Viscosity Of Nanofluids Research Articles

Rheometric Analysis of Impact of Temperature, Volume Fraction and Mass of Nanoparticle on the Viscosity of Water Based Nanofluids

Nanoparticle volume fraction, temperature and molecular mass of nanoparticle impact viscosity of nanofluids. Among them, temperature has an intrinsic influence on viscosity and is recommended to be the most crucial and dominant parameter. There are several theory based models for finding viscosity of nanofluids. Even a small addition of nanoparticles to the base liquid greatly increases the viscosity of nanofluid. The viscosity of the nanofluid varies with the particle size even if the nanoparticle does not change. In this work, theoretical viscosity of nanofluid is compared with experimental viscosity at different temperatures and volume fraction. The analysis is done for nanofluids having nanoparticles of different molecular masses.

Nanotechnology-integrated phase change material and nanofluids for solar applications as a potential approach for clean energy strategies: Progress, challenges, and opportunities

Integrating nanotechnology into phase change materials (PCMs) has emerged as a novel approach to improving PCM thermal properties and performance in various thermal energy storage applications. Nanofluids, nanoparticle suspensions in a base fluid, have been identified as a promising method of increasing the thermal conductivity of PCMs and thus reducing thermal energy charging and discharging duration. This review paper investigates recent advances in incorporating nanotechnology into PCMs, focusing on their applications in the solar energy sector. Thermal conductivity, specific heat, viscosity, density, and convective heat transfer coefficient of nanofluids are discussed, as are the thermal properties of nano-enhanced PCMs (NEPCMs), including thermal conductivity, latent heat, specific heat, viscosity, supercooling, and phase-change temperature. The paper also gives an in-depth look at the uses of nanofluids and NEPCMs in solar collectors, photovoltaic/thermal (PV/T) systems, coolants, and desalination, emphasizing their potential to improve system efficiency. The review also considers the potential environmental and human health concerns connected with using nanofluids and NEPCMs, emphasizing the importance of rigorous evaluation and risk assessment in developing and applying these materials. Overall, the review provides valuable insights into the potential benefits and challenges of incorporating nanotechnology into PCMs and emphasizes the importance of ongoing research and development in this field to further advance the use of thermal energy storage technologies in various applications, including solar energy systems.

A study of Cu/Ag nanoparticle shape-augmented heat transfer in Darcy–Brinkman–Forchheimer flow in a square cavity

ABSTRACT The present work is an analysis of heat transfer flow phenomena due to the natural convection process in a square enclosure filled with a porous medium containing Cu/Ag water-based nanofluid. Multiphysics faceted Darcy–Brinkman–Forchheimer model is used to govern the flow in a porous medium. Different shapes (sphere, blades, bricks, cylinders, and platelets) of the nanoparticles are considered to examine their influence on heat transfer. The model has been solved by the finite element method. The numerical results have been validated against results from the literature. Computed results have been presented graphically by streamlines, isotherms, local and cumulative Nusselt number plots for various physical parameters, Rayleigh number (Ra), inverse Darcy number (), Brinkman effect (Λ), the porosity of the porous medium and solid volume fraction of the nanoparticles. The study pioneers the investigation on the significance of the combined influence of effective viscosity and nanofluid viscosity through the Brinkman effect and reports its importance on nanoparticle shape-based heat transfer augmentation. Cost-effective Cu nanoparticles are shown to augment the heat transfer as achieved by the conventionally preferred Ag nanoparticles. While spherical nanoparticles outperform other shapes in heat transfer augmentation for Brinkman effect , the blade-shaped nanoparticles dominate when Brinkman effect . Higher heat transfer rates are noticed for lesser values of the Brinkman effect, high porosity, the maximum volume fraction of the nanoparticles, high Rayleigh number, and low permeability with spherical-shaped nanoparticles than blade-shaped nanoparticles.

