Search

This study utilises mathematical modelling and computations to analyse the magnetohydrodynamic (MHD) effects on non-Newtonian Eyring–Powell fluid flow in an inclined non-uniform channel under long-wavelength, low Reynolds number conditions. The governing equations are solved by applying slip boundary conditions to determine the velocity, temperature, concentration, and streamline profiles. The key findings show that the magnetic parameter dampens the flow rate. The relationship between the variable viscosity, velocity, and temperature is nonlinear. The wall rigidity parameter and axial velocity are directly proportional until a threshold. Increasing inclination angles distorts streamlines. The magnetic field alters concentration contours and thermal transport. MATLAB parametric analysis explores MHD effects. This study enhances the understanding of inclined channel fluid dynamics, offering insights into variable viscosity, magnetic fields, wall properties, and impacts of inclination angles on non-Newtonian flow characteristics. This knowledge can optimise industrial MHD conduit/channel transport applications.

In this paper, the peristaltic wave propagation of a Non-Newtonian Casson liquid in a non-uniform (flexible)channel with wall properties and heat transfer is analyzed. Long wavelength and low Reynolds number approximations are considered. Analytical solution for velocity, stream function and temperature in terms of various physical parameters is obtained. The impact of yield stress, elasticity, slip and non-uniformity parameters on the peristaltic flow of Casson liquidare observed through graphs and discussed. The important outcome is that an increase in rigidity, stiffness and viscous damping force of the wall results in the enhancement of the size and number of bolus formed in the flow pattern.

An analysis is presented for mixed convection and heat transfer in a viscous electrically conducting fluid flow at an impermeable stretching vertical sheet with variable thickness. The nonlinear equations that describe the fluid flow, and heat transfer processes have been solved using the Keller-box method. A limited parametric study is undertaken to determine the sensitivity and changes in the flow and temperature fields with respect to variations in the buoyancy parameter, the temperature dependent viscosity and thermal conductivity parameters, the plate velocity power index, and the Prandtl number which are presented in graphical and tabulated formats. To validate the results, comparisons are made with the available results in the literature for some special cases and the results are found to be in good agreement. The effects of embedded parameters on the dimensionless velocity profiles and temperature are examined through graphs. The variation of Local Nusselt number is also analysed. One of the important findings of our study is that the velocity distribution at a point near the plate decreases as the wall thickness parameter increases and hence the thickness of the boundary layer becomes thinner when m < 1. Further, the effect of the magnetic field is to reduce the fluid velocity and to increase the temperature field.

Microscale heat transfer is vital for the performance of smart thermal devices like heat sinks, thermosyphons, and microheat pipes. This study introduces a biothermal pumping flow model based on a multi-membrane pumping mechanism that leverages microscale heat transfer. The model describes rhythmic contraction and relaxation of membranes, combined with electro-osmosis in Jeffery fluid flow within a vertical microchannel of finite length. Two membranes on the microchannel walls, with varying amplitudes, diameters, and phase lags, generate pressure that moves fluid in both directions through contraction and expansion cycles. The model is based on the conservation of mass and momentum, using a low Reynolds number approximation to capture microscale transport phenomena at biomedical scales. Dimensionless conservation equations are analytically solved under no-slip boundary conditions, with results computed in MATLAB for clarity. Axial velocity results are simulated and verified using the optimal homotopy analysis method. The model explores the influence of key parameters ( U H S , m e , λ , G r , β) on pressure gradient, velocity distribution, volumetric flow rates, skin friction, Nusselt number, and stream function. The findings demonstrate that pressure from membrane motion is significantly affected by thermal effects and buoyancy forces, and flow and pumping characteristics are largely determined by the fluid's rheological qualities and the geometrical features of the membrane. This study provides novel ideas for enhancing the functionality and design of smart thermal devices while also advancing microscale heat transfer technology. [ABSTRACT FROM AUTHOR]

In this study, the effects of suction and buoyancy are used to analyze the Ternary hybrid nanofluid flow on a stretching sheet through a porous media. Ternary hybrid nanofluid is (Carbon nanotubes) CNT + Graphene + Al 2 O 3 with base fluid as Water. Case-1 Buoyancy Assisting flow and Case-2 Buoyancy Opposing flow. Hybrid nanofluids have been used to speed up the heat transfer process. Nonlinear partial differential equations (PDEs) have been converted to ordinary differential equations (ODEs) using Lie group transformations. The ODE45, an algorithmic approach, has been using the aid of this built-in solver, and the resulting Ordinary differential equations were resolved. The general relationship between temperature, velocity, heat transfer rate, and shear stress on a stretchy surface is shown for a range of values of the significant factors. The temperature profiles have been rising with the impact of Da , f w . Using streamlines to examine the flow pattern of a fluid and a method of machine learning, in terms of modern language, an artificial neural network (ANN) made up of artificial neurons or nodes is known as a neural network. A neural network is a network or circuit of genetic neurons. [ABSTRACT FROM AUTHOR]

AbstractThis investigation delves into the peristaltic flow characteristics of a Newtonian fluid within a permeable channel, emphasizing the influence of nonlinear curvature and wall properties. The study employs wave trains of varying amplitudes and phases to induce an asymmetric configuration along the channel boundaries. The curvature effects are contingent upon the ratio of channel width to wavelength, and the accuracy of results is maintained up to the second order in δ. To facilitate the derivation of closed-form solutions at higher levels, a domain transformation is executed, converting a channel with a variable cross section into one with a uniform cross section. To impose constraints on parameters and achieve a singular flux in the presence of a specified pressure gradient, a distinctiveness criterion is formulated. This study investigates the interplay of inertia and curvature on pumping and trapping, examining both symmetric and asymmetric channels. The pressure difference escalates with wave number in both channel configurations. Moreover, in the absence of inertial forces and flux, the impact of curvature on the pressure difference demonstrates a direct proportionality. Conversely, an augmentation in the permeability of the porous channel correlates with a reduction in the pressure difference. An intriguing observation arises as phase differences increase: the visibility of the bolus diminishes, and the streamlines become more prominent. This establishes a favorable environment for fluid flow, enhancing our understanding of the intricate dynamics at play in peristaltic flow within permeable channels. [ABSTRACT FROM AUTHOR]

This study delves into the intricacies of the peristaltic mechanism exhibited by a non-Newtonian Ree-Eyring Nano-liquid, investigating its behavior in the presence of varying liquid properties and heat and mass transfer phenomena. The objective of the present investigation is to conduct a thorough analysis of this mechanism, taking into account the influence of wall properties and the dynamic nature of liquid properties. The Buongiorno nanofluid model is adopted to investigate heat and mass transfer phenomena, while the Ree-Eyring non-Newtonian fluid model is employed to comprehend the complex rheological characteristics of the fluid. This complex process's modeling involves utilizing long wavelengths and low Reynolds number approximations. The resulting non-linear ordinary differential equations are addressed using the homotopy perturbation technique, incorporating appropriate non-dimensional parameters. A strong correlation is observed between homotopy perturbation results and previously existing literature. The magnetic field is a critical factor in shaping the fluid flow behavior, while the interplay between variable liquid properties and nanofluid characteristics significantly affects temperature profiles. The Ree-Eyring fluid and magnetic parameters enhance the heat and mass transfer coefficient. This research advances the understanding of peristalsis in the context of Ree-Eyring Nano-liquids, holding promising implications for diverse applications in both technical and medical domains. [ABSTRACT FROM AUTHOR]

The importance of perfect surface quality in flat steel manufacturing has grown; however, traditional computer vision approaches for automatic fault spotting are inaccurate and slow, lagging behind assembly line needs. This research investigates using pre-trained Convolutional Neural Networks (CNNs) as feature extractors to reliably categorize steel surface flaws. Sample images containing defects in hotrolled steel strips were obtained from JSW Steel Ltd's Vijayanagara Works, Ballari, Karnataka, India. Features were extracted from the penultimate layers of VGG16 and DenseNet-201 models before feeding the data to supervised classifiers (Support Vector Machine, Multilayer Perceptron, Random Forest) to categorize defects. We examined performance across model configurations based on combining the pretrained CNNs and classifiers. The DenseNet-201+MLP model achieved maximum accuracy of 98.31%, outperforming VGG16 combinations and highlighting the promise of this approach. Thorough statistical evaluation via F-1 scores, precision, recall, etc. further demonstrates the value of using pre-trained CNNs with classifiers for manufacturing quality control, significantly improving steel surface defect detection speed and accuracy over traditional computer vision. This strategy shows promise for enabling reliable, large-scale automated inspection critical for industrial steel production. [ABSTRACT FROM AUTHOR]