scholarly journals Intelligent computing technique based supervised learning for squeezing flow model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maryam Mabrook Almalki ◽  
Eman Salem Alaidarous ◽  
Dalal Adnan Maturi ◽  
Muhammad Asif Zahoor Raja ◽  
Muhammad Shoaib
Keyword(s):  
Differential Equations ◽  
Volume Flow Rate ◽  
Squeezing Flow ◽  
Parallel Plates ◽  
Data Set ◽  
Transition Analysis ◽  
Computing Technique ◽  
Intelligent Computing ◽  
Computing Paradigm ◽  
Error Dynamics

AbstractIn this study, the unsteady squeezing flow between circular parallel plates (USF-CPP) is investigated through the intelligent computing paradigm of Levenberg–Marquard backpropagation neural networks (LMBNN). Similarity transformation introduces the fluidic system of the governing partial differential equations into nonlinear ordinary differential equations. A dataset is generated based on squeezing fluid flow system USF-CPP for the LMBNN through the Runge–Kutta method by the suitable variations of Reynolds number and volume flow rate. To attain approximation solutions for USF-CPP to different scenarios and cases of LMBNN, the operations of training, testing, and validation are prepared and then the outcomes are compared with the reference data set to ensure the suggested model’s accuracy. The output of LMBNN is discussed by the mean square error, dynamics of state transition, analysis of error histograms, and regression illustrations.

scholarly journals Intelligent Computing Technique Based Supervised Learning for Squeezing Flow Model

2021 ◽  
Author(s):  
Maryam Mabrook Almalki ◽  
Eman Salem Alaidarous ◽  
Dalal Maturi ◽  
Muhammad Asif Zahoor Raja ◽  
Muhammad Shoaib
Keyword(s):  
Differential Equations ◽  
Volume Flow Rate ◽  
Squeezing Flow ◽  
Parallel Plates ◽  
Data Set ◽  
Transition Analysis ◽  
Computing Technique ◽  
Intelligent Computing ◽  
Computing Paradigm ◽  
Error Dynamics

Abstract In this study, the unsteady squeezing flow between infinite parallel plates (USF-IPP) is investigated through the intelligent computing paradigm of Levenberg-Marquard backpropagation neural networks (LMBNN). Similarity transformation introduces the fluidic system of the governing partial differential equations (PDEs) into nonlinear ordinary differential equations (ODEs). A dataset is generated based on squeezing fluid flow system USF-IPP for the LMBNN through the Runge-Kutta method by the suitable variations of Reynolds number and volume flow rate. TO attain approximation solutions for USF-IPP to different scenarios and cases of LMBNN, the operations of training, testing, and validation are prepared and then the outcomes are compared with the reference data set to ensure the suggested model's accuracy. The output of LMBNN is discussed by the mean square error, dynamics of state transition, analysis of error histograms, and regression illustrations.

HOMOTOPY ANALYSIS OF TRANSIENT MAGNETO-BIO-FLUID DYNAMICS OF MICROPOLAR SQUEEZE FILM IN A POROUS MEDIUM: A MODEL FOR MAGNETO-BIO-RHEOLOGICAL LUBRICATION

2012 ◽  
Vol 12 (03) ◽  
pp. 1250051 ◽  
Author(s):  
O. ANWAR BÉG ◽  
M. M. RASHIDI ◽  
T. A. BÉG ◽  
M. ASADI
Keyword(s):  
Magnetic Field ◽  
Porous Medium ◽  
Angular Velocity ◽  
Differential Equations ◽  
Darcy Number ◽  
Squeeze Film ◽  
Squeezing Flow ◽  
Parallel Plates ◽  
Viscosity Parameter ◽  
Homotopy Analysis

The transient squeezing flow of a magneto-micropolar biofluid in a noncompressible porous medium intercalated between two parallel plates in the presence of a uniform strength transverse magnetic field is investigated. The partial differential equations describing the two-dimensional flow regime are transformed into nondimensional, nonlinear coupled ordinary differential equations for linear and angular momentum (micro-inertia). These equations are solved using the robust Homotopy Analysis Method (HAM) and also numerical shooting quadrature. Excellent correlation is achieved. The influence of magnetic field parameter (Ha) , micropolar spin gradient viscosity parameter (Γ) and unsteadiness parameter (S) on linear and angular velocity (micro-rotation) are presented graphically, for specified values of the micropolar vortex viscosity parameter (R), Darcy number (Da i.e. permeability parameter) and medium porosity parameter (ε). Increasing magnetic field (Ha) serves to decelerate both the linear and angular velocity i.e. enhances lubrication. The excellent potential of HAM in bio-lubrication flows is highlighted.

scholarly journals Semi-Analytical Resolution of a Squeezing Unsteady Nanofluid Flow Between Two Parallel Plates Using Homotopy Perturbation Method (HPM)

2021 ◽  
Vol 16 ◽  
pp. 1-13 ◽  
Author(s):  
A. El Harfouf ◽  
A. Wakif ◽  
S. Hayani Mounir
Keyword(s):  
Differential Equations ◽  
Perturbation Method ◽  
Ordinary Differential Equations ◽  
Homotopy Perturbation Method ◽  
Skin Friction Coefficient ◽  
Heat Transfer Analysis ◽  
Squeezing Flow ◽  
Nonlinear Ordinary Differential Equations ◽  
Parallel Plates ◽  
Homotopy Perturbation