Investigation of different nanoparticles properties on the thermal conductivity and viscosity of nanofluids by molecular dynamics simulation

Abstract The mechanisms of thermal conductivity enhancement and the factors influencing viscosity are of great interest in the study of nanofluids, while molecular dynamics (MD) simulations considering nanofluids provide more accurate predictions of microscopic properties than conventional experimental studies. MD simulations of non-equilibrium molecular dynamics and reversing perturbation non-equilibrium molecular dynamics methods were used to study thermal conductivity and viscosity, taking into account a variety of influencing factors, as well as nanoparticle material and volume fraction. Through the analysis of the number density distribution, radial distribution function (RDF), and mean square displacement (MSD), the influences of different nanoparticles (Ag, Cu, Au, and Fe) were described and investigated: Ag particles contribute to 47.0% increase in thermal conductivity of 2.5 vol% nanofluids; Au particles improved the viscosity of 2.5 vol% nanofluids by 20.2%; the number density distribution showed positive linear relationship with the atomic mass; the results of MSD and RDF (mean square displacement and radial distribution function) in combination indicated a positive effect of interfacial nanolayer. The results of this research provide important perspectives for comprehending the impacts of multiple nanoparticles on the micro-thermal properties of nanofluids and also highlight the simulation potential of Au–Ar nanofluids.

Numerical study of dissipative SW/MWCNT-nanofluid coating flow from a stretching wall to a porous medium with shape factor effects

A mathematical model is developed for incompressible steady-state dissipative CNT-aqueous nanofluid boundary layer flow from a stretching sheet to a saturated isotropic porous medium. Three different CNT shapes (spheres, blades and platelets) are considered. Both single-walled (SWCNT) and multi-walled (MWCNT) carbon nanotubes are examined. A Darcy-Brinkman model is adopted for the porous medium and a modified viscous dissipation formulation is considered which features porous media influence in the energy conservation equation. Appropriate expressions are deployed for the CNT-modified nanofluid viscosity, density, specific heat capacity, thermal conductivity and CNT shape factor. Via similarity transformations, the governing conservation equations for mass, momentum and energy with associated boundary conditions are normalized to generate a coupled nonlinear ordinary differential boundary value problem. A numerical solution is presented with the robust MATLAB-based bvp4c method and 4th order Runge-Kutta shooting scheme. Validation with previous studies is included. Velocity, temperature, skin friction and Nusselt number are computed for a range of selected parameters. The computations show that elevation in CNT volume fraction parameter accelerates the boundary layer flow whereas increment in the Darcian (inverse permeability) parameter induces strong deceleration. MWCNTs produce higher velocities than SWCNTs. Temperature and thermal boundary layer thickness are found to be enhanced with increasing Eckert number, Darcian (inverse permeability) parameter, CNT volume fraction and CNT shape factor. Significantly greater temperatures are computed for MWCNTs.

Optimization of the Effects of Binary Hybrid Nanofluid Synthesis Parameters on the Thermal and Hydraulic Characteristics

Due to the growing interest in hybrid nanofluids, researchers have been primarily focused to obtain the thermophysical properties of hybrid nanofluids. Several parameters such as temperature, volume or weight fractions, nanoparticle types and shapes affected the thermophysical properties of nanofluids. Accordingly, the optimization in thermal conductivity and viscosity of nanofluids obtained by mixing binary nanocomposite particles GnP-Fe3O4 in an ethylene glycol-water base fluid with a mixing ratio of 20-80 % was investigated in this study. The Taguchi approach is used for single-objective optimization and the significance values of the synthesis parameters were determined using the ANOVA technique. Five different factors, including mechanical strring time, ultrasonic mixing time and power, surfactant mixing ratio, and nanofluid weight ratio, were optimized at three different levels during the synthesis of hybrid nanofluids. The experimental L27(35) orthogonal array trial was built in order to carry out the optimization. According to the results, mechanical striring time was found to have the least impact on both thermophysical parameters, whereas ultrasonic mixing power, nanofluid weight ratio, and ultrasonic mixing time were also ranked from low to high impact. The usage of surfactant was shown to be the parameter that had the greatest impact, with rates of 63.57% and 65.31%, on thermal conductivity and viscosity, respectively.

