Hybrid Nanofluid Flow Research Articles

Thermal analysis of chemically reactive and radiative hybrid nanofluid flow by a curved stretchable surface with bioconvection

Thermal analysis of chemically reactive and radiative hybrid nanofluid flow by a curved stretchable surface with bioconvection

Designing machine learning based intelligent network for assessment of heat transfer performance of ternary hybrid nanofluid flow between a cone and a disk: Case of MLP feed forward neural network

In the current study, authors have studied the heat transfer through ternary hybrid nanofluid (THNF) between the gap of a disk and cone, where both are co-rotating with regard to the other. The authors have developed a mathematical model of THNF flow inside a gap between a cone and a disk with the effects of magnetic field, Hall effects, and radiation parameters. The unique aspect of this study is the implementation of a powerful artificial neural network (ANN) that exhibits improved robustness and accurate predictive performance for the Nusselt number at the surface of both the cone and disk. Through the use of proper similarity transformations, the model equations are transformed into a collection of nonlinear ODEs. Using MATLAB and an inbuilt bvp4c function, these equations are numerically solved. The main objective of this research is to examine the impacts of cone and disk rotation within the co-rotation system. The heat transfer rate is seen to be higher at the surface of the disk. It is followed from the analysis of the ANN model that the regression value for each case varies between 0.99574 and 1, and in many cases, it is exactly equal to 1. In addition, the mean square error (MSE) for every dataset is notably very close to zero. The effect of the angle of the conical gap is also one of the noteworthy subjects due to the significance of its effects in several engineering sector applications.

Exploiting and Enhancing the Heat Transport Rate of MoS2-Ag/EO Hybrid Nanofluid Flow via a Stretching Cylinder using Extended Yamada-Ota and Xue Models.

The current model offers valuable insights for materials science, heat exchangers, renewable energy production, nanotechnology, manufacturing, medicinal treatments, and environmental engineering. The findings of this study have the potential to improve material design, increase heat transfer efficiency across various systems, enhance energy conversion processes, and drive advancements in nanotechnology, medicinal treatments, and engineering design. The goal of the current research is to analyze the effects of thermal radiation and the volume fraction of nanoparticles in MoS2-Ag/engine oil-based hybrid nanofluid flow passing through a cylinder. After performing a substantial similarity transformation, the nonlinear dimensionless framework is recast as ODEs. The Yamada-Ota and Xue models are then applied to the dimensionless equation setup, which is numerically solved using the BVP4C approach. The resulting velocity and temperature fields, corresponding to various parameters, are examined and compared across both models. This investigation demonstrates a significant variation in heat transfer rates between the Yamada-Ota and Xue models, with the former having a larger impact. The velocity and temperature fields decrease as the magnetic field parameter increases in both nanofluids. However, as the magnetic field parameter values grow, the velocity fields in the two nanofluids behave differently. The Yamada-Ota and Xue models are used to determine the behavior of the hybrid nanofluid flow over a nonlinear extended cylinder. In all situations, the velocity and temperature fields exhibit superior decay characteristics.

Computational micropolar model of hybrid nanofluid flow across a wedge

Non-Newtonian flow from wedges is a vital problem in coatings, polymer processing etc. Hence the main aim is to investigate the convective boundary layer flow of micropolar hybrid nanofluid (MHN)from a wedge. It is assumed that the wedge surface is isothermal. The Eringen model is employed to define micropolar fluid. Nanoscale Tiwari–Das formulations are used to study the specific effects of nanoparticles and volume fractions. The dimensionless boundary value problem arises by suitable coordinate transformations as a system of nonlinearly coupled ordinary differential equations. The so-called Falkner Skan flow case is resolved. Dimensionless, transformed, coupled momentum, microrotation, boundary layer equations are resolved using the numerical scheme MATLAB bvp4c. The parameteric investigations are performed on hybrid nanofluids using water as the basis liquid and varying volume fractions from 0 to 8%. Affirmation with previous work is done. The consequence of Eringen micropolar parameter, Hartree pressure gradient parameter, nanoparticle volume fraction, Eckert number, heat absorption (sink) parameter, on the flow and physical characteristics are visualized graphically and in tables. With rising material factor and volume fraction of nanoparticle, temperature is markedly enhanced. Increases in volume fraction dampen angular velocity close to the wedge surface, while farther from the wall, the opposite effect is seen. Temperature and thermal boundary layer thickness are both greatly enhanced by increasing Eckert number. With an increase in the volume proportion of nanoparticles, velocity decreases. Heat generation raises temperatures while a heat sink lowers them. The pressure gradient parameter increases skin friction while decreasing Nusselt number. Nusselt number for hybrid nanofluid is higher than for nanofluid.

