Viscosity Of Nanofluids Research Articles

Numerical simulation of Electrokinetically Driven Peristaltic Pumping of Silver-Water Nanofluids in an asymmetric microchannel

This article intends to focus on the theoretical and numerical investigation of the peristaltic pumping of water-based silver nanofluid in the presence of electroosmotic forces. The investigation is carried out in an asymmetric microchannel subject to the influence of mixed convection and viscous dissipation. No-slip boundary conditions for velocity, temperature, and nanoparticle volume fraction are imposed on channel walls. The lubrication approach is utilized to simplify the normalized constitutive equations. The distribution of electric potential in the electric double layer is characterized by Poisson-Boltzmann ionic distribution which is further linearized by Debye-Hückel approximation. Nanofluid properties are predicted by a combination of the Buongiorno two-phase mixture model and homogeneous flow model. Additionally, the effective thermal conductivity and dynamic viscosity of silver-water nanofluid are characterized by the Corcione model. Silver nanoparticles of 20nm diameter are utilized in this suspension. The transformed set of nonlinear and coupled equations is numerically executed for axial velocity, temperature, and nanoparticle volume fraction by employing the mathematical software Maple 17. Pumping and trapping phenomena are also investigated. A comparison between the thermal conductivity of nanofluid predicted by the Corcione model and the Maxwell model is further presented. The influence of various flow parameters is outlined through graphical results. It has been observed that the thermal conductivity of silver-water nanofluid enhances with increasing nanoparticle volume fraction and temperature but decreases for larger sized nanoparticles. Moreover, the heat transfer rate rises significantly when smaller silver nanoparticles are suspended in water. Furthermore, the temperature of nanofluid is directly related to the Debye length parameter and the Helmholtz- Smoluchowski velocity parameter.

Heat capacity and viscosity of ternary carbonate nanofluids

SummaryRecent studies have shown that eutectic salt mixtures show remarkable enhancement in heat capacity after nanoparticles are dispersed at small concentrations. The exact mechanism behind these heat capacity enhancements is still inconclusive. However, recent studies proposed that the enhancement could be associated with nucleation and grain growth of salt dendritic structures. To investigate the hypothesis, we synthesized several samples of ternary carbonate salt mixture doped with 1% alumina nanoparticles and thermally cycled them at various heating rates and cooled them back to the solid‐state during the synthesis procedure. It can affect the nucleation/grain growth of salt dendritic nanostructures and, as a result, there can be different heat capacity enhancements. A differential scanning calorimetry was employed to characterize the heat capacity values of the systems. It was observed that the heat capacity enhancements decreased with increases in the heating rates. The highest heat capacity enhancement was observed at the lowest heating rate (ie, 2°C/min). A transmission electron microscope was employed to confirm the effect of heating cycling rates on the formation of dendritic structures. Moreover, pH variation method was used to study the effect of the dendritic structures on the heat capacity of the mixture. Furthermore, a rheometer was employed to characterize the rheological behavior. It was observed that nanofluid samples showed shear‐thinning behavior, whereas shear thinning was not observed in pure and nanofluid prepared with the pH variation method.

Exponentially reduced carrier mobility of natural ester via blocking effect of 2D hexagonal boron nitride nanosheets

In this work, the boron nitride (BN) nanosheets were dispersed in natural esters to fabricate the dielectric nanofluid. Microstructures and chemical compositions of the nanosized BN are determined. The stability, viscosity, and thermal conductivity of the BN nanofluid, were obtained. And the dissipation factor, electrical conductivity, and relative permittivity of the BN nanofluid, were measured. The ion mobilities and migrating times of the nanofluid were measured under different volumetric fractions of BN nanosheets and temperatures. Results show that the electrical conductivity and dissipation factor of the nanofluid decreased by 54% and 48% with the addition of only 0.1% of BN nanosheets under 110°C. Both the half-reduced carrier mobility of the nanofluid and the declined ion concentration by blocking of BN nanosheet contributed to the exponentially reduced electrical conductivity and enhanced dielectric performances of the BN nanofluid. The suppression coefficient k is proposed to quantitatively describe the hinder effect of ion migration in nanofluids by 2D BN nanosheet. Results provide a strategy to design and develop advanced nanofluids with low-dielectric loss by aids of surface adsorption of ion and steric hindrance of 2D nanosheets.

Synthesis of Al2O3–SiO2/water hybrid nanofluids and effects of surfactant toward dispersion and stability

In this work, a series of Al2O3–SiO2/water hybrid nanofluid with different volume fractions and surfactant types were prepared. The Al2O3 and SiO2 were synthesized using the sol-gel method, and ultrasonic vibration was used to disperse the powders into the DI water to form nanofluids. The effects of anionic (SDS), cationic (CTAB) and nonionic (PVP) surfactants toward the stability, electrical conductivity, and viscosity of the nanofluid were then investigated. Zeta potential analysis, spectral analysis method, sedimentation method, and centrifugation method were used to compare the stability of the nanofluids. The obtained results indicated that the surfactant played an important role in dispersing the nanoparticles (NPs) and forming stable nanofluids. Nanofluids with low concentration of Al2O3 showed good dispersion in water, but it tended to aggregate when the amount used was higher. The viscosity of nanofluids increased slightly when the surfactant was added due to the increase of solid-phase concentration in the nanofluids. SDS positively affected the dispersion and stability of the nanofluids, and the best ratio of Al2O3:SiO2 was at 30:70. The electrical conductivity increased with temperature, and nanofluid containing CTAB and SDS had a higher increment in conductivity.

