Abstract
Recently, the study of weak conductor dynamics influenced by an electromagnetic (Riga) plate has garnered scholarly interest, taking into account various physical factors. Electromagnetic sensors find extensive use across engineering and industrial domains, spurring our exploration into the flow characteristics and heat–mass transfer mechanisms of a mildly conducting ionized fluid near an oscillating electromagnetic plate embedded within porous structures. The chosen flow scenario is meticulously modeled, encapsulating various physical dynamics such as radiative heat discharge, chemical interactions, Darcian porous drag effects, buoyancy forces, and velocity slippage conditions. Within this context, the Darcy model is employed to articulate drag influences in the porous domain. The resulting flow model is mathematically articulated as a set of time-dependent partial differential equations (PDEs). Leveraging the Laplace transform (LT) approach, we derive concise representations for the core variables in the model. In our study, dimensionless fluid velocity, temperature, and concentration gradients are extensively graphed, and the corresponding non-dimensional heat transfer, mass transfer, and friction rates are tabled. Key observations highlight an amplification in the velocity with an enhancement in the modified Hartmann number and diminishing with an enlargement in the Darcy number. Higher radiative heat intensities correspondingly dissipate more energy, cooling the medium further. Notably, the chemical reactions induce higher heat and mass transfer rates in the hybrid nanofluid. An artificial neural network (ANN) model is also developed based on reference datasets procured via the LT evaluation. This ANN framework exhibits commendable precision in predicting essential flow quantities, achieving an impressive 99.95% Such innovative insights from this research hold great promise for practical applications in steam generators, chemical reactors, electromagnetic sensors and gadgets, and material processing phase transitions.
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Data Availability Statement
The manuscript has associated data in a data repository. [Authors’comment: Data will be available on request].
Abbreviations
- C :
-
Concentration (mol m\(^{-3}\))
- \(C_\infty \) :
-
Constant ambient concentration (mol m\(^{-3}\))
- \(c_{\textrm{p}}\) :
-
Specific heat at constant pressure (J kg\(^{-1}\)K\(^{-1}\))
- \(C_w\) :
-
Riga plate concentration (mol m\(^{-3}\))
- D :
-
Mass diffusivity (m\(^2\)s\(^{-1}\))
- \(\text {Da}\) :
-
Darcy number
- E :
-
Modified Hartmann number
- \(e_{\lambda _0}\) :
-
Planck’s function
- \({\tilde{f}}\) :
-
Activation function
- g :
-
Acceleration due to gravity (m s\(^{-2}\))
- \({\text {Gc}}\) :
-
Solutal Grashof number
- \({\text {Gr}}\) :
-
Thermal Grashof number
- i :
-
Complex unity (\(=\sqrt{-1}\))
- \(J_0\) :
-
Characteristic electric strength by electrodes (M m\(^{-1}\))
- k :
-
Dimensionless frequency
- K :
-
Permeability of porous medium (m\(^2\))
- \({\text {Kr}}\) :
-
Chemical reaction parameter
- \({\text {Kr}}^*\) :
-
Chemical reaction coefficient (M s\(^{-1}\))
- \(K_{\lambda _0}\) :
-
Absorption coefficient (m\(^{-1}\))
- L :
-
Width of electrodes (m)
- \(m_0\) :
-
Characteristic magnetic strength by magnets (T)
- n :
-
Frequency
- \({\text {Pr}}\) :
-
Prandtl number
- \(q_r\) :
-
Radiative heat flux (kg s\(^{-3}\))
- \(Q_1\) :
-
Species generation/absoption coefficient (m\(^2\) kg\(^{-1}\))
- \({\text {Qr}}\) :
-
Species generation/absorption parameter
- R :
-
Coefficient of determination
- \({\text {Ra}}\) :
-
Radiation parameter
- s :
-
Laplace transform parameter
- S :
-
Dimensionless width of magnets and electrodes (m)
- \({\text {Sc}}\) :
-
Schmidt number
- t :
-
Time (s)
- \(t_0\) :
-
Characteristic time (s)
- T :
-
Temperature (K)
- \(T_w\) :
-
Riga plate temperature (K)
- \(T_\infty \) :
-
Constant ambient temperature (K)
- u :
-
Velocity (m s\(^{-1}\))
- \(u_0\) :
-
Reference velocity (m s\(^{-1}\))
- \(u_1\) :
-
Dimensionless velocity
- (x, y):
-
Cartesian coordinates (m)
- \(\beta \) :
-
Velocity slippage parameter
- \(\beta _0\) :
-
Velocity slippage length
- \(\beta _C\) :
-
Volumetric solutal expansion coefficient
- \(\beta _T\) :
-
Volumetric thermal expansion coefficient (K\(^{-1}\))
- \(\eta \) :
-
Dimensionless variable
- \(\theta \) :
-
Dimensionless temperature
- \(\kappa \) :
-
Thermal conductivity (W m\(^{-1}\)K\(^{-1}\))
- \(\lambda \) :
-
Thermal radiation wavelength (m)
- \(\mu \) :
-
Dynamic viscosity (kg m\(^{-1}\)s\(^{-1}\))
- \(\rho \) :
-
Density (kg m\(^{-3}\))
- \(\sigma \) :
-
Electrical conductivity (\(\Omega ^{-1}\)m\(^{-1}\))
- \(\tau \) :
-
Dimensionless time
- \(\phi \) :
-
Dimensionless concentration
- EMHD:
-
Electro-magnetodydrodynamics
- ANN:
-
Artificial neural network
- NPs:
-
Nanoparticles
- NF:
-
Nanofluid
- HNF:
-
Hybrid nanofluid
- HTR:
-
Heat transfer rate
- LT:
-
Laplace tranform
- MLP:
-
Multilayer perceptron
- MSE:
-
Mean squared error
- MTR:
-
Mass transfer rate
- SS:
-
Shear stress
- OP:
-
Oscillating plate
- SP:
-
Stationary plate
- VP:
-
Velocity profile
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Appendix A
Appendix A
The following constant expressions are utilized in the results.
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Karmakar, P., Das, S., Mahato, N. et al. Dynamics prediction using an artificial neural network for a weakly conductive ionized fluid streamed over a vibrating electromagnetic plate. Eur. Phys. J. Plus 139, 407 (2024). https://doi.org/10.1140/epjp/s13360-024-05197-w
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DOI: https://doi.org/10.1140/epjp/s13360-024-05197-w