Lower Disk

Present model analyze the flow and heat transfer of water-based carbon nanotubes (CNTs) [Formula: see text] ferrofluid flow between two radially stretchable rotating disks in the presence of a uniform magnetic field. A study for entropy generation analysis is carried out to measure the irreversibility of the system. Using similarity transformation, the governing equations in the model are transformed into a set of nonlinear coupled differential equations in non-dimensional form. The nonlinear coupled differential equations are solved numerically through the finite element method. Variable viscosity, variable thermal conductivity, thermal radiation, and volume concentration have a crucial role in heat transfer enhancement. The results for the entropy generation rate, velocity distributions, and temperature distribution are graphically presented in the presence of physical and geometrical parameters of the flow. Increasing the values of ferromagnetic interaction number, Reynolds number, and temperature-dependent viscosity enhances the skin friction coefficients on the surface and wall of the lower disk. The local heat transfer rate near the lower disk is reduced in the presence of Harman number, Reynolds number, and Prandtl number. The ferrohydrodynamic flow between two rotating disks might be useful to optimize the use of hybrid nanofluid for liquid seals in rotating machinery.

Artificial neural networking in applied dentistry

2021 ◽  
Vol 13 (4) ◽  
pp. 289
Author(s):  
Varsha Kommalapati ◽  
M Sujesh ◽  
C Ravikumar ◽  
K Sunitha
Keyword(s):  
Artificial Neural Networking ◽  
Artificial Neural

Heat Transfer and Entropy Generation Characteristics of a Non-Newtonian Fluid Squeezed and Extruded Between Two Parallel Plates

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
P Kaushik ◽  
Pranab Kumar Mondal ◽  
Sukumar Pati ◽  
Suman Chakraborty
Keyword(s):  
Heat Transfer ◽  
Newtonian Fluid ◽  
Entropy Generation ◽  
Bejan Number ◽  
Engineering Systems ◽  
Power Law Model ◽  
Parallel Plates ◽  
Unsteady Heat Transfer ◽  
Thermodynamic Performance ◽  
Fluid Rheology

This study investigates the unsteady heat transfer and entropy generation characteristics of a non-Newtonian fluid, squeezed and extruded between two parallel plates. In an effort to capture the underlying thermo-hydrodynamics, the power-law model is used here to describe the constitutive behavior of the non-Newtonian fluid. The results obtained from the present analysis reveal the intricate interplay between the fluid rheology and the squeezing dynamics, toward altering the Nusselt number and Bejan number characteristics. Findings from this study may be utilized to design optimal process parameters for enhanced thermodynamic performance of engineering systems handling complex fluids undergoing simultaneous extrusion and squeezing.

scholarly journals Entropy Analysis on a Three-Dimensional Wavy Flow of Eyring–Powell Nanofluid: A Comparative Study

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Arshad Riaz ◽  
Ahmed Zeeshan ◽  
M. M. Bhatti
Keyword(s):  
Viscous Dissipation ◽  
Newtonian Fluid ◽  
Entropy Generation ◽  
Pressure Rise ◽  
Software Tool ◽  
Bejan Number ◽  
Mathematical Treatment ◽  
Cylindrical Domain ◽  
Special Cases ◽  
The Impact

The thermal management of a system needs an accurate and efficient measurement of exergy. For optimal performance, entropy should be minimized. This study explores the enhancement of the thermal exchange and entropy in the stream of Eyring–Powell fluid comprising nanoparticles saturating the vertical oriented dual cylindrical domain with uniform thermal conductivity and viscous dissipation effects. A symmetrical sine wave over the walls is used to induce the flow. The mathematical treatment for the conservation laws are described by a set of PDEs, which are, later on, converted to ordinary differential equations by homotopy deformations and then evaluated on the Mathematica software tool. The expression of the pressure rise term has been handled numerically by using numerical integration by Mathematica through the algorithm of the Newton–Cotes formula. The impact of the various factors on velocity, heat, entropy profile, and the Bejan number are elaborated pictorially and tabularly. The entropy generation is enhanced with the variation of viscous dissipation but reduced in the case of the concentration parameter, but viscous dissipation reveals opposite findings for the Newtonian fluid. From the abovementioned detailed discussion, it can be concluded that Eyring–Powell shows the difference in behavior in the entropy generation and in the presence of nanoparticles due to the significant dissipation effects, and also, it travels faster than the viscous fluid. A comparison between the Eyring-Powell and Newtonian fluid are also made for each pertinent parameter through special cases. This study may be applicable for cancer therapy in biomedicine by nanofluid characteristics in various drugs considered as a non-Newtonian fluid.

Development of sediment load estimation models by using artificial neural networking techniques

2015 ◽  
Vol 187 (11) ◽  
Author(s):  
Muhammad Hassan ◽  
M. Ali Shamim ◽  
Ali Sikandar ◽  
Imran Mehmood ◽  
Imtiaz Ahmed ◽  
...  
Keyword(s):  
Sediment Load ◽  
Load Estimation ◽  
Artificial Neural Networking ◽  
Artificial Neural ◽  
Sediment Load Estimation ◽  
Estimation Models
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