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Wire coating is a commercial method to insulate wires for mechanical intensity and environmental protection. In this experimental study, the technique of computational intelligence is used for nonlinear wire coating analysis by soaking the wires in Oldroyd 8-constant fluid under a constant pressure gradient with the help of feed forward artificial neural networks (ANNs). The system of partial differential equations generated for the process of wire coating is transformed into a nonlinear dimensionless ordinary differential equation. One of the salient features of ANNs is the mathematical modeling of transformed equations by exploring the unsupervised error. A new scheme based on inverse multiquadric neural networks (IMQNNs) is used with a hybridization process of well-known genetic algorithms (GAs) and sequential quadratic programming (SQP) to obtain expeditious convergence, i.e., IMQNNs-GA-SQP. The applicability of the problem is investigated by altering the values of the dilatant constant, pressure gradient, shear stress, and pseudo-plastic constant, the outcome of which is in the form of varying polymer coating thickness. Comparison of highly accurate results in the shape of error analysis based on absolute errors of obtained results with those of the reference solution, calculated using the Adams numerical technique using MATHEMATICA software, statistical analysis such as root-mean-square error, Theil's coefficient for inequality, E-R2 (error function based on the coefficient of determination), E-VAF (variance accounted for), E-NSE (Nash–Sutcliffe efficiency), mean absolute error, box plot analyses, and the cumulative distributive function through histogram analyses, is also carried out in this research, which guarantees the effectiveness of the used scheme. [ABSTRACT FROM AUTHOR]

In this exploration, we decided to investigate the significance of prescribed thermal conditions on unsteady 3D dynamics of water-based radiative hybrid nanofluid with the impact of cylindrical-shaped nanosized particles (alumina ( Al 2 O 3 ) and titania (Ti O 2 )). For physical relevancy, the impact of the Lorentz force is also included. The combination of suitable variables has been used to transform the transport equations into the system of ordinary differential equations and then numerically solved via the Keller-Box approach. Graphical illustrations have been used to predict the impact of the involved parameters on the thermal setup. Convergence analysis is presented via the grid independence approach. Skin frictions and local Nusselt numbers against various choices of involved parameters are plotted and arranged in tabular forms. It is observed through the present investigation that temperature distribution is increased with the higher choices of radiation parameter 0.0 ≤ R d ≤ 2.0 and decreased with the improvement in the choices of temperature maintaining indices (i.e., − 2.0 ≤ r , s ≤ 2.0). Moreover, the thermophysical properties except specific heat for hybrid nanofluid are improved with the involvement of cylindrical-shaped nanoparticles. The temperature of the hybrid nanofluid is observed to be higher for variable thermal conditions as compared to uniform thermal conditions. Outfalls for a limited version of the report have been compared with a previous published paper. [ABSTRACT FROM AUTHOR]

In this research-based study, the electro-magneto impacts are numerically observed during Darcy-Forchheimer (DF) viscous fluid flow having nonlinear form of thermal radiation. This two-dimensional fluid flow over a stretchable sheet is exposed to joule heating, thermal convection and viscous dissipation. The entire computational work is successfully performed using reliable neuro computing technique (NCT) through a viable combination of genetic algorithms (GAs) a well-known global solver and a famous local solver named sequential quadratic programming (SQP) i-e NCT-GA-SQP. Partial differential equations (PDEs) that express the DF viscous fluid flow are successfully converted into a third order nonlinear system of ordinary differential equations (ODEs) through manipulation of similarity variables. Numerical illustrations with strong accuracy and effective graphical representations are adopted to visualize the velocity profile, thermal distribution and concentration of fluid flow due to the variational effects of various physical parameters namely the electric field parameter (E), magnetic parameter (M), the porosity parameter (λ), the radiation parameter (Rd), the Prandtl number (Pr), the chemical reaction parameter (γ) and the Schmidt number (Sc). The reference solutions attained through well-known Adams method are used to validate the numerical results gathered through complexity analysis and different statistical operators and are closely matched which guarantees the reliability of the proposed solver. [ABSTRACT FROM AUTHOR]

The main theme of the present communication is to analyze the flow of micropolar Casson fluid with the influence of dissipation effects and inclined magnetic field upon a stretching surface by incorporating the Lobatto IIIA based numerical solver. In the presented study, the Navier–Stokes theory is exploited to model the governing fluidic system and similarity variables are used to reduce the system of governing partial differential equations into the equivalent ordinary differential equation-based model. A graphical and tabular illustration of the result of the system using the Lobatto IIIA method for the study of influential effects of various sundry parameters against the velocity profile, temperature, and microrotation are presented with brief interpretations. Results show that the increment of the inclination angle diminishes the velocity and upsurges the temperature, while, the Eckert number reduces the rate of heat transfer and shear stress, however, on the other hand, enhances the temperature and velocity profiles. [ABSTRACT FROM AUTHOR]