Nanofluids for Performance Improvement of Heavy Machinery Journal Bearings: A Simulation Study.

Nanofluids have extensive applications in hydrodynamic journal bearings used in heavy industry machinery. Inorganic fullerene-like tungsten disulfide nanoparticles (IF-WS2 NPs) are the most common additive for lubrication purpose due to their excellent mechanical characteristics along with their effect on reducing friction and wear. In this work, a computational simulation approach with discrete phase modeling (DPM) of suspended nanoparticles was used to evaluate the application of the IF-WS2 nanofluid lubricant on load carrying capacity of high-load journal bearings where the normal loads are high, considering the bearing dimensions. For accurate simulation, nanofluid viscosity was calculated considering the aggregation effect of NPs by using scanning electron microscopy (SEM) imaging of the nanofluids. A benchmark study was first performed to assess the model accuracy. Hydrodynamic lubrication was simulated under different nanofluid weigh fractions. The simulated pressure distribution was then employed to determine the load capacity of the bearing. The results show an approximately 20% improvement of load carrying capacity at 5% weight fraction of WS2-oil nanofluid.

Nanofluids based on molten carbonate salts for high-temperature thermal energy storage: Thermophysical properties, stability, compatibility and life cycle analysis

Molten salts-based nanofluids have been widely considered for Thermal Energy Storage (TES) applications due to their enhanced thermophysical properties. However, the application of such fluids faces many challenges, among which are the correct determination of their properties, stability, compatibility with construction materials and the overall environmental impact. In this work, we attempt to provide a comprehensive analysis of nanofluids based on nano-alumina and molten carbonate salt for the benefit of next-generation high-temperature TES applications. In particular, considerable statistics, cross-verification, novel preparation and characterization methods were applied to record ~12% increase of thermal conductivity, ~7% increase of heat capacity and ~35% increase of viscosity. It was demonstrated that such nanofluids have poor dispersion stability under static conditions; however, the enhanced thermophysical properties can be maintained by mechanical stimuli, e.g. mixing or redistribution. We show that some nanoparticles interact with typical construction materials such as stainless steel 310 by forming mixed oxides and considerably reducing the corrosion rates. An erosion study has been performed demonstrating negligible effect of nanoparticles even in the case of their strong agglomeration. Finally, life cycle analysis revealed that viscosity and preparation method of such nanofluids must be targeted to minimize the environmental impact.

Effect of C60-SiO2 hybrid nanoparticles on thermophysical and tribological properties of a multigrade engine oil: an experimental study

In this study, the thermophysical and tribological properties of a hybrid nanofluid containing fullerene-silica nanoparticles in a multigrade engine oil have been investigated. The nanoparticles were suspended in SAE 5W30 engine oil with a volume fraction of 0.1 to 1% using the two-step method. The results showed that thermal conductivity of nanofluid increased by approximately 9% at 1% volume fraction, and at ambient temperature, rather than the base oil. Investigation of shear stress variations for base fluid and nanofluids at different concentrations revealed that the rheological behavior of the nano-oil is non-Newtonian and shear thinning. Also, the results represented that by increasing the concentration, the nanofluid dynamic viscosity had an erratic trend. The highest increase was 14% and occurred at 1% volume fraction and 5 °C. Also, the behavior of nanofluids against temperature was in accordance with the conventional fluids. Besides, viscosity decreased after the enhancement of temperature in all volume fractions. The wear test was carried out using a pin-on-disk device based on the ASTMG99 standard. The results revealed that the wear rate decreased by 18% in comparison with the base oil at concentration of 1% and temperature of 25 °C. Finally, in order to predict the viscosity of nanofluid, a precise equation was presented based on the experimental data in terms of three variables of temperature, volume fraction and shear rate by using response surface methodology (RSM). Two-degree polynomial function with R2 = 0.9983 has been employed as the optimal function to model the viscosity of nanolubricant.

Effect of ZnO Nanoparticles on the Thermo-Physical Properties and Heat Transfer of Nano-Fluid Flows

In this research, an improvement in alternative heat transfer and thermal properties of ZnO nanoparticles (NPs)/ distilled water, DW nanofluid are experimentally studied and investigated. The two-step approach is utilized to prepare the ZnO NPs/water nanofluid using different concentrations and different inlet temperatures. Scanning Electron Microscopy (SEM) and UV–visible spectrophotometer is employed to characterize the nanofluid. Varying flow rates and the temperatures are examined in the heat exchanger's horizontial tube and shell pipe as well as the impacts of ZnO nanoparticles and the temperatures are studied on nanofluid's thermal conductivