Search Results (399)

In order to enhance the efficiency of direct absorption solar collectors, this study carried out an experimental analysis about the optical and photothermal conversion performance of Fe₃O₄, ATO (Antimony-doped tin oxide), and Fe₃O₄/ATO nanofluids with a total concentration of 0.1 wt%. According to the results of the experiments, Fe₃O₄ nanofluid outperforms ATO nanofluid in terms of optical absorption; nevertheless, at wavelengths shorter than 600 nm, it also shows significant scattering reflection. The solar-weighted absorption coefficient of Fe₃O₄/ATO nanofluid rose from 0.863 ($m_{Fe3O4}/m_{Total}$= 0.2) to 0.932 ($m_{Fe3O4}/m_{Total}$= 0.8) when the optical path length increased from 0.01 m to 0.06 m. Moreover, the Fe₃O₄/ATO hybrid nanofluid achieved a photothermal conversion efficiency of 0.932 when the mass ratio of Fe₃O₄ to total mass was 0.2, surpassing the efficiencies of 0.892 and 0.898 recorded for 0.1 wt% ATO and Fe₃O₄ nanofluids, respectively. When present together, the opposing optical characteristics of Fe₃O₄ and ATO boost photothermal conversion performance, which is anticipated to raise the efficiency of direct absorption solar collectors. Full article

In the present work, hybrid Cellulose Nanocrystal–MXene (CNC–MXene) nanolubricants were prepared via a two-step method and investigated as potential heat-transfer hybrid nanofluids for the first time. CNC–MXene nanolubricants were synthesized via a two-step method by varying the weight percentage of CNC–MXene nanoparticles (ranging from 0.01 to 0.05 wt%) and characterized using Fourier-Transform Infrared Spectroscopy and TGA (Thermogravimetric Analysis). Response surface methodology (RSM) was used in conjunction with the miscellaneous design model to identify prediction models for the thermophysical properties of the hybrid CNC–MXene nanolubricant. Minitab 18 statistical analysis software and Response Surface Methodology (RSM) based on Central Composite Design (CCD) were utilized to generate an empirical mathematical model investigating the effect of concentration and temperature. The analysis of variance (ANOVA) results indicated significant contributions from the type of nanolubricant (p < 0.001) and the quadratic effect of temperature (p < 0.001), highlighting non-linear interactions that affect viscosity and thermal conductivity. The findings showed that the predicted values closely matched the experimental results, with a percentage of absolute error below 9%, confirming the reliability of the optimization models. Additionally, the models could predict more than 85% of the nanolubricant output variations, indicating high model accuracy. The optimization analysis identified optimal conditions for maximizing both dynamic viscosity and thermal conductivity. The predicted optimal values (17.0685 for dynamic viscosity and 0.3317 for thermal conductivity) were achieved at 30 °C and a 0.01% concentration, with a composite desirability of 1. The findings of the percentage of absolute error (POAE) reveal that the model can precisely predict the optimum experimental parameters. This study contributes to the growing field of advanced nanolubricants by providing insights into the synergistic effects of CNC and MXene in enhancing thermophysical properties. The developed models and optimization techniques offer valuable tools for tailoring nanolubricant formulations to specific tribological applications, potentially leading to improved efficiency and durability in various industrial settings. Full article

Installation of flat solar collectors (FSCs) has been increasing due to the zero cost of renewable energy. However, the performance of this equipment is limited by the area, the material and the thermophysical properties of the working fluid. To improve the properties of the fluid, metal and metal oxide nanoparticles have mainly been used. This paper presents the performance assessment of the FSCs using simple and hybrid carbon nanofluids of low thermal capacity. Energy and mass balance modeling was performed for this study. A parametric analysis was conducted to examine the impact of key variables on the performance of the solar collectors using simple graphite and fullerene nanofluids, as well as hybrid metal–oxide–carbon nanofluids. From the results of heat transfer in FSCs, using graphite and fullerene nanofluids, it can be concluded that adding these nanoparticles improves the convection coefficient by 40% and 30%, respectively, with 10% nanoparticles. The graphite and fullerene nanoparticles can enhance the efficiency of FSCs by 2% and 1.5% more than base fluid. As the decrease in efficiency using fullerene with magnesium oxide is less than 0.2%, fullerene hybrid nanofluids could still be used in FSCs. Full article