Effect of silica-based hybrid nano-surfactant on interfacial tension reduction for enhanced oil recovery

Crude oil production has considerably stabilized economic growth on a global scale. Unfortunately, the difficulties of oil production from a reservoir is being a global concern. Enhanced oil recovery (EOR) is one of the methods employed recently to increase oil production rate. Nanotechnology has been identified as one of the effective EOR techniques employed using nanoparticles (NPs) as a suitable candidates. Altering a reservoir's petrophysical property such as reducing interfacial tension (IFT) between crude oil and other liquids is an essential factor that plays a significant role in EOR. Considering the significant effects shown by silicon dioxide (SiO2) and iron dioxide (Fe2O3) NPs in EOR, the present study is aimed at investigating the influence of Fe2O3-SiO2 hybrid NPs supported with a surfactant on IFT. The hybrid NPs were produced and separately incorporated with surfactants of anionic sodium dodecylbenzene sulfonate (SDBS) and polyvinylpyrrolidone (PVP). The physical and chemical properties of the hybrid NPs were analyzed using X-ray diffraction (XRD) and Fourier transforms infrared (FTIR) spectroscopies, respectively. According to the results, the XRD pattern of the Fe2O3-SiO2 hybrid NPs displayed a crystalline structure of Fe2O3 NPs. The FTIR spectra of Fe2O3-SiO2 hybrid NPs showed characteristic absorptions of Fe2O3 and SiO2, indicating successful hybrid formation. The viscosity of Fe2O3-SiO2 nanofluids has increased from 0.95 to 1.85 cP. Upon introducing a hybrid nano-surface of SDBS and PVP, the viscosity values increased to 1.95 cP and 1.90 cP, respectively. Moreover, the Fe2O3-SiO2 hybrid NPs has reduced oil/brine/nanofluids IFT from 17.39 mN m−1 to 5.03 mN m−1. Additional reductions were observed when the surfactants were included. Consequently, IFT (mN m−1) was reduced from 17.39 to 3.08 for Fe2O3-SiO2-PVP and from 17.39 to 2.55 for Fe2O3-SiO2-SDBS. The finding of this study can help for a better understanding of the significant effect of silica-based hybrid nano-surfactant for IFT reduction which in turn could enhance oil production.

Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning

This study focuses on predicting the dynamic viscosity of nanofluids, specifically Polyalpha-Olefin-hexagonal boron nitride (PAO-hBN) using machine learning models. The primary goal of this research is to assess and contrast the effectiveness of three distinct machine learning models: Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main objective is the identification of a model that demonstrates the highest level of accuracy in predicting a nanofluid’s viscosity namely, PAO-hBN nanofluids. The models were trained and validated using 540 experimental data points, where the mean square error (MSE) and the coefficient of determination R2 were utilized for performance evaluation. The results demonstrated that all three models could predict the viscosity of PAO-hBN nanofluids accurately, but the ANFIS and ANN models outperformed the SVR model. The ANFIS and ANN models had similar performance, but the ANN model was preferred due to its faster training and computation time. The optimized ANN model had an R2 of 0.99994, which indicates a high level of accuracy in predicting the viscosity of PAO-hBN nanofluids. The elimination of the shear rate parameter from the input layer improved the accuracy of the ANN model to an absolute relative error of less than 1.89% over the full temperature range (−19.7 °C–70 °C) compared to 11% in the traditional correlation-based model. These results suggest that the use of machine learning models can significantly improve the accuracy of predicting the viscosity of PAO-hBN nanofluids. Overall, this study demonstrated that the use of machine learning models, specifically ANN, can be effective in predicting PAO-hBN nanofluids’ dynamic viscosity. The findings provide a new perspective on how to predict the thermodynamic properties of nanofluids with high accuracy, which could have important applications in various industries.