Analysis of Dynamical Assisting and Opposing Flow Characteristics of Darcy Surface-Filled Ternary Nanoparticles and Fourier Flux: Artificial Neural Network and Levenberg Method

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[Formula: see text]Graphene[Formula: see text] 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 [Formula: see text]. 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.

Magnetohydrodynamic Darcy-Forchheimer flow of non-Newtonian second-grade hybrid nanofluid bounded by double-revolving disks with variable thermal conductivity: Entropy generation analysis

The study of hybrid nanofluid flow bounded by double-revolving disks is crucial due to its significant applications in enhancing heat transfer in various industrial processes, including cooling systems, lubrication technologies, and energy storage systems. This manuscript presents an entropy generation analysis of the magnetohydrodynamic Darcy-Forchheimer flow of a non-Newtonian second-grade hybrid nanofluid between double-revolving disks with variable thermal conductivity. The hybrid nanofluid combines titanium dioxide (TiO2) and cobalt ferrite (CoFe2O4) nanoparticles in a base fluid of engine oil. Appropriate similarity transformations convert the dimensional equations governing the flow phenomena into a non-dimensional form. The resulting non-dimensionalized system of equations is then solved using the homotopy analysis method (HAM), a semi-analytical technique. The results are validated by comparing them with previously published work for a specific case of the present analysis, and they are found to be in very good agreement. A comprehensive parametric analysis is conducted to understand the behavior of flow and heat transfer to various physical parameters involved in the study. It was found that there is an enhanced heat transfer rate at both disks when the Reynolds number and titanium dioxide nanoparticle concentration are higher. It was also found that Bejan number declined with increasing Brinkman number.

Heat transfer evaluation of (CaTe+SiC) hybrid nanofluid flow based RT42 HC (Rubitherm) phase change material: Cooling photovoltaic panels application

This paper inspects the combined effects of heat and mass transfers in a hybridized Williamson viscous nanofluid composed of cadmium telluride (CdTe) and silicon carbide (SiC) nanoparticles in RT42 (Rubitherm) as base fluid in the existence of heat source and thermal radiative aspects. Knowing that the base fluid RT42 is a phase change material (PCM), it is also considered that the surface on which the nanofluid flows is an expandable surface with varying thickness. The influence of chemical reactions process and viscous dissipation on the flow and temperature of the hybridized nanofluid is examined. The parameters’ influences on the problem are evaluated after setting appropriate similarity transformations to transform the collection of major partial differential equations (PDEs) into nondimensional ordinary differential equations (ODEs). The study concludes that the presence of hybridized nanoparticles of CdTe and SiC reduces the horizontal and vertical surface frictional forces of the hybrid nanofluid. The integration of nanoparticles in RT42 enhances heat transfer rates and reduces mass transfer. The thermal radiative variable declines the heat transfer of hybridized nanofluid. The results indicate that altering the variable parameter of surface thickness reduces frictional forces in both directions.

An investigation into the impact of thermal radiation and chemical reactions on the flow through porous media of a Casson hybrid nanofluid including unstable mixed convection with stretched sheet in the presence of thermophoresis and Brownian motion

Abstract This article investigates the unsteady mixed convention two-dimensional flow of magnetohydrodynamic Casson hybrid nanofluids (alumina oxide and titanium oxide nanoparticles with base fluid water) flow through porous media over a linearly stretched sheet. We analyzed the heat and mass transfer in mixed convection, thermal radiation, variable thermal conductivity, variable mass diffusivity, and chemical reaction in the presence of thermophoresis and Brownian motion. A system of partial differential equations is reduced to a solvable system of ordinary differential equations by applying a suitable similarity transformation. We used the Runga–Kutta method along with the shooting procedure to solve the flow, heat, and mass transfer equations along with boundary conditions. The results obtained from MATLAB codes are compared with previously published results of the same type in a limiting case. The results of the velocity, temperature, and concentration profile of the hybrid nanofluid for varying different flow parameters are obtained in the form of graphs, while the rate of shear stress, rate of heat, and mass transfer are expressed in tables. We noticed that velocity and temperature diminish as an unsteady parameter increases; however, the reverse trend was observed in the nanoparticle concentration profile. With an increase in the thermal radiation parameter, the resultant velocity and temperature profile improves, while the concentration of nanoparticle profiles decreases. The velocity and temperature increase with higher Brownian motion, while the velocity increases and temperature decreases with higher thermophoresis.