This paper presents a numerical study of natural convection in an annular cavity filled with a hybrid nanofluid under the influence of a magnetic field. This study is significant for applications requiring enhanced thermal management, such as in heat exchangers, electronics cooling, and energy systems. The inner cylinder, equipped with fins and subjected to uniform volumetric heat generation, contrasts with the adiabatic outer cylinder. This study aims to investigate how different nanoparticle combinations (Fe₃O₄ with Cu, Ag, and Al₂O₃) and varying Hartmann and Rayleigh numbers impact heat transfer efficiency. The finite volume method is employed to solve the governing equations, with simulations conducted using Fluent 6.3.26. Parameters such as volume fraction (ϕ₂ = 0.001, 0.004, 0.006), Hartmann number (0 ≤ Ha ≤ 100), Rayleigh number (3 × 10³ ≤ Ra ≤ 2.4 × 10⁴), and fin number (N = 0, 2, 4, 6, 8) are analyzed. Streamlines, isotherms, and induced magnetic field contours are utilized to assess flow structure and heat transfer. The results reveal that increasing the Rayleigh number and magnetic field enhances heat transfer, while the presence of fins, especially at N = 2, may inhibit convection currents and reduce heat transfer efficiency. These findings provide valuable insights into optimizing nanofluid-based cooling systems and highlight the trade-offs in incorporating fins in thermal management designs. Full article

This study aims to propose a central composite design (CCD) combined with response surface methodology (RSM) to create a statistical experimental design. A new parametric optimization of entropy generation is presented. The flow behavior of magnetohydrodynamic hybrid nanofluid (HNF) flow through two flat contracting expanding plates of channel alongside radiative heat transmission was considered. The lower fixed plate was externally heated whereas the upper porous plate was cooled by injecting a coolant fluid with a uniform velocity inside the channel. The resulting equations were solved by the Homotopic Analysis Method using MATHEMATICA 10 and Minitab 17.1. The design consists of several input factors, namely a magnetic field parameter ($\mathrm{M}$), radiation parameter ($\mathrm{N}$) and group parameter ($\mathrm{B}\mathrm{r}/\mathrm{A}1$). To obtain the values of flow response parameters, numerical experiments were used. Variables, especially the entropy generation ($\mathrm{N}\mathrm{e}$), were considered for each combination of design. The resulting RSM empirical model obtained a high coefficient of determination, reaching 99.97% for the entropy generation number ($\mathrm{N}\mathrm{e}$). These values show an excellent fit of the model to the data. Full article

Hybrid nanofluids have caught the attention of scholars and investigators in the present technological period due to their improved thermophysical features and the desire to boost heat transfer rates compared to those of conventional fluids. The present paper is mainly concerned with heat transmission in cone-disk geometry in the presence of a magnetic field, activation energy, and non-uniform heat absorption/generation. In this work, the cone-disk (CD) apparatus is considered to have a rotating cone (RC) and a stretching disk, along with iron oxide and cobalt ferrite-based hybrid nanofluid. Appropriate similarity transformations are employed to change the physically modeled equations into ordinary differential equations (ODEs). Heat transfer rates at both surfaces are estimated by implementing a modified energy equation with non-uniform heat absorption/generation. The outcomes illustrated that the inclusion of such physical streamwise heat conduction variables in the energy equation has a significant impact on the well-known conclusions of heat transfer rates. To understand flow profile behavior, we have resorted to the RKF-45 method and the shooting method, which are illustrated using graphs. The findings provide conclusive evidence that wall stretching alters the flow, heat, and mass profile characteristics within the conical gap. The wall deformation caused by disk stretching was found to have a potential impact of modifying the centripetal/centrifugal flow characteristics of the disk, increasing the flow velocity and swirling angles. A rise in activation energy leads to an improved concentration field. 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

The main objective of this study is to explore the inventive conception of the magnetohydrodynamic flow of a hybrid nanofluid over-porous stretching/shrinking sheet with the effect of radiation and mass suction/injection. The hybrid nanofluid advances both the manufactured nanofluid of the current region and the base fluid. For the current investigation, hybrid nanofluids comprising two different kinds of nanoparticles, aluminium oxide and ferrofluid, contained in water as a base fluid, are considered. A collection of highly nonlinear partial differential equations is used to model the whole physical problem. These equations are then transformed into highly nonlinear ordinary differential equations using an appropriate similarity technique. The transformed differential equations are nonlinear, and thus it is difficult to analytically solve considering temperature increases. Then, the outcome is described in incomplete gamma function form. The considered physical parameters namely, magnetic field, Inverse Darcy number, velocity slip, suction/injection, temperature jump effects on velocity, temperature, skin friction and Nusselt number profiles are reviewed using plots. The results reveal that magnetic field, and Inverse Darcy number values increase as the momentum boundary layer decreases. Moreover, higher values of heat sources and thermal radiation enhance the thermal boundary layer. The present problem has various applications in manufacturing and technological devices such as cooling systems, condensers, microelectronics, digital cooling, car radiators, nuclear power stations, nano-drag shipments, automobile production, and tumour treatments. 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

For efficient heating and cooling applications, minimum wall shear stress and maximum heat transfer rate are desired. The current study optimized the local skin friction coefficient and Nusselt number in Al₂O₃-Cu/water hybrid nanofluid flow over a permeable shrinking rotating disk. First, the governing equations and boundary conditions are solved numerically using the bvp4c solver in MATLAB. Von Kármán’s transformations are used to reduce the partial differential equations into solvable non-linear ordinary differential equations. The augmentation of the mass transfer parameter is found to reduce the local skin friction coefficient and Nusselt number. Higher values of these physical quantities of interest are observed in the injection case than in the suction case. Meanwhile, the increase in the magnitude of the shrinking parameter improved and reduced the local skin friction coefficient and Nusselt number, respectively. Then, response surface methodology (RSM) is conducted to understand the interactive impacts of the controlling parameters in optimizing the physical quantities of interest. With a desirability of 66%, the local skin friction coefficient and Nusselt number are optimized at 1.528780016 and 0.888353037 when the shrinking parameter ($\lambda$) and mass transfer parameter ($S$) are −0.8 and −0.6, respectively. Full article

This paper concerns an economic and exergetic efficiency analysis of a plate heat exchanger placed in a solar installation with TiO₂:SiO₂/DI:EG nanofluid. This device separates the primary circuit—with the solar fluid—and the secondary circuit—in which domestic hot water flows (DHW). The solar fluid is TiO₂:SiO₂ nanofluid with a concentration in the range of 0.5–1.5%vol. and T = 60 °C. Its flow is maintained at a constant level of 3 dm³/min. The heat-receiving medium is domestic water with an initial temperature of 30 °C. This work records a DHW flow of ${\dot{V}}_{DHW,in}$ = 3–6(12) dm³/min. In order to calculate the exergy efficiency of the system, first, the total exergy destruction, the entropy generation number N_s, and the Bejan number Be are determined. Only for a comparable solar fluid flow, DHW ${\dot{V}}_{nf}={\dot{V}}_{DHW}$ 3 dm³/min, and concentrations of 0 and 0.5%vol. is there no significant improvement in the exergy efficiency. In other cases, the presence of nanoparticles significantly improves the heat transfer. The TiO₂:SiO₂/DI:EG nanofluid is even a 13 to 26% more effective working fluid than the traditional solar fluid; at Re = 329, the exergy efficiency is ${\eta }_{exergy}$ = 37.29%, with a nanoparticle concentration of 0% and ${\eta }_{exergy}$(1.5%vol.) = 50.56%; with Re = 430, ${\eta }_{exergy}$(0%) = 57.03% and ${\eta }_{exergy}$(1.5%) = 65.9%. Full article

