Fluid Devices by the Use of Electrohydrodynamic Effects of Water

Sugiyama, Hiroshi; Ogura, Hironao; Otsubo, Yasufumi

doi:10.36884/jafm.4.01.11898

Cited by 3 publications

(1 citation statement)

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“…It is the study of the motion of ionized fluid and controls transportation phenomena in fluid flow. EHD flows have a variety of applications in different fields such as electrostatic precipitators [6,7], designs of dielectric pumps [8], the flow of ions in diverter tokamak reactors [9], thermal management of microelectronics by using electro gas dynamical pumps [10], application of Taylor cone EHD jets for electrospray of liquid atomization [11], electro-fluid control of colloidal particles near electrodes [12], MEMS devices [13], inkjet devices [14], propulsion of small scale naval vessels [15], processing of nickel particle flows and their regulation in medical powder [16], etc.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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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