Search Results (142)

Based on admirable porous media performance and the popularity of additive manufacturing technology, gradient porous media are progressively being applied in increasing fields. In this study, convection heat transfer within a square vented cavity, partially occupied by two copper metal foam layers of $10$ and $20$ PPI saturated with nanofluid, was assessed numerically. The left wall was heated uniformly and non-uniformly by applying multi-frequency spatial heating following a sinusoidal function. Governing equations, including continuity, the Darcy–Brinkmann–Forchheimer model, and local thermal non-equilibrium energy equations, were adopted and solved by employing the finite volume method. The influences of relevant parameters, including nanoparticle concentrations $\left(0\%\le \phi \le 10\%\right)$, Reynolds number ($1\le Re\le 100$), inlet and outlet port aspect ratios $\left(0.1\le D/H\le 0.4\right)$, three outlet vent opening locations ($\left(S_o=0\right)$ left, ($S_o=\left(H/2-D/2\right)$) middle, and ($S_o=\left(H-D\right)$) right), sinusoidal offset temperature (${\theta }_o=0.5$, $1$), frequency ($f=1, 3, 5$), and amplitude ($A=0-1$), were examined. The results demonstrate that flow and heat transfer fields are impacted mainly by these parameters. Streamlines are more intensified at the upper-left corner when the outlet opening vent is shifted towards the right-corner upper wall. Fluid- and solid-phase Nusselt number increases $Re$, $D/H$, ${\theta }_o$, $A$, and $f$ are raised, specifically when $A\ge 0.3$. The Nusselt number remains constant when the frequency is raised from $3$ to $5$, definitely when $D/H\ge 0.25$. In uniform and non-uniform heating cases, the Nusselt number of both phases remains constant as the outlet port is shifted right for $Re\le 10$ and slightly for higher $Re$ as the outlet vent location is translated from left to right. Full article

Highlighting the importance of artificial intelligence and machine learning approaches in engineering and fluid mechanics problems, especially in heat transfer applications is main goal of the presented article. With the advancement in Artificial Intelligence (AI) and Machine Learning (ML) techniques, the computational efficiency and accuracy of numerical results are enhanced. The theme of the study is to use machine learning techniques to examine the thermal analysis of MHD boundary layer flow of Eyring-Powell Hybrid Nanofluid (EPHNFs) passing a horizontal cylinder embedded in a porous medium with heat source/sink and viscous dissipation effects. The considered base fluid is water ($H_{2}O$) and hybrid nanoparticles titanium oxide (${TiO}_2$) and Copper oxide ($CuO$). The governing flow equations are nonlinear PDEs. Non-similar system of PDEs are obtained with efficient conversion variables. The dimensionless PDEs are truncated using a local non-similarity approach up to third level and numerical solution is evaluated using MATLAB built-in-function bvp4c. Artificial Neural Networks (ANNs) simulation approach is used to trained the networks to predict the solution behavior. Thermal boundary layer improves with the enhancement in the value of $Rd$. The accuracy and reliability of ANNs predicted solution is addressed with computation of correlation index and residual analysis. The RMSE is evaluated [0.04892, 0.0007597, 0.0007596, 0.01546, 0.008871, 0.01686] for various scenarios. It is observed that when concentration of hybrid nanoparticles increases then thermal characteristics of the Eyring-Powell Hybrid Nanofluid (EPHNFs) passing a horizontal cylinder. Full article

The present study evaluates the heat transfer performance of an immersion liquid cooling system, utilizing copper-water (Cu-water) nanofluids under various flow and geometric conditions, including different Reynolds and Rayleigh numbers, nanoparticle volume fractions, and block spacing configurations. To this end, numerical simulations were conducted to assess the impact of these parameters on the system’s temperature distribution and overall cooling efficiency. The findings indicate that augmenting the Reynolds number from 100 to 500, and the nanoparticle volume fraction from 0% to 5%, at a Rayleigh number of 10⁵, results in substantial enhancements in heat transfer, with improvements reaching up to 193.8%. Furthermore, an increase in the Rayleigh number from 10³ to 10⁶, in conjunction with elevated nanoparticle concentrations at a Reynolds number of 500, yielded a heat transfer enhancement of up to 36.3%. These findings demonstrate that higher Reynolds and Rayleigh numbers promote better heat dissipation through increased convective flow and buoyancy-driven convection. Furthermore, the study underscores the pivotal function of block spacing in maximizing cooling efficacy. While closer spacing results in higher temperatures, wider spacing improves heat transfer efficiency by reducing thermal interference between blocks. The study emphasizes the synergistic effect of an enhanced thermal conductivity, strong convective flow, and optimal geometric configurations in maximizing cooling efficiency. These findings are of crucial importance for the design of more efficient thermal management systems, with applications in electronics cooling, energy systems, and industrial processes. Full article

This study explores transient magnetohydrodynamic (MHD) heat and mass transfer in the flow of a hybrid nanofluid over a stretching surface, considering both steady and unsteady scenarios. The investigation incorporates chemical reactions, slip boundary conditions, and the effects of thermal radiation. The hybrid nanofluid, composed of copper oxide (CuO) and gold (Au) nanoparticles in a water-based fluid, demonstrates enhanced thermal performance compared with base fluids. Key findings reveal that higher nanoparticle concentrations significantly improve heat transfer, highlighting the potential of hybrid nanofluids in advanced thermal management applications. Additionally, machine learning models effectively predict heat transfer characteristics with high accuracy (R² = 0.99), showcasing their effectiveness in complementing traditional numerical methods. These findings contribute to the understanding of hybrid nanofluids in complex thermal systems and highlight the utility of emerging computational tools for thermal analysis. Full article

This study explores the optimization of a Cu–Al₂O₃/water hybrid nanofluid within an irregular wavy enclosure under inclined periodic MHD effects. Hybrid nanofluids, with different mixture ratios of copper (Cu) and alumina (Al₂O₃) nanoparticles in water, are used in this study. Numerical simulations using the Galerkin residual-based finite-element method (FEM) are conducted to solve the governing PDEs. At the same time, artificial neural networks (ANNs) and response surface methodology (RSM) are employed to optimize thermal performance by maximizing the average Nusselt number (Nu_av), the key indicator of thermal transport efficiency. Thermophysical properties such as viscosity and thermal conductivity are evaluated for validation against experimental data. The results include visual representations of heatlines, streamlines, and isotherms for various physical parameters. Additionally, Nu_av, friction factors, and thermal efficiency index are analyzed using different nanoparticle ratios. The findings show that buoyancy and MHD parameters significantly influence heat transfer, friction, and thermal efficiency. The addition of Cu nanoparticles improves heat transport compared to Al₂O₃ nanofluid, demonstrating the superior thermal conductivity of the Cu–Al₂O₃/water hybrid nanofluid. The results also indicate that adding Al₂O₃ nanoparticles to the Cu/water nanofluid diminishes the heat transport rate. The waviness of the geometry shows a significant impact on thermal management as well. Moreover, the statistical RSM analysis indicates a high R² value of 98.88% for the response function, which suggests that the model is well suited for predicting Nu_av. Furthermore, the ANN model demonstrates high accuracy with a mean squared error (MSE) of 0.00018, making it a strong alternative to RSM analysis. Finally, this study focuses on the interaction between the hybrid nanofluid, a wavy geometry, and MHD effects, which can optimize heat transfer and contribute to energy-efficient cooling or heating technologies. Full article

The present study investigates the entropy generation of chemically reactive micropolar hybrid nanoparticle motion with mass transfer. Magnetic oxide (Fe₃O₄) and copper oxide (CuO) nanoparticles were mixed in water to form a hybrid nanofluid. The governing equations for velocity, concentration, and temperature are transformed into ordinary differential equations along with the boundary conditions. In the fluid region, the heat balance is kept conservative with a source/sink that relies on the temperature. In the case of radiation, there is a differential equation along with several characteristic coefficients that transform hypergeometric and Kummer’s differential equations by a new variable. Furthermore, the results of the current problem can be discussed by implementing a graphical representation with different factors, namely the Brinkman number, porosity parameter, magnetic field, micropolar parameter, thermal radiation, Schmidt number, heat source/sink parameter, and mass transpiration. The results of this study are presented through graphical representations that depict various factors influencing the flow profiles and physical characteristics. The results reveal that an increase in the magnetic field leads to a reduction in velocity and entropy production. Furthermore, temperature and entropy generation rise with a stronger radiation parameter, whereas the Nusselt number experiences a decline. This study has several industrial applications in technology and manufacturing processes, including paper production, polymer extrusion, and the development of specialized materials. Full article

The present study investigates hybrid nanofluid (HNF) behavior at the stagnation point near a stretching/shrinking sheet using the Tiwari and Das model. The governing equations were transformed into a boundary layer flow model and simulated using COMSOL Multiphysics 6.0. This research examines flow characteristics, temperature profiles, and distributions by varying parameters: stretching/shrinking ($\lambda$, −2 to 2), slip flow ($\delta$, 0 to 1 m), suction ($\gamma$, 0 to 1), and similarity variables ($\eta$, 0 to 5). The HNF comprised equal ratios of copper and alumina with total concentrations ranging from 0.01 to 0.1. The results showed that velocity profiles increased with distance from the stagnation point, escalated in shrinking cases, and decayed in stretching cases. Increased suction consistently reduced velocity profiles. Temperature distribution was slightly slower in shrinking compared to stretching cases, with expansion along the sheet directly proportional to $\eta$ estimates but controllable through suction adjustments. The findings were applied to enhance photovoltaic thermal (PV/T) system performance. Stretching sheets proved crucial for improving electricity production efficiency. Non-slip wall conditions and increased copper volume fractions in the presence of suction effects led to notable improvements in electrical efficiency. The maximum average efficiency was achieved when $\gamma$ = 0.4, $\lambda$ = 2, $\delta$ = 0.7, and ${\varphi }_2$ = 0.01, which was of about 10%. The present numerical work also aligned well with the experimental results when evaluating the thermal efficiency of conventional fluids. These insights contribute to optimizing PV/T system parameters and advancing solar energy conversion technology, with potential implications for broader applications in the field. Full article

To comprehend the thermal regulation within the conical gap between a disk and a cone (TRHNF-DC) for hybrid nanofluid flow, this research introduces a novel application of computationally intelligent heuristics utilizing backpropagated Levenberg–Marquardt neural networks (LM-NNs). A unique hybrid nanoliquid comprising aluminum oxide, $A{l}_2{O}_3$, nanoparticles and copper, $Cu$, nanoparticles is specifically addressed. Through the application of similarity transformations, the mathematical model formulated in terms of partial differential equations (PDEs) is converted into ordinary differential equations (ODEs). The BVP4C method is employed to generate a dataset encompassing various TRHNF-DC scenarios by varying magnetic parameters and nanoparticles. Subsequently, the intelligent LM-NN solver is trained, tested, and validated to ascertain the TRHNF-DC solution under diverse conditions. The accuracy of the LM-NN approach in solving the TRHNF-DC model is verified through different analyses, demonstrating a high level of accuracy, with discrepancies ranging from ${10}^{-10}$ to ${10}^{-8}$ when compared with standard solutions. The efficacy of the framework is further underscored by the close agreement of recommended outcomes with reference solutions, thereby validating its integrity. Full article

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 $A{l}_2{O}_3$ 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 $\zeta =\pi /6$ 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. Full article

This study theoretically investigates the temperature and velocity spatial distributions in the flow of a copper–water nanofluid induced by a rotating rigid disk in a porous medium. Unlike previous work on similar systems, we assume that the disk surface is well polished (coated); therefore, there are velocity and temperature slips between the nanofluid and the disk surface. The importance of considering slip conditions in modeling nanofluids comes from practical applications where rotating parts of machines may be coated. Additionally, this study examines the influence of heat generation on the temperature distribution within the flow. By transforming the original Navier–Stokes partial differential equations (PDEs) into a system of ordinary differential equations (ODEs), numerical solutions are obtained. The boundary conditions for velocity and temperature slips are formulated using the effective viscosity and thermal conductivity of the copper–water nanofluid. The dependence of the velocity and temperature fields in the nanofluid flow on key parameters is investigated. The major findings of the study are that the nanoparticle volume fraction significantly impacts the temperature distribution, particularly in the presence of a heat source. Furthermore, polishing the disk surface enhances velocity slips, reducing stresses at the disk surface, while a pronounced velocity slip leads to distinct changes in the radial, azimuthal, and axial velocity components. The study highlights the influence of slip conditions on fluid velocity as compared to previously considered non-slip conditions. This suggests that accounting for slip conditions for coated rotating disks would yield more accurate predictions in assessing heat transfer, which would be potentially important for the practical design of various devices using nanofluids. Full article

Understanding of dusty fluids for different Brinkman numbers in porous media is limited. This study examines the Darcy–Brinkman model for two-dimensional magneto-hydrodynamic fluid flow across permeable stretching/shrinking surfaces with heat transfer. Water was considered as a conventional base fluid in which the copper (Cu), silver (Ag), and titanium dioxide ($Ti{O}_2$) nanoparticles were submerged in a preparation of a ternary dusty nanofluid. The governing nonlinear partial differential equations are converted to ordinary differential equations through suitable similarity conversions. Under radiation and mass transpiration, analytical solutions for stretching sheets/shrinking sheets are obtained. Several parameters are investigated, including the magnetic field, Darcy–Brinkman model, solution domain, and inverse Darcy number. The outcomes of the present article reveal that increasing the Brinkman number and inverse Darcy number decreases the velocity of the fluid and dusty phase. Increasing the magnetic field decreases the momentum of the boundary layer. Ternary dusty nanofluids have significantly improved the heat transmission process for manufacturing with applications in engineering, and biological and physical sciences. The findings of this study demonstrate that the ternary nanofluid phase’s heat and mass transpiration performance is better than the dusty phase’s performance. Full article