Effect of the concentration of carbon nanotubes (CNTs) on the rheological behavior in a lubricating oil of plant-derived lubricant

Nanofluids have attracted a lot of attention since their significant enhancement in thermal properties, however there are few reports on the study of the viscosity of nanofluids from an experimental point of view. In this work, the rheological behavior of functionalized multi-walled carbon nanotubes (F-MWCNTs) and coated with MoS2 (MWCNTs-MoS2) in a commercial palm oil as base fluid was evaluated. The purification and functionalization process of the MWCNTs was performed by acid leaching and investigated by FTIR, and stable nanofluids were prepared with MWCNTs concerning time. The rheological behavior of nanofluids with MWCNTs was performed as a function of temperature at different concentrations (0.1, 0.5 and 1% wt.). The effect of time, temperature, shear rate and shear stress versus shear rate were determined. The results indicate that the nanofluid behaves as a Newtonian fluid independently of the MWCNTs concentrations and temperatures evaluated. The viscosity index increases by 16% at a concentration of 1% wt. of F-MWCNTs and for nanofluids added with MWCNTs-MoS2 had significant effect on the effect of temperature.

Experimental Investigation with ANN Modeling of Thermal Conductivity and Viscosity of a Ternary Nanofluid at Different Mixing Ratios and Volume Concentrations

An experimental investigation of thermal conductivity and viscosity of a new ternary nanofluid with four different mixing ratios and three different volume concentrations is conducted for different temperatures. Graphene nanoplatelets, copper oxide and alumina nanoparticles are used in the preparation of the ternary nanofluid with water-ethylene glycol mixture as the base fluid. The thermal conductivity results reveal the predominant contribution of graphene nanoplatelets in the particle mix. The maximum enhancement in thermal conductivity is found to be 19% at 0.5% volume concentration. Increasing the nanofluid concentration beyond 0.3% gives only a marginal enhancement in terms of figure of merit. Artificial neural network is also designed to predict the thermal conductivity and the viscosity. Seventy-eight sets of experimental data are utilised to build the ANN model and an overall regression data of 0.99496 is obtained indicating good agreement between the ANN model and the present experimental data.

Dynamic viscosity modeling of nanofluids with MgO nanoparticles by utilizing intelligent methods

Nanofluids, as the fluids with modified thermal characteristics, are usable for improving the efficiency of renewable energy systems and enhancement of thermal management. Dynamic viscosity is one of the most significant features of fluids involved in their heat transfer and flow characteristics. This property of nanofluids is under effect of various elements and their modeling is somehow complex. Intelligent methods, regarding their outstanding performance in modeling complex problems, are attractive tool for this purpose. Due to the importance of dynamic viscosity, it is necessary to have accurate model for prediction of this property of nanofluids. Nanofluids with MgO nanoparticles have shown significant performance in thermal mediums and energy devices. In the present article, two intelligent approaches including Adaptive Neuro Fuzzy Inference (ANFIS) and Group Method of Data Handling (GMDH) are made use for modeling dynamic viscosity of nanofluids with MgO nanoparticles. Comparing the determined values by the intelligent methods and the actual quantities revealed significant function of the applied approaches. Among these two methods, using GMDH is preferred regarding its more exactness based on the statistical criteria. R2 of the mentioned methods based on GMDH and ANFIS were 0.9996 and 0.9616, respectively. Average Absolute Relative Deviation (AARD) is another criterion used for comparison of the proposed models that was around 5.13% and 19.41% for the mentioned models, respectively. According to these values, it is concluded that employment of GMDH is preferred in term of exactness.

Characterization Nanoparticles via Newtonian Heating for Fractionalized Hybrid Nanofluid in a Channel Flow

The characterization or measurement of the physical and chemical properties of nanoparticles usually lies in the field of nanometrology based on the different characterization techniques. In order to boost the rate of heat transmission, in this regard, an unsteady and convection flow of a fractionalized hybrid nanofluid in a vertical microchannel consisting of two parallel plates apart is studied through Newtonian heating. The governing equations are fractionalized based on the Caputo–Fabrizio derivative of the non-singular and as an exponential kernel. The fractionalized governing equations are solved by utilizing the Laplace approach. For the Laplace inversions, the Zakian algorithm is utilized to acquire the semi-analytical solution. The obtained solutions are also compared with Stehfest and Tzou numerical algorithms to check the validity. For understanding the physics of the under-considered problem, Mathcad software is used for numerical results and graphical representations. The impacts of different physical parameters are deliberated and exhibited in figures. The Prandtl number Pr always shows a decreasing trend for the temperature and velocity profile. Velocity decreases by growing φ due to increasing the viscosity of nanofluid with φ. The thermal conductivity is improved by increasing φ, so that temperature distribution is increased. This study has numerous applications in thermal engineering.

A Critical Overview of the Impact of Nanoparticles in Ester Fluid for Power Transformers

This paper examines the impact of various nanoparticles on ester fluids with a special focus on their usage towards power transformers. The precautionary measures to be considered on the nanofluids such as preparation methodologies with an appropriate surfactant and its stability is well elucidated. The electrical double layer (EDL) formation around the nanoparticles on its diffusion in the insulating fluid is explained by its different layers away from the particle surface. The partial discharge of ester nanofluids with different detection methods is elaborated on its comparison with conventional IEC 60270 measurements. The field configurations on ester-nanofluids govern the breakdown mechanism with variations in the streamer patterns. The equation of relaxation time towards breakdown is valid only when it is lower than the initiation time for streamers. The flow charges induced at the solid/liquid interface inside transformers depends on the structure of the nanofluid and the condition of pressboard/paper insulation. The impact of different concentrations of nanoparticles on ester nanofluids observes a change in its flow behaviour affecting the streaming current. The permittivity of nanofluid depends on the polarization of nanoparticles where the Clausius-Mossotti equation governing this mechanism is explained towards ester-nanofluids. The viscosity of nanofluids observed no significant variation whereas the other physio-chemical properties such as flash point, interfacial tension, and oxidation stability improved depending on the type of nanoparticle. The addition of metal-oxide nanoparticles on ester fluids increases thermal conductivity with different models proposed based on the structure and shape of a nanoparticle. The impact of ageing on nanofluids observes an instability over a longer ageing duration with specific nanoparticles which should be better understood before implementing them in real-time power transformers.

Viscosity of Fe2O3-water nanofluids by molecular dynamics simulations: Effects of nanoparticle content, temperature and size

In this study, Molecular Dynamics (MD) Simulations are used to calculate the viscosity of Fe2O3-water nanofluid within the Green-Kubo framework considering the influence of nanoparticle volume fraction (2.05, 4.08, 6.08 vol%), nanofluid temperature (T = 5, 25, 45 and 65 °C), and size (d = 1, 1.24, and 1.44 nm). First, by screening some existing water models, SPC/E model is found to be the winning candidate for viscosity calculation under the temperature range. Second, the simulation method and parameters for Fe2O3-Water nanofluid interactions are developed and verified against experimental studies. Fe-O (RDF) of Fe2O3 nanoparticle first peak occurs at r = 1.879 Å with the maximum error of 7.4% in comparison to experimental and density functional theory (DFT) reported values. Simulation results show that the viscosity of Fe2O3-water nanofluid is significantly larger compared to that of the water base fluid (>57%). It is also demonstrated that the viscosity of the nanofluid increases with the particle volume fraction and decreases with temperature and size of nanoparticles with local sensitivities of about 0.69, −0.65, and −0.37, respectively. A second order polynomial regression analysis is performed on various volume fractions to express the relationship between the volume fraction and the viscosity. Our results are in agreement with existing experimental results. It was found that the presence of nanoparticles created a structured water layer near the nanoparticle surface, and that the interactions between water molecules and iron oxide nanoparticles were prominent in the Fe_Ow (iron-oxygen of water) pair interactions. Parameters such as SASA (Solvent Accessible Surface Area), surface to volume ratio, moment of inertia around principal axes, radius of gyration, and RDF are used to provide insights into the behavior of Fe2O3-water nanofluids and the underlying mechanisms. To the authors’ best knowledge, this is the first MD simulation of Fe2O3 nanofluids conducted to date.