Computational assessment of flow and heat transfer characteristics through Cu + Al2O3 + TiO2 ternary hybrid nanomaterial host-water with shape effect

ABSTRACT Solar energy stands out as a vital source of thermal power derived from the sun. Its capacity finds applications across various technological domains, including photovoltaic panels, solar vehicles, solar lighting infrastructure, and solar water pumps. The contemporary landscape emphasizes the integration of solar energy in industries, notably exemplified by the prevalence of solar-powered charging stations. This article proposes a new perspective on heat transport analysis, incorporating the shape effect on the ternary hybrid nanofluid model along with considerations of thermal radiation, Cattaneo–Christov heat flux factor, heat source, and sink. For the evaluation, three differently shaped ternary hybrid nanoparticles are taken into account including platelet-shaped Cu , cylindrical-shaped A l 2 O 3 , and disc-shaped Ti O 2 . Through boundary layer approximations, the contributions of radiation, heat source, and sink variables are implemented into the regulating equations. The redesigned ordinary differential equations are numerically solved employing the NDSolve method after treating similarity variables. In the case of physical quantities, multiple linear regression is introduced to discover solutions for the flow elements. Augmentation in the momentum of dissimilarly shaped Cu , A l 2 O 3 and Ti O 2 nanoparticles enhance the velocity of ternary hybrid nanofluid flow more than that of spherical-shaped nanoparticles. The significant heat transfer is observed in mono, hybrid and ternary nanofluids with the existence of radiation and heat source phenomena. The multiple linear regression precisely forecasted the physical quantities with the error 10 − 3 . An application of a ternary hybrid nanofluid in a solar powered charging station can efficiently perform and successfully address an energy crisis by exhibiting an acceptable amount of power. This capability contributes to the reliable charging of electronic devices. Also, the machine learning technique can reliably and accurately perform the heat transfer analysis.

Modeling of artificial neural network to analyze heat and mass transfer of ternary hybrid nanofluid between two parallel plates with inclined magnetic field

The applications of the artificial neural network (ANN) have become the focus of interest of researchers due to their convenience for accurate modeling, simulation, and efficiency of evaluation. The primary objective of this study is to investigate the characteristics of heat and mass transfer of the ternary hybrid nanofluid flow (THNF), which is squeezed between two parallel plates, using ANN. The plate which lies on x-axis is stretching while the upper plate (UP) can move in upward and downward direction. An inclined magnetic field (MF) is also applied to the lower plate (LP). A system of partial differential equations of flow, energy, and mass transfer is used to simulate the THNF, which is then condensed using similarity substitution to a collection of ordinary differential equations (ODEs). Using the differential transform method (DTM), the resultant nonlinear ODEs in dimensionless form are further solved. The influence of the different varying physical parameters on velocity, temperature, and concentration is graphically presented and discussed. It becomes apparent that the velocity, heat, and mass transfer in squeezing flows are significantly impacted by the inclination angle of the applied MF. To demonstrate the validity of the study, the numerical findings of the Nusselt and the Sherwood numbers are provided. For the accuracy of the used approach, DTM results are compared with results from the numerical approach. The novelty of the current work is to train the neural network with the Levenberg–Marquardt algorithm in the model. To get the estimated output of the model, different scenarios are set for training, testing, and validation. The analysis is done by mean square error (MSE), histogram, fitness curve, and regression (RG). The created ANN model is shown to be reliable due to its exceptional accuracy throughout the training, validation, and testing stages.

