Artificial neural network modeling of MHD slip-flow over a permeable stretching surface

Soomro, Feroz Ahmed; Alamir, Mahmoud A.; El‐Sapa, Shreen; Haq, Rizwan Ul; Soomro, Mukhtiar Ali

doi:10.1007/s00419-022-02168-4

Cited by 5 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The margin of deviation (MoD) values, which express the percentage divergence between the neural network outputs and the target data, were also calculated and evaluated to further the performance study of the ANN model. The equation used to calculate the MoD values is given below [42][43][44][45]:…”

Section: Formulation Of Annmentioning

confidence: 99%

Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects

2022

View full text Add to dashboard Cite

The convective heat transfer in non-Newtonian fluid flow in the presence of temperature stratification, heat generation, and heat absorption effects is debated by using artificial neural networking. The heat transfer rate is examined for the four different thermal flow regimes namely (I) thermal flow field towards a flat surface along with thermal radiations, (II) thermal flow field towards a flat surface without thermal radiations, (III) thermal flow field over a cylindrical surface with thermal radiations, and (IV) thermal flow field over a cylindrical surface without thermal radiations. For each regime, a Nusselt number is carried out to construct an artificial neural networking model. The model prediction performance is reported by using varied neuron numbers and input parameters, and the results are assessed. The ANN model is designed by using the Bayesian regularization training procedure, and a high-performing MLP network model is used. The data used in the creation of the MLP network was 80 percent for model training and 20 percent for testing. The graph shows the degree of agreement between the ANN model projected values and the goal values. We discovered that an artificial neural network model can provide high-efficiency forecasts for heat transfer rates having engineering standpoints. For both flat and cylindrical surfaces, the heat transfer normal to the surface reflects inciting nature towards the Prandtl number and heat absorption parameter, while the opposite is the case for the temperature stratification parameter and heat generation parameter. It is important to note that the magnitude of heat transfer is significantly larger for Flow Regime-IV in comparison with Flow Regimes-I, -II, and -III.

show abstract

Section: Formulation Of Annmentioning

confidence: 99%

Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects

2022

View full text Add to dashboard Cite

show abstract

“…The magnetic field plays a crucial part in controlling the properties of fluid motion, which is essential for symmetric biomedical devices, high-temperature plasma, cooling of atomic reactors, symmetric magnetohydrodynamics generators, hyperthermia, and other applications [8]. Recently, efforts have been undertaken to develop a mathematical model of biomagnetic fluids by adapting the field of ferrohydrodynamics (FHD), which investigates the mechanics of fluid motion as it is affected by strong magnetic polarization forces.…”

Section: Introductionmentioning

confidence: 99%

“…The first ferrofluid synthesis was discovered in 1965 as a result of Papell's creative research [9]. In the medical area, magnetic nanoparticles in bodily fluids such as lymph fluid and blood are employed for medication transfer at the specific afflicted site, allowing for novel cancer therapy approaches and inducing hyperthermia, and magneto-nanofluids are useful for directing the movement of particles up the bloodstream to a tumor using magnets [8,10,11]. Nasir et al [12] explored the effect of nonlinear thermal radiation on the ferrohydrodynamic flow of a SiO 2 + TiO 2 + Al 2 O 3 /H 2 O hybrid nanofluid on a stretched sheet.…”

Section: Introductionmentioning

confidence: 99%

Numerical and Machine Learning Approach for Fe3O4-Au/Blood Hybrid Nanofluid Flow in a Melting/Non-Melting Heat Transfer Surface with Entropy Generation

Jakeer,

Easwaramoorthy,

Reddy

et al. 2023

Symmetry

View full text Add to dashboard Cite

The physiological system loses thermal energy to nearby cells via the bloodstream. Such energy loss can result in sudden death, severe hypothermia, anemia, high or low blood pressure, and heart surgery. Gold and iron oxide nanoparticles are significant in cancer treatment. Thus, there is a growing interest among biomedical engineers and clinicians in the study of entropy production as a means of quantifying energy dissipation in biological systems. The present study provides a novel implementation of an intelligent numerical computing solver based on an MLP feed-forward backpropagation ANN with the Levenberg–Marquard algorithm to interpret the Cattaneo–Christov heat flux model and demonstrate the effect of entropy production and melting heat transfer on the ferrohydrodynamic flow of the Fe3O4-Au/blood Powell–Eyring hybrid nanofluid. Similarity transformation studies symmetry and simplifies PDEs to ODEs. The MATLAB program bvp4c is used to solve the nonlinear coupled ordinary differential equations. Graphs illustrate the impact of a wide range of physical factors on variables, including velocity, temperature, entropy generation, local skin friction coefficient, and heat transfer rate. The artificial neural network model engages in a process of data selection, network construction, training, and evaluation through the use of mean square error. The ferromagnetic parameter, porosity parameter, distance from origin to magnetic dipole, inertia coefficient, dimensionless Curie temperature ratio, fluid parameters, Eckert number, thermal radiation, heat source, thermal relaxation parameter, and latent heat of the fluid parameter are taken as input data, and the skin friction coefficient and heat transfer rate are taken as output data. A total of sixty data collections were used for the purpose of testing, certifying, and training the ANN model. From the results, it is found that the fluid temperature declines when the thermal relaxation parameter is improved. The latent heat of the fluid parameter impacts the entropy generation and Bejan number. There is a less significant impact on the heat transfer rate of the hybrid nanofluid over the sheet on the melting heat transfer parameter.

show abstract

Free vibration and buckling of functionally graded porous beams using analytical, finite element, and artificial neural network methods

Turan

Yaylacı

2022

Arch Appl Mech

View full text Add to dashboard Cite

Artificial neural network modeling of MHD slip-flow over a permeable stretching surface

Cited by 5 publications

References 30 publications

Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects

Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects

Numerical and Machine Learning Approach for Fe3O4-Au/Blood Hybrid Nanofluid Flow in a Melting/Non-Melting Heat Transfer Surface with Entropy Generation

Free vibration and buckling of functionally graded porous beams using analytical, finite element, and artificial neural network methods

Contact Info

Product

Resources

About