Numerical study of thermal and solutal advancements in ZnO-SAE50 nanolubricant flow past a convergent/divergent channel with the effects of thermophoretic particle deposition.

The present research focuses on the significance of thermophoretic particle deposition on a ZnO-SAE50 nanolubricant flow in a stretchable/shrinkable convergent/divergent channel in the presence of an applied magnetic field and nonlinear heat radiation. A parameter in the governing differential equations and wall boundary conditions defines the physical mechanism of the model. The Galerkin finite element method, combined with similarity transformation, is adopted to solve the governing equations. The Levenberg-Marquardt backpropagating algorithm of an artificial neural network model forecasts heat and mass transfer properties. The results reveal that by stretching/shrinking the walls enough, the classical flow and heat properties are significantly affected. The stretching of the convergent or divergent channel is observed to increase the velocity profiles, whilst shrinking results in backflow regions. In terms of the temperature field, stretching causes more heat to be produced in the flow; nevertheless, the thermal layer is decreased and cooling is attained by channel shrinkage, which may have important technical implications.

Impact of solid-fluid interfacial layer and nanoparticle diameter on Maxwell nanofluid flow subjected to variable thermal conductivity and uniform magnetic field.

The utilization of Maxwell fluid with nanoparticle suspension exhibits promising prospects in enhancing the efficacy of energy conversion and storage mechanisms. They have the potential to be utilized in sophisticated cooling systems for power generation facilities, thereby augmenting the overall energy efficacy. Keeping this in mind, the current research examines the Maxwell nanofluid flow over a rotating disk with the impact of a heat source/sink. The present study centers on the examination of flow characteristics in the existence of a uniform magnetic field. The conversion of governing equations into ordinary differential equations is achieved using appropriate similarity variables. To derive the Nusselt number (Nu) and skin friction (SF) model related to the flow and temperature parameters, the suggested back-propagation artificial neural networking (ANN) technique is used. The Runge-Kutta-Fehlberg fourth-fifth order (RKF-45) method is used to solve the reduced equations and produce the necessary data to create the Nu and SF model. Both the Nu and SF models require 1000 data for training the network, respectively. Graphs are utilized to communicate numerical outcomes. The results concluded that the upsurge in magnetic parameter drops the velocity profile but advances the heat transport. Rise in the thermal conductivity parameter, increases the heat transport.

Heat transfer analysis in magnetohydrodynamic nanofluid flow induced by a rotating rough disk with non-Fourier heat flux: aspects of modified Maxwell-Bruggeman and Krieger-Dougherty models.

Non-Newtonian fluids have unique heat transfer properties compared to Newtonian fluids. The present study examines the flow of a Maxwell nanofluid across a rotating rough disk under the effect of a magnetic field. Furthermore, the Cattaneo-Christov heat flux model is adopted to explore heat transport features. In addition, a comparison of fluid flow without and with aggregation is performed. Using similarity variables, the governing partial differential equations are transformed into a system of ordinary differential equations, and this system is then solved by employing the Runge-Kutta Fehlberg fourth-fifth order method to obtain the numerical solution. Graphical depictions are used to examine the notable effects of various parameters on velocity and thermal profiles. The results reveal that an increase in the value of Deborah number decreases the velocity profile. An increase in the thermal relaxation time parameter decreases the thermal profile. An artificial neural network is employed to calculate the rate of heat transfer and surface drag force. The R values for skin friction and Nusselt number were computed. The results demonstrate that artificial neural networks accurately predicted skin friction and Nusselt number values.