In this current work, the heat transfer analysis for the unsteady squeezing flow of a viscous nanofluid between two parallel plates considering Fourier heat flux model have been explored. The partial differential equations representing flow model are reduced to nonlinear ordinary differential equations by introducing a similarity transformation. The dimensionless and nonlinear ordinary differential equations of the velocity and temperatures functions obtained are solved by employing The Homotopy Perturbation Method (HPM). The results found in this peper are verified by comparing it with the results obtained using the numerical method RK4, The results obtained are agree with this numerical solution. The effects of different parameters on the velocity and temperature profiles are examined graphically, and numerical calculations for the skin friction coefficient and local Nusselt number are tabulated. It is found an excellent agreement in the comparative study with literature results.

scholarly journals A Levenberg-Marquardt backpropagation method for unsteady squeezing flow of heat and mass transfer behaviour between parallel plates

2021 ◽  
Vol 13 (10) ◽  
pp. 168781402110408
Author(s):  
Imran Khan ◽  
Hakeem Ullah ◽  
Mehreen Fiza ◽  
Saeed Islam ◽  
Asif Zahoor Raja ◽  
...  
Keyword(s):  
Mass Transfer ◽  
Differential Equations ◽  
Heat And Mass Transfer ◽  
Chemical Reaction ◽  
Squeezing Flow ◽  
Parallel Plates ◽  
Reaction Parameter ◽  
Levenberg Marquardt ◽  
Backpropagation Method ◽  
Chemical Reaction Parameter

In this study, a new computing model by developing the strength of feed-forward neural networks with Levenberg-Marquardt Method (NN-BLMM) based backpropagation is used to find the solution of nonlinear system obtained from the governing equations of unsteady squeezing flow of Heat and Mass transfer behaviour between parallel plates. The governing partial differential equations (PDEs) for unsteady squeezing flow of Heat and Mass transfer of viscous fluid are converting into ordinary differential equations (ODEs) with the help of a similarity transformation. A dataset for the proposed NN-BLMM is generated for different scenarios of the proposed model by variation of various embedding parameters squeeze Sq, Prandtl number Pr, Eckert number Ec, Schmidt number Sc and chemical-reaction-parameter [Formula: see text]. Physical interpretation to various embedding parameters is assigned through graphs for squeeze Sq, Prandtl Pr, Eckert Ec, Schmidt Sc and chemical-reaction-parameter [Formula: see text]. The processing of NN-BLMM training (T.R), Testing (T.S) and validation (V.L) is employed for various scenarios to compare the solutions with the reference results. For the fluidic system convergence analysis based on mean square error (MSE), error histogram (E.H) and regression (R.G) plots is considered for the proposed computing infrastructures performance in term of NN-BLMM. The results based on proposed and reference results match in term of convergence up to 10-02 to 10-08 proves the validity of NN-BLMS. The Optimal Homotopy Asymptotic Method (OHAM) is also used for comparison and to validate the results of NN-BLMM.

Intelligent computing for the dynamics of entropy optimized nanofluidic system under impacts of MHD along thick surface

2021 ◽  
pp. 2150269
Author(s):  
M. Asif Zahoor Raja ◽  
M. Shoaib ◽  
Rafia Tabassum ◽  
M. Ijaz Khan ◽  
R. J. Punith Gowda ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Fluid Motion ◽  
Lewis Number ◽  
Index Formula ◽  
Brownian Diffusion ◽  
Intelligent Computing ◽  
Similarity Transformations ◽  
Computing Paradigm ◽  
Numerical Solver ◽  
Nanofluidic System

This article examines entropy production (EP) of magneto-hydrodynamics viscous fluid flow model (MHD-VFFM) subject to a variable thickness surface with heat sink/source effect by utilizing the intelligent computing paradigm via artificial Levenberg–Marquardt back propagated neural networks (ALM-BPNNs). The governing partial differential equations (PDEs) of MHD-VFFM are transformed into ODEs by applying suitable similarity transformations. The reference dataset is obtained from Adam numerical solver by the variation of Hartmann number (Ha), thickness parameter [Formula: see text], power index ([Formula: see text], thermophoresis parameter (Nt), Brinkman number (Br), Lewis number (Le) and Brownian diffusion parameter (Nb) for all scenarios of proposed ALM-BPNN. The reference data samples arbitrary selected for training/testing/validation are used to find and analyze the approximated solutions of proposed ALM-BPNNs as well as comparison with reference results. The excellent performance of ALM-BPNN is consistently endorsed by Mean Squared Error (MSE) convergence curves, regression index and error histogram analysis. Intelligent computing based investigation suggests that the rise in values of Ha declines the velocity of the fluid motion but converse trend is seen for growing values of [Formula: see text]. The rising values of Ha, Nt and Br improve the heat transfer but converse trend is seen for growing values of [Formula: see text]. The inclining values of Nt incline the mass transfer but it shows reverse behavior for escalating values of Le. The inclining values of Br incline the EP.

Numerical Simulation of Distributed Dynamic Systems using Hybrid Tools of Intelligent Computing

2013 ◽  
pp. 360-383
Author(s):  
Fethi H. Bellamine ◽  
Aymen Gdouda
Keyword(s):  
Numerical Simulation ◽  
Differential Equations ◽  
Dynamic Systems ◽  
Mixed Boundary ◽  
Simulation Models ◽  
Control Synthesis ◽  
Scaling Analysis ◽  
Intelligent Computing ◽  
Boundary Constraints ◽  
Reduction Techniques

Developing fast and accurate numerical simulation models for predicting, controlling, designing, and optimizing the behavior of distributed dynamic systems is of interest to many researchers in various fields of science and engineering. These systems are described by a set of differential equations with homogenous or mixed boundary constraints. Examples of such systems are found, for example, in many networked industrial systems. The purpose of the present work is to review techniques of hybrid soft computing along with generalized scaling analysis for the solution of a set of differential equations characterizing distributed dynamic systems. The authors also review reduction techniques. This paves the way to control synthesis of real-time robust realizable controllers.

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