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This attempt numerically investigates the heat transfer in parabolic trough solar collectors due to the rotating tube for the hybrid nanofluid flow over the Riga surface with Darcy Forchheimer’s porous medium under the effect of solar radiation. The influences of viscous dissipation and Joule heating are also considered. Equations governing the fluid flow are non-dimensionalized by implementing appropriate similarity variables. The resulting non-dimensionalized ordinary differential equations are solved using the shooting technique with Adam Bashforth and Adam Moulten’s fourth-order numerical approach. The numerical outcomes for various influential physical parameters regarding the fluid velocity, temperature, Nusselt number, and entropy generation are presented in graphical form. It is observed that the thermal profile escalates with the higher values of Reynold’s number, modified magnetic field parameter, and Prandtl number. Also, the Nusselt number diminishes with augmenting values of the Eckert number, modified magnetic field parameter, Forchheimer number, and Darcy number. The optimization of heat transfer in parabolic trough collectors is essential to improve the performance of solar collectors. The concentrated solar power technology is adequate for storing radiation energy in higher amounts.

This study aims to analyze a Bayesian regularization backpropagation algorithm for micropolar ternary hybrid nanofluid flow over curved surfaces with homogeneous and heterogeneous reactions, Joule heating and viscous dissipation. The ternary hybrid nanofluid consists of nanoparticles of titanium oxide (TiO2), copper oxide (CuO), and silicon oxide (SiO2), with blood as the base fluid. The governing partial differential equations for the fluid flow are converted into ordinary differential equations using a group of self-similar transformations. The ordinary differential equations are solved using an appropriate shooting algorithm in MATLAB. The effects of physical parameters including curvature, micro-polar, radiation, magnetic, Prandtl, Eckert, Schmidt, and homogeneous and heterogeneous chemical reaction parameters are analyzed for velocity, micro rotational, temperature, and concentration profile. Physical quantities of engineering interest like heat transfer rate, mass transfer rate, skin friction coefficient, couple stress coefficient, and entropy generation are also discussed in this study. A Bayesian regularization backpropagation algorithm is also designed for the solution of the ordinary differential equations. The obtained network is analyzed using training state, performance, error histograms, model response, Error autocorrelation, and input-error correlation plots. It is observed that the entropy generation and the Bejan number increase for enhancing Brinkman and radiation parameter. Clinical researchers and biologists may use the results of this computational study to forecast endothelial cell damage and plaque deposition in curved arteries, by which the severity of these conditions can be reduced.

The aim of present study is to examine the augmentation of thermal energy transfer in hybrid nanofluid flow caused by a rotating Riga disk in the presence of thermal radiation and chemical reaction. The silver and aluminium oxide nanoparticles are used to examine the thermal effect of water base fluid. The Darcy-Forchheimer model is considered to endorse the inertial and porous media effects and makes the model more realistic from the physical scenario. Levenberg-Marquardt backpropagation algorithm is considered to analyze the hybrid nanofluid’s properties. Using scaling group transformations, the governing partial differential equations are transformed into a system of ordinary differential equations. Resulting ordinary differential equations are solved numerically by applying a suitable shooting technique by MATLAB. The results obtained for the governing differential equations have been incorporated into a dataset on which the neural network has been trained. The effects of physical parameters have been analyzed for velocity, temperature, and concentration profiles. The determination, designing, convergence, verification, and stability of the Levenberg-Marquardt backpropagation neural network algorithm are validated on the assessment of achieved accuracy through performance, fit, regression, and error histogram plots for the discussed hybrid nanofluid. It is observed that fluid velocity reduces for enhanced Darcy-Forchheimer number, magnetic parameters and boosted for enhanced modified Hartmann number. Temperature profile increases by increasing the Brownian motion and thermophoresis parameters.

Current investigation deals with the melting heat transfer for the Jeffrey hybrid-nanofluid flow in parabolic trough solar collectors through Darcy Forchheimer porous media over a variable thick vertical elongation Riga surface under the effect of solar radiation. The impacts of viscous dissipation and Joule heating are also investigated. Equations governing the Jeffrey’s hybrid nanofluid flow are higher-order non-linear partial differential equations (PDEs). These governing PDEs are transformed into the non-linear ordinary differential equations (ODEs) by introducing appropriate similarity transformations and dimensionless parameters. Runga Kutta’s fourth-order numerical scheme is implemented with the shooting technique to solve coupled higher-order ODEs. Results for velocity profile, temperature profile, drag coefficient and Nusselt number are discussed for various influential parameters. Artificial neural networking is also performed to predict Nusselt numbers in different cases and scenarios. The Artificial Neural Network gives the desired outputs with the highest possible accuracy. It is observed that the temperature profile rises with increase in Hartmann number, porosity parameter, and Forchheimer number. However thermal profile worsens with escalates in the exponential index. Radiant energy of the Sun is a renewable energy source available in considerable amounts in our living environment. A parabolic trough solar collector is an efficient solar collector that stores the concentrated incoming radiant energy from the Sun to fulfill the requirement of high temperatures in thermal energy storage systems. It has various applications in solar-powered appliances for cooking and air conditioning, solar-powered systems for treating wastewater, photovoltaic lighting, solar-powered cars, aircraft, etc.

The heat transfer rate of the MHD nanofluid blood flow through a stenosed composite artery with hematocrit-dependent viscosity and Hall effect is optimized by using the response surface methodology (RSM). An experimental design and sensitivity analysis based on RSM are employed to examine the impact of different physical parameters and how changes to these parameters affect the response factors of interest. RSM is utilized in the process of constructing the model dependencies between the output response variables, such as the skin friction coefficient and the local Nusselt number, and the independent input parameters, such as the magnetic field parameter, the Hall parameter, and the Brinkman number. These model dependencies are used to determine the relationship between the output response variables and the independent input parameters. For medical applications, the effects of the aforementioned parameters on the velocity and temperature along the radial axis have been examined and physically interpreted. Shear stress and Nusselt number are analyzed using graphs for several physical factors in addition to stenosis height. The increases in the hematocrit parameter are accompanied with a decrease in velocity profile, as it enhances the fluid viscosity that reduces the fluid motion. The sensitivity of Nux(Nusselt number) and (shear stress profile) are positive for Br, while negative for M and Be. In addition, current research may be helpful in biomedical by detecting the abnormalities in the artery with the help of the artery image, also known as magnetic resonance angiography (MRA).