Thermal Transportation in Heat Generating and Chemically Reacting MHD Maxwell Hybrid Nanofluid Flow Past Inclined Stretching Porous Sheet in Porous Medium with Solar Radiation Effects

The emerging concept of hybrid nanofluids has grabbed the attention of researchers and scientists due to improved thermal performance because of their remarkable thermal conductivities. These fluids have enormous applications in engineering and industrial sectors. Therefore, the present research study examines thermal and mass transportation in hybrid nanofluid past an inclined linearly stretching sheet using the Maxwell fluid model. In the current problem, the hybrid nanofluid is engineered by suspending a mixture of aluminum oxide Al2O3 and copper Cu nanoparticles in ethylene glycol. The fluid flow is generated due to the linear stretching of the sheet and the sheet is kept inclined at the angle ζ=π/6 embedded in porous medium. The current proposed model also includes the Lorentz force, solar radiation, heat generation, linear chemical reactions, and permeability of the plate effects. Here, in the current simulation, the cylindrical shape of the nanoparticles is considered, as this shape has proven to be excellent for the thermal performance of the nanomaterials. The governing equations transformed into ordinary differential equations are solved using MATLAB bvp4c solver. The velocity field declines with increasing magnetic field parameter, Maxwell fluid parameter, volume fractions of nanoparticles, and porosity parameter but increases with growing suction parameter. The temperature drops with increasing magnetic field force and suction parameter values but increases with increasing radiation parameter and volume fraction values. The concentration profile increases with increasing magnetic field parameters, porosity parameters, and volume fractions but reduces with increasing chemical reaction parameters and suction parameters. It has been noted that the purpose of the inclusion of thermal radiation is to augment the temperature that is serving the purpose in the current work. The addition of Lorentz force slows down the speed of the fluid and raises the boundary layer thickness, which is visible in the current study. It has been concluded that, when heat generation parameters increase, the temperature field increases correspondingly for both nanofluids and hybrid nanofluids. The increase in the volume fraction of the nanoparticles is used to enhance the thermal performance of the hybrid nanofluid, which is evident in the current results. The current results are validated by comparing them with published ones.

Mixed convection hybrid nanofluid flow over a rotating cone in a rotating fluid environment with interfacial nanolayer effect

This study explores the dynamics of a mixed magneto-hybrid nanofluid (HNF) flowing over a rotating cone within a rotational fluid flow, focusing on the effects of joule heating and thermal radiation. Employing water as a base fluid, enhanced with nanoparticles like molybdenum disulfide (MoS2) and graphene oxide (GO), we examine the critical role of liquid–solid interfacial layers on thermal integrity and boundary conditions. The governing model integrates joule heating, thermal radiation, mixed convection, and magnetic effects to fully represent the complexities of the flow dynamics. A system of partial differential equations (PDEs), simplified under boundary layer assumptions, is transformed into ordinary differential equations (ODEs) through similarity transformations. These ODEs are then solved using the Homotopy Analysis Method (HAM) in Mathematica. Notably, our results demonstrate an improvement in heat transfer rates under combined magnetic and rotational influences, compared to conventional fluids. This enhanced cooling efficiency is critical for applications like aeronautical gas turbines and power production turbines, where higher thermal regulation directly correlates with improved performance, durability, and operational efficiency. Moreover, the study reveals a significant sensitivity of the thermal boundary layer to variations in the Prandtl number, indicating that higher Prandtl numbers lead to a lower temperature profile. This finding is vital for optimizing heat transfer processes, particularly in engineering applications where precise control over thermal properties is required. Furthermore, the study provides detailed insights into the friction factors for azimuthal and tangential directions, alongside local Nusselt numbers, offering substantial agreement with prior research. These findings contribute significantly to the design and optimization of modern cooling systems, emphasizing the potential of HNFs in high-temperature environments.