Characterization of Copper Oxide–Jatropha Oil Nanofluid as a Secondary Refrigerant

Nanofluid is a new fluid with special characteristics that can be used in various industrial heat transfer applications. The nanofluid was created using the two-step approach of dispersing nanoparticles into Jatropha oil. The experiments were conducted for four samples of mixture nanofluid at four different volume concentrations of 0.5%, 1.0%, 1.5%, and 2%. The nanoparticles were characterized by X-ray diffractometric analysis, X-ray fluorescence spectroscopy, and scanning electron microscopy. The greatest increase in thermal conductivity for CuO–Jatropha oil nanofluids was made possible by 0.5%, 1.0%, 1.5%, and 2% mixture was 4.5%, 6.01%, 7.6, and 9.1%. The study observed that a mixture achieved the highest thermal conductivity for nanofluid at 2%. At a mixture of 0.5%, the lowest value of thermal conductivity within the range under consideration, a “deeding” effect was seen. To determine the thermal conductivity of CuO–Jatropha oil nanofluids based on temperature, volume concentration, and nanoparticle mixture volume concentrations, the study suggested and compared four sample models. The effect of nanoparticles on the rise of thermal conductivity and viscosity was investigated at temperatures ranging from 298 to 323 K. The X-ray diffractometer is used to characterize nanoparticles. Viscosity and thermal conductivity have been tested in the lab. The findings show that the copper oxide nanoparticles improved the base fluid’s thermophysical abilities more than Jatropha oil alone. For the evaluation of the thermal conductivity and viscosity of nanofluid, new empirical correlations were presented based on experimental data. The thermal conductivity of the nanofluids increases as the temperature rises, while the viscosity of the nanofluids decreases.

Experimental study, prediction modeling, sensitivity analysis, and optimization of rheological behavior and dynamic viscosity of 5W30 engine oil based SiO2/MWCNT hybrid nanofluid

In this paper, the rheological performance and dynamic viscosity of hybrid nanofluid containing SiO2 and multi-walled carbon nanotubes (MWCNTs) nanoparticles (90:10) with 5W30 engine oil as base fluid is experimentally evaluated under different shear rates (SRs) in the range of 50–1000 rpm. The hybrid nanofluid volume fractions (VFs) and temperatures are considered in the ranges of 0.05–1.00 vol% and 5–65 °C, respectively. It was found that the hybrid nanofluid under study behaves as a non-Newtonian fluid. In addition, the calculated power law index was lower than unity, resulting in pseudoplastic features of hybrid nanofluid in all VFs and temperatures. It was observed that the rise of nanofluid temperature from 5 to 65 °C leads to the dynamic viscosity reduction (a 93% decrease in viscosity was observed in a VF of 0.2%), while the increase of nanofluid VF brings about the dynamic viscosity elevation (By increasing VF from 0.05% to 1% at SR of 800 rpm and temperature of 25 °C, the viscosity increases by 29.21%). Based on measured data, an innovative three-variable correlation was established that can more accurately estimate the experimental data than published correlations in the literature. Moreover, the capabilities of GMDH-type neural network (NN) and response surface methodology (RSM) to predict the relative viscosity of the hybrid nanofluid were evaluated. It was concluded that both NN and RSM approaches have a superior ability to forecast the dynamic viscosity behavior of the corresponding hybrid nanofluid, having R2 values of 0.999656 and 0.9955. Furthermore, the optimization was performed and the best solution for achieving the minimum dynamic viscosity with the maximum desirability (1.00) was obtained. Eventually, the dynamic viscosity sensitivity to changes in VF, temperature, and SR was evaluated. It was observed that the dynamic viscosity sensitivity increases as the nanofluid temperature and concentration increase considering a constant SR of 800 rpm.

