scholarly journals Parameters and Branching Auto-Pulses in a Fluid Channel with Active Walls

Fluids ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 160
Author(s):  
Dmitry Strunin ◽  
Fatima Ahmed
Keyword(s):  
Numerical Solutions ◽  
Radial Basis Function Network ◽  
Nonlinear Partial Differential Equation ◽  
Continuity Condition ◽  
Active Motion ◽  
Network Method ◽  
Fluid Channel ◽  
Branching Point ◽  
Phenomenological Coefficients ◽  
Function Network

We present numerical solutions of the semi-phenomenological model of self-propagating fluid pulses (auto-pulses) in the channel branching into two thinner channels, which simulates branching of a hypothetical artificial artery. The model is based on the lubrication theory coupled with elasticity and has the form of a single nonlinear partial differential equation with respect to the displacement of the elastic wall as a function of the distance along the channel and time. The equation is solved numerically using the 1D integrated radial basis function network method. Using homogeneous boundary conditions on the edges of space domain and continuity condition at the branching point, we obtained and analyzed solutions in the form of auto-pulses penetrating through the branching point from the thick channel into the thin channels. We evaluated magnitudes of the phenomenological coefficients responsible for the active motion of the walls in the model.

scholarly journals Simulations of autonomous fluid pulses between active elastic walls using the 1D-IRBFN method

2018 ◽  
Vol 13 (5) ◽  
pp. 47 ◽  
Author(s):  
Fatima Z. Ahmed ◽  
Mayada G. Mohammed ◽  
Dmitry V. Strunin ◽  
Duc Ngo-Cong
Keyword(s):  
Numerical Solutions ◽  
Initial Conditions ◽  
Radial Basis Function Network ◽  
Active Motion ◽  
One Dimensional ◽  
Pulse Trains ◽  
The One ◽  
Semi Empirical ◽  
Function Network ◽  
Elastic Walls

We present numerical solutions of the semi-empirical model of self-propagating fluid pulses (auto-pulses) through the channel simulating an artificial artery. The key mechanism behind the model is the active motion of the walls in line with the earlier model of Roberts. Our model is autonomous, nonlinear and is based on the partial differential equation describing the displacement of the wall in time and along the channel. A theoretical plane configuration is adopted for the walls at rest. For solving the equation we used the One-dimensional Integrated Radial Basis Function Network (1D-IRBFN) method. We demonstrated that different initial conditions always lead to the settling of pulse trains where an individual pulse has certain speed and amplitude controlled by the governing equation. A variety of pulse solutions is obtained using homogeneous and periodic boundary conditions. The dynamics of one, two, and three pulses per period are explored. The fluid mass flux due to the pulses is calculated.

New radial basis function network method based on decision trees to predict flow variables in a curved channel

2017 ◽  
Vol 30 (9) ◽  
pp. 2771-2785 ◽  
Author(s):  
Azadeh Gholami ◽  
Hossein Bonakdari ◽  
Amir Hossein Zaji ◽  
Salma Ajeel Fenjan ◽  
Ali Akbar Akhtari
Keyword(s):  
Radial Basis Function ◽  
Decision Trees ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Curved Channel ◽  
Network Method ◽  
Radial Basis ◽  
Flow Variables ◽  
Function Network

scholarly journals A BLOCK BY BLOCK METHOD FOR SOLVING SYSTEM OF VOLTERRA INTEGRAL EQUATIONS WITH CONTINUOUS AND ABEL KERNELS

2015 ◽  
Vol 20 (6) ◽  
pp. 737-753 ◽  
Author(s):  
Roghayeh Katani ◽  
Sedaghat Shahmorad
Keyword(s):  
Integral Equations ◽  
Homotopy Perturbation Method ◽  
Radial Basis Function Network ◽  
Volterra Integral Equations ◽  
Block Method ◽  
Homotopy Perturbation ◽  
Network Method ◽  
Abel Integral Equations ◽  
The Given ◽  
Function Network

Abstract The aim of the present paper is to introduce a block by block method for solving system of nonlinear Volterra integral equations with continuous kernels and system of Abel integral equations. We prove convergence of the method and show that its convergence order is at least six. To illustrate performance of the method, numerical experiments are presented and they are compared with HPM (Homotopy Perturbation Method) and RBFN (Radial Basis Function Network) method. The given results demonstrate remarkable ability of the proposed method.

RECOGNITION OF FACIAL EXPRESSION BY IMAGE PROCESSING AND RADIAL BASIS FUNCTION NETWORK

2016 ◽  
Author(s):  
Olímpio Murilo Capeli ◽  
Euvaldo Ferreira Cabral Junior ◽  
Sadao Isotani ◽  
Antonio Roberto Pereira Leite de Albuquerque
Keyword(s):  
Image Processing ◽  
Facial Expression ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Radial Basis ◽  
Function Network
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