On the thermal performance of radiative stagnation-point hybrid nanofluid flow across a wedge with heat source/sink effects and sensitivity analysis

The present article aims to examine the thermal performance and the sensitivity analysis of a GO−TiO2/water hybrid nanofluid in the presence of different nanoparticle shapes along with heat absorption and thermal radiation effects over a wedge geometry. Analyzing the effects of heat generation and radiation effects is one of the key studies conducted by researchers in various nanofluid flows over some required geometries. However, a combined study of these effects has yet to be studied over a moving wedge, and that combination defines the novelty of the work. Similarity transformations are implemented to the governing equations to obtain the final set of nondimensional equations, which are solved using the bvp4c code in MATLAB. The results obtained were in close agreement with the published results. The Nusselt number decreased with an increase in the heat source parameter Q, and it increased with an increasing Hartree pressure gradient β and thermal radiation parameter Rd. The sensitivity is statistically analyzed for the variations in radiation effect, heat source, and pressure gradient parameters on the Nusselt number. The high values for R2=99.99% and Adj R2=99.96% validate the ANOVA results obtained using a Box–Behnken design (BBD) model in the response surface methodology (RSM) with 14 degrees of freedom. The input parameters Rd and β show positive sensitivity, while Q shows negative sensitivity toward the skin friction. The Nusselt number proves to be most sensitive toward the pressure gradient parameter. TiO2, graphene (Gr), and the derivative forms of graphene, are gaining much importance due to their wide applications in the oil and petroleum industries. Thus, this study contributes to lubrication purposes, emulsion stabilizers, oxalic acid removal, anti-corrosive properties, etc.

Quantitative analysis of the electromagnetic hybrid nanofluid flow within the gap of two tubes using deep learning neural networks

Purpose(1) A mathematical model for the Hybrid nanofluids flow is used as carriers for delivering drugs. (2) The flow conditions are controlled to enable drug-loaded nanofluids to flow through the smaller gap between the two tubes. (3) Hybrid nanofluids (HNFs) made from silver (Ag) and titanium dioxide (TiO2) nanoparticles are analyzed for applications of drug delivery. (Ag) and (TiO2) (NPs) are suitable candidates for cancer treatment due to their excellent biocompatibility, high photoactivity, and low toxicity. (4) The new strategy of artificial neural networks (ANN) is used which is machine-based and more prominent in validation, and comparison with other techniques.Design/methodology/approachThe two Tubes are settled in such a manner that the gap between them is uniform. The Control Volume Finite Element Method; Rk-4 and Artificial Neural Network (ANN).Findings(1) From the obtained results it is observed that the dispersion and distribution of drug-loaded nanoparticles within the body will be improved by the convective motion caused by hybrid nanofluids. The effectiveness and uniformity of drug delivery to target tissues or organs is improved based on the uniform flow and uniform gap. (2) The targeting efficiency of nanofluids is further improved with the addition of the magnetic field. (3) The size of the cylinders, and flow rate, are considered uniform to optimize the drug delivery.Research limitations/implications(1)The flow phenomena is considered laminar, one can use the same idea through a turbulent flow case. (2) The gap is considered uniform and will be interesting if someone extends the idea as non-uniform.Practical implications(1) To deliver drugs to the targeted area, a suitable mathematical model is required. (2) The analysis of hybrid nanofluids (HNFs) derived from silver (Ag) and titanium dioxide (TiO2) nanoparticles is conducted for the purpose of drug delivery. The biocompatibility, high photoactivity, and low toxicity of (Ag) and (TiO2) (NPs) make them ideal candidates for cancer treatment. (3) Machine-based artificial neural networks (ANN) have a new strategy that is more prominent in validation compared to other techniques.Social implicationsThe drug delivery model is a useful strategy for new researchers. (1) They can extend this idea using a non-uniform gap. (2) The flow is considered uniform, the new researchers can extend the idea using a turbulent case. (3) Other hybrid nanofluids flow, in the same model for other industrial usages are possible.Originality/valueAll the obtained results are new. The experimental thermophysical results are used from the existing literature and references are provided.

Cattaneo-Christov heat flux effect on Darcy-Forchheimer dual-phase dusty shear-thickening carreau hybrid nanofluid flow along a stretched vertical cylinder

