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

Naveed Ahmad Khan, Muhammad Sulaiman, Carlos Andrés Tavera Romero and Fawaz Khaled Alarfaj
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Naveed Ahmad Khan: Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan
Muhammad Sulaiman: Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan
Carlos Andrés Tavera Romero: COMBA R&D Laboratory, Faculty of Engineering, Universidad Santiago de Cali, Cali 76001, Colombia
Fawaz Khaled Alarfaj: Department of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia

Energies, 2021, vol. 14, issue 22, 1-19

Abstract: 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 ( H a 2 ) 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).

Keywords: electrohydrodynamic flow; circular cylindrical conduit; Hartmann electric number; artificial neural networks; generalized normal distribution optimization; neuro soft computing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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