This study investigates the potential of a hybrid nanofluid composed of MoS₂ and ZnO nanoparticles dispersed in engine oil, aiming to enhance the properties of a lubricant’s chemical reaction with the Soret effect on a stretching sheet under the influence of an applied magnetic field. With the growing demand for efficient lubrication systems in various industrial applications, including automotive engines, the development of novel nanofluid-based lubricants presents a promising avenue for improving engine performance and longevity. However, the synergistic effects of hybrid nanoparticles in engine oil remain relatively unexplored. The present research addresses this gap by examining the thermal conductivity, viscosity, and wear resistance of the hybrid nanofluid, shedding light on its potential as an advanced lubrication solution. Overall, the objectives of studying the hybrid nanolubricant MoS₂ + ZnO with engine oil aim to advance the development of more efficient and durable lubrication solutions for automotive engines, contributing to improved reliability, fuel efficiency, and environmental sustainability. In the present study, the heat and mass transformation of a Casson hybrid nanofluid (MoS₂ + ZnO) based on engine oil over a stretched wall with chemical reaction and thermo-diffusion effect is analyzed. The governing nonlinear partial differential equations are simplified as ordinary differential equations (ODEs) by utilizing the relevant similarity variables. The MATLAB Bvp4c technique is used to solve the obtained linear ODE equations. The results are presented through graphs and tables for various parameters, namely, M, Q, β, Pr, Ec, Sc, Sr, Kp, Kr, and ${\varphi }_2*$ (hybrid nanolubricant parameters) and various state variables. A comparative survey of all the graphs is presented for the nanofluid (MoS₂/engine oil) and the hybrid nanofluid (MoS₂ + ZnO/engine oil). The results reveal that the velocity profile diminished against the values of M, Kp, and β, and the temperature profile rises with Ec and Q, whereas Pr decreases. The concentration profile is incremented (decremented) with the value of Sr (Sc and Kr). A comparison of the nanofluid and hybrid nanofluid suggests that the velocity f′ (η) becomes slower with the augmentation of ${\varphi }_2*$ whereas the temperature increases when ${\varphi }_2*$ = 0.6 become slower. 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

Particle trapping and enrichment into confined volumes can be useful in particle processing and analysis. This review is an evaluation of the methods used to trap and enrich particles into constrained volumes in microfluidic and nanofluidic systems. These methods include physical, optical, electrical, magnetic, acoustic, and some hybrid techniques, all capable of locally enhancing nano- and microparticle concentrations on a microscale. Some key qualitative and quantitative comparison points are also explored, illustrating the specific applicability and challenges of each method. A few applications of these types of particle trapping are also discussed, including enhancing biological and chemical sensors, particle washing techniques, and fluid medium exchange systems. Full article

This study systematically explores and compares the performance of various artificial-intelligence (AI)-based models to predict the electrical and thermal efficiency of photovoltaic–thermal systems (PVTs) cooled by nanofluids. Employing extreme gradient boosting (XGB), extra tree regression (ETR), and k-nearest-neighbor (KNN) regression models, their accuracy is quantitatively evaluated, and their effectiveness measured. The results demonstrate that both XGB and ETR models consistently outperform KNN in accurately predicting both electrical and thermal efficiency. Specifically, the XGB model achieves remarkable correlation coefficient (R²) values of approximately 0.99999, signifying its superior predictive capabilities. Notably, the XGB model exhibits a slightly superior performance compared to ETR in estimating electrical efficiency. Furthermore, when predicting thermal efficiency, both XGB and ETR models demonstrate excellence, with the XGB model showing a slight edge based on R² values. Validation against new data points reveals outstanding predictive performance, with the XGB model attaining R² values of 0.99997 for electrical efficiency and 0.99995 for thermal efficiency. These quantitative findings underscore the accuracy and reliability of the XGB and ETR models in predicting the electrical and thermal efficiency of PVT systems when cooled by nanofluids. The study’s implications are significant for PVT system designers and industry professionals, as the incorporation of AI-based models offers improved accuracy, faster prediction times, and the ability to handle large datasets. The models presented in this study contribute to system optimization, performance evaluation, and decision-making in the field. Additionally, robust validation against new data enhances the credibility of these models, advancing the overall understanding and applicability of AI in PVT systems. Full article