This research ventures into the convoluted behavior of dusty Copper - Titanium Dioxide and Water shear-thickening Carreau hybrid nanofluid flow within a porous vertical cylinder, incorporating the effects of viscous dissipation and the Cattaneo-Christov heat flux model. By applying the Runge-Kutta-Fehlberg fourth-order method alongside the shooting technique, we address the nonlinear coupled ordinary differential equations governing this flow. The study systematically investigates the impact of various dimensionless parameters on critical flow characteristics such as velocity and thermal profiles, skin friction coefficient, and thermal transfer rate. Our findings indicate that increasing local Weissenberg numbers and power law indices for the Carreau fluid model enhances velocity distribution while reducing the temperature profile. Higher Weissenberg numbers correlate with a decreased skin friction coefficient and an elevated Nusselt number. Initially, the curvature parameter shows a gradual increase in the fluid phase of the velocity profile, but it experiences a significant surge near the boundary. The heat transfer rate diminishes with rising values of the porosity and Forchheimer parameters. Notably, the study reveals the remarkable benefits of the dusty Carreau hybrid nanofluid, demonstrating a 39% increase in absolute shear stress compared to the dusty Carreau nanofluid, highlighting its potential for improved momentum transfer. Additionally, a 20% enhancement in heat transfer rate underscores its suitability for high-performance engineering and heat transfer systems. These results offer promising advancements for industries requiring efficient heat and momentum transfer, contributing to superior performance and energy efficiency.

Computational modelling of micropolar blood-based magnetised hybrid nanofluid flow over a porous curved surface in the presence of artificial bacteria.

This work provides a brief comparative analysis of the influence of heat creation on micropolar blood-based unsteady magnetised hybrid nanofluid flow over a curved surface. The Powell-Eyring fluid model was applied for modelling purposes, and this work accounted for the impacts of both viscous dissipation and Joule heating. By investigating the behaviours of Ag and TiO2 nanoparticles dispersed in blood, we aimed to understand the intricate phenomenon of hybridisation. A mathematical framework was created in accordance with the fundamental flow assumptions to build the model. Then, the model was made dimensionless using similarity transformations. The problem of a dimensionless system was then effectively addressed using the homotopy analysis technique. A cylindrical surface was used to calculate the flow quantities, and the outcomes were visualised using graphs and tables. Additionally, a study was conducted to evaluate skin friction and heat transfer in relation to blood flow dynamics; heat transmission was enhanced to raise the Biot number values. According to the findings of this study, increasing the values of the unstable parameters results in increase of the blood velocity profile.

Numerical assessment of viscoelastic tetra hybrid nanofluid flow across a stretchable rotatory disk under the Soret and Dufour aspects

PurposeFluid flows through rotatory disks are encountered in industrial and practical engineering processes, such as computer storage devices, gas turbine rotators, rotating machinery, air cleaning machines, etc. The primary purpose of this research is to examine the combined aspects of variable electrical conductivity, thermal radiation, Soret and Dufour effects on a magnetohydrodynamic Maxwell single-walled carbon nanotubes–graphene oxide–multi-walled carbon nanotubes–copper (SWCNT–GO–MWCNT–Cu)/sodium alginate tetra-hybrid nanofluid flow through a stretchable rotatory disk.Design/methodology/approachThe modeled administrative equations of the present flow problem are converted to a non-dimensional system of ordinary differential equations by applying suitable similarity conversion and then solved numerically by implementing the bvp4c method. The impressions of noteworthy dimensionless parameters on velocity, temperature, concentration distributions, Nusselt number, skin friction and Sherwood number are reported via graphs and tables.FindingsThe authors figured out that the developed values of the rotation parameter diminish the temperature but enhance both the radial and angular velocities. Further, the mass and heat transmission rates are better for tetra-hybrid nanofluids than for ternary and hybrid nanofluids.Originality/valueThe present study emphasizes a special type of fluid called the tetra-hybrid nanofluid. The existing literature has not discussed the Maxwell tetra hybrid nanofluid flow through a stretchable rotatory disk with variable electrical conductivity. Besides, the novel aspects of magnetohydrodynamics, thermal radiation, Soret and Dufour effects are also incorporated into the present flow problem.

