Impact of fractional magnetohydrodynamic and hall current on ree-eyring fluid flow by using radial basis function method

Zahir, Hina; Mehnaz,; Gul, Javairia; Inc, Mustafa; Alqahtani, Rubayyi T.

doi:10.1016/j.aej.2024.01.019

Cited by 2 publications

(1 citation statement)

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“…RBF networks are computational models used for their simple architecture, fast training speed and the opportunity to include real data in the system. In the analysis of the magnetohydrodynamics of a Ree-Eyring fluid, the effect of the Hall current between two parallel plates was numerically solved by using radial basis functions [31].…”

Section: Related Workmentioning

confidence: 99%

Magnetic Characterization of MR Fluid by Means of Neural Networks

Kowol,

Lo Sciuto,

Brociek

et al. 2024

Electronics

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Magnetorheological and electrorheological fluids manifest a change in rheological behavior when subjected to a magnetic or electric field, respectively, such that they require electrical and magnetic characterization. In this paper, a simple and accurate mathematical model based on a small number of parameters provides the relative magnetic permeability of magnetorheological fluids as a function of the applied magnetic field. Furthermore, for the testing and magnetic characterization of magnetorheological fluids, a new metering equipment setup is implemented. Starting with the achieved experimental data, the mathematical relation μr=f(B) is represented by means of a radial basis function neural network, with neurons having a Gaussian activation function; by means of post-training pruning procedures, the trained neural network is applied using the proposed data. Therefore, the obtained mathematical relation μr=f(B) is in good agreement with the experimental data, with an approximate error of 8%.

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Section: Related Workmentioning

confidence: 99%