Numerical Analysis of Electrohydrodynamic Flow in a Circular Cylindrical Conduit by Using Neuro Evolutionary Technique

This paper analyzes the mathematical model of electrohydrodynamic (EHD) fluid flow in a circular cylindrical conduit with an ion drag configuration. The phenomenon was modelled as a nonlinear differential equation. Furthermore, an application of artificial neural networks (ANNs) with a generalized n...

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Vydáno v:Energies (Basel) Ročník 14; číslo 22; s. 7774
Hlavní autoři: Khan, Naveed Ahmad, Sulaiman, Muhammad, Tavera Romero, Carlos Andrés, Alarfaj, Fawaz Khaled
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.11.2021
Témata:
Algorithms
artificial neural networks
Boundary value problems
circular cylindrical conduit
Civil engineering
Design
Electric fields
electrohydrodynamic flow
generalized normal distribution optimization
Hartmann electric number
Hybridization
Mathematical models
Neural networks
neuro soft computing
Normal distribution
Velocity
ISSN:1996-1073, 1996-1073
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Shrnutí:This paper analyzes the mathematical model of electrohydrodynamic (EHD) fluid flow in a circular cylindrical conduit with an ion drag configuration. The phenomenon was modelled as a nonlinear differential equation. Furthermore, an application of artificial neural networks (ANNs) with a generalized normal distribution optimization algorithm (GNDO) and sequential quadratic programming (SQP) were utilized to suggest approximate solutions for the velocity, displacements, and acceleration profiles of the fluid by varying the Hartmann electric number (Ha2) and the strength of nonlinearity (α). ANNs were used to model the fitness function for the governing equation in terms of mean square error (MSE), which was further optimized initially by GNDO to exploit the global search. Then SQP was implemented to complement its local convergence. Numerical solutions obtained by the design scheme were compared with RK-4, the least square method (LSM), and the orthonormal Bernstein collocation method (OBCM). Stability, convergence, and robustness of the proposed algorithm were endorsed by the statistics and analysis on results of absolute errors, mean absolute deviation (MAD), Theil’s inequality coefficient (TIC), and error in Nash Sutcliffe efficiency (ENSE).
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content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en14227774