Thermal Performance Evaluation of Plate-Type Heat Exchanger with Alumina–Titania Hybrid Suspensions

This paper aims to develop models for the thermal conductivity and viscosity of hybrid nanofluids of aluminium oxide and titanium dioxide (Al2O3-TiO2). The study investigates the impact of fluid temperature (283 K–298 K) on the performance of a plate heat exchanger using Al2O3-TiO2 hybrid nanofluids with different particle volume ratios (0:5, 1:4, 2:3, 3:2, 4:1, and 5:0) prepared with a 0.1% concentration in deionised water. Experimental evaluations were conducted to assess the heat transfer rate, Nusselt number, heat transfer coefficient, Prandtl number, pressure drop, and performance index. Due to the lower thermal conductivity of TiO2 nanoparticles compared to Al2O3, a rise in the TiO2 ratio decreased the heat transfer coefficient, Nusselt number, and heat transfer rate. Inlet temperature was found to decrease pressure drop and performance index. The Al2O3 (5:0) nanofluid demonstrated the maximum enhancement of around 16.9%, 16.9%, 3.44%, and 3.41% for the heat transfer coefficient, Nusselt number, heat transfer rate, and performance index, respectively. Additionally, the TiO2 (0:5) hybrid nanofluid exhibited enhancements of 0.61% and 2.3% for pressure drop and Prandtl number, respectively. The developed hybrid nanofluids enhanced the performance of the heat exchanger when used as a cold fluid.

A Novel Correlation for Predicting the Dynamic Viscosity of Nanofluids based on MWCNTs

A Novel Correlation for Predicting the Dynamic Viscosity of Nanofluids based on MWCNTs

Thermocapillarity in Cross Hybrid Nanofilm Flow Past an Unsteady Stretching Sheet

The present work is highly interested in examining the transport phenomena of the thin Cross hybrid nanofluid film flow over a continuously stretching surface. The proposed thin film flow study elucidates the film extrusion process, which is prominent in the packaging industry. With the intention of improvising the quality of the coating process, the thermocapillarity and injection effects have been probed in the present model. A suitable similarity transformation and the MATLAB software aid in producing accurate numerical solutions. The accumulated numerical results indicate that an increment in the hybrid nanofluid viscosity and surface tension intensity reduces the wall shear stress past the permeable stretching sheet and improves the heat transfer rate. Remarkably, negative film thickness has been identified when the unsteadiness parameter is greater than or equal to 0.9 while the thermocapillarity parameter falls within the range of 0 and 0.6.

Entropy generation analysis in MHD hybrid nanofluid flow: Effect of thermal radiation and chemical reaction

The current study computationally investigates the thermally radiative incompressible flow of hybrid nanofluid induced by a radially stretchable rotating disk with entropy generation analysis. The temperature-dependent viscosity of hybrid nanofluid, stretching of the disk, chemical reaction, Joule, and viscous dissipation effects are incorporated in the formulation of the mathematical flow model. In this study, Karman’s transformations are used to convert the governing equations of partial derivative form into their ordinary derivative form, which is evaluated numerically together with boundary conditions using the BVP Midrich technique built-in Maple software. The impacts of variable viscosity parameter, magnetic field parameter, radiation parameter, Eckert number, Prandtl number, Biot Number, and chemical reaction parameter on flow-field factors and entropy generation rate are presented graphically. The outcomes indicate that higher estimations of magnetic field parameter and entropy generation rate increase the velocity distribution. Also, an increment in Schmidt number and chemical reaction parameter diminishes hybrid fluid concentration, and a higher magnetic field parameter maximizes the same. This study is significant in industrial thermal management and heat transport increment in renewable energy systems.