In order to more accurately characterize the effects of nanoparticles on lubricant viscosity, the effects of copper dialkyl dithiophosphate (HDDP)-modified (CuDDP) nanoparticles on the dynamic viscosity of mineral oils 150N, alkylated naphthalene (AN5), diisooctyl sebacate (DIOS), and polyalphaolefins (PAO4, PAO6, PAO10, PAO40, and PAO100) were investigated at an experimental temperature of 40 °C and additive mass fraction ranging from 0.5% to 2.5%. CuDDP exhibits a viscosity-reducing effect on higher-viscosity base oils, such as PAO40 and PAO100, and a viscosity-increasing effect on lower-viscosity base oils, namely, 150N, AN5, DIOS, PAO4, PAO6, and PAO10. These effects can be attributed to the interfacial slip effect and the shear resistance of the nanoparticles. The experimental dynamic viscosity of the eight base oils containing CuDDP was compared with that calculated by the three classical formulae of nanofluid viscosity, The predicted viscosity values of the formulae deviated greatly from the experimental viscosity values, with the maximum deviation being 7.9%. On this basis, the interface slip effect was introduced into Einstein’s formula, the interface effect was quantified with the aniline point of the base oil, and a new equation was established to reflect the influence of CuDDP nanoparticles on lubricating oil viscosity. It can better reflect the influence of CuDDP on the viscosity of various base oils, and the deviation from the experimental data is less than 1.7%. Full article

Polyacrylamide, silica, and other nanoparticles have all been realized in the field of enhanced oil recovery. Researchers often explore the mechanisms of spreading behavior and simulated displacement to develop more efficient types of nanoparticles. In this study, copper quantum dots were introduced into a acrylamide copolymerization system to obtain composite nanospheres and its structure, topographic, and application performance were characterized. The results show that the composite nanospheres have a particle size of around 25 nm, are uniformly loaded with copper particles, and have good temperature resistance. The spreading ability on the quartz flake surfaces and displacement effect in microchannels of composite nanospheres, acrylamide copolymer nanospheres, and copper quantum dots were compared by nanofluid spreading experiments and microchannel chip oil displacement experiments. The results indicate that the composite nanospheres can effectively reduce the water contact angle, promote the spreading of aqueous phase, and accelerate the oil droplet removal process; the accelerating effect is stronger than other samples. Its oil displacement effect is also the strongest, and it is minimized by the influence of channel size, temperature, and dispersing medium, with better stratigraphic adaptability. This work supports the practical application of copper quantum dot/polyacrylamide composite nanospheres in the oilfield. Full article

This study investigates the flow of a magnetohydrodynamic (MHD) Maxwell fluid over a stretching sheet using a Darcy-Forchheimer (DF) model. We employ numerical analysis with a copper (Cu) nanofluid suspended in water, considering Cattaneo–Christov heat flow, viscous dissipation, and joule heating. Nonlinear ordinary differential equations (ODEs) are solved using the bvp4c method in Matlab and we examine the normalized shear stress, temperature profile, and heat flux rate. Our findings reveal insights for practical applications, showing how parameters such as the relaxation Prandtl number, magnetic parameter, Eckert number parameter, and radiation parameter impact system behaviour. Full article

In this current work, we assume the mathematical modelling of non-Newtonian time-dependent hybrid nanoparticles via a cylindrical stenosis artery. In this work, blood is used as a base fluid, and the nanoparticles (copper and aluminum oxide) of cylindrical shape are inserted inside the artery to combine with blood to form hybrid nanofluid (HNF). The homotopy analysis method (HAM) is deployed for the solution of nonlinear resulting equations. For the validation of this current work, the results of the existing work have been compared with our proposed model results. A comparison of key profiles like velocity, temperature, wall shear stress, and flow rate is also performed at a specific critical height of the stenosis. It is also observed that the thermal conductance of hybrid nanofluids is greater than that of nanofluids. Including the hybrid nanoparticles (copper and aluminum oxide) inside the blood enhances the blood axial velocity. These simulations are applicable to the magnetic targeting treatment of stenosed artery disorders and the diffusion of nanodrugs. Full article