Numerical study of electroconductive non-Newtonian hybrid nanofluid flow from a stretching rotating disk with a Cattaneo–Christov heat flux model

Motivated by emerging technologies in smart functional nanopolymeric coating systems in pharmaceutical and robotics applications, the convective heat transfer characteristics in swirl coating with a magnetic hybrid power-law rheological nanofluid polymer on a radially stretching rotating disk are examined theoretically. An axial magnetic field is imposed. Copper (Cu) and aluminium alloy (AA7075) nanoparticles with sodium alginate (C6H9NaO7) as a base fluid are considered, and a hybrid volume fraction model is deployed. A non-Fourier (Cattaneo–Christov) heat flux model is deployed to inspire thermal relaxation impacts absent in the classical Fourier heat conduction formulation. Through similarity proxies and the Von Karman transformations, the partial derivative systems of equations with associated boundary constraints are reduced to a system of derivative boundary value problems, which is solved numerically via the weighted residual method with an integral Galerkin scheme, executed in the MAPLE symbolic software. Validation with special cases from articles is captured. The computations revealed that radial, tangential and axial velocity are depleted, whereas temperature is enhanced with increasing magnetic parameter ( M). High thermal transfers are obtained for the hybrid nanofluid relative to the AA7075 alloy nanofluid. A strong increment in temperature is also produced with a greater thermal relaxation effect.

Non-similar simulations of Ellis model based ternary hybrid nanofluid flow through porous media

The investigation of nanofluid flow through porous media is an emerging area of research in the optimization of thermal processing and numerous thermodynamic processes. The principal objective of these thermal applications is to examine the movement of ternary hybrid nanofluid across a stretched sheet oriented vertically. The flow being examined is represented mathematically in order to incorporate the influences of magnetic fields, thermal radiation, and viscous dissipation. The ternary hybrid nanofluid thermal performance can be enhanced and surpassed by adding nanoparticles to the base fluid. The non-Newtonian Ellis model takes into account the fluid movement through the porous media. Ferrous oxide ( Fe 3 O 4 ) , Zinc oxide ( ZnO ) , and Molybdenum disulfide ( Mo S 2 ) are regarded as nanoparticles, with ethylene glycol ( EG ) serving as the base fluid. To convert the governing equations into a dimensionless system, suitable non-similar transformations have been established. The local non-similarity (LNS) approach is used with the MATLAB built-in tool bvp4c up to the second truncation level. The physical impacts of dimensionless factors on the temperature and velocity profiles of the studied nanofluids are thoroughly investigated. When the magnetic and porosity parameters change, the velocity profile becomes smaller. The heat transmission rate declines when the assessments of the magnetic number and Eckert number increase. The skin friction coefficient experiences an increase as the estimates of magnetic properties and nanoparticle value rise. The current study holds various practical implications, encompassing Solar Thermal Energy Storage, Waste Heat Recovery, Advanced Cooling Technologies, Enhanced Geothermal Systems, and Enhanced Oil Recovery.

Newtonian Heating in Magnetohydrodynamic (MHD) Hybrid Nanofluid Flow Near the Stagnation Point over Nonlinear Stretching and Shrinking Sheet

Hybrid nanofluids have demonstrated superior heat transfer performance in numerous applications. However, there remains a need for further research to broaden the scope of their potential applications. The unique behavior of hybrid nanofluids, driven by their potential for improved thermal efficiency, continues to be a focal point of investigation and exploration. This study focuses on the effects of Newtonian heating in MHD hybrid nanofluid near the stagnation point over a nonlinear stretching/shrinking sheet. The Tiwari and Das model, which is a single-phase model, was used to develop the mathematical model. The base fluid and the nanoparticles are assumed to be in thermal equilibrium; hence there is no thermal slip between them. The combination of metal (Cu) and metal oxide (Al2O3) nanoparticles with water (H2O) as the base fluid is used for the analysis. Furthermore, the governing equations are transformed using a similarity transformation technique into similarity equations, which are then solved numerically using a bvp4c function in MATLAB software. Numerical comparison with the published literature is conducted to validate the numerical results, and excellent agreement is found. The impact of physical parameters on the velocity, temperature, skin friction, and local Nusselt number is graphically deliberated. The outcomes suggest that non-unique solutions are found in a specific range of the shrinking parameter. It is also observed that increasing Cu (copper) nanoparticle volume fractions cause an increase in the skin friction coefficient and the local Nusselt number. The presence of magnetic and nonlinear parameters widens the range of solutions to exist while different observation is noticed with an increase in the volume fraction of Cu. Other than that, it has been shown that the Nusselt number increases as the magnetic parameter increases. Lastly, the rise of Newtonian heating contributes to an increase in the temperature profile. This investigation is crucial for understanding the thermal behavior of Cu-Al2O3/ H2O under the influence of physical factors like a magnetic field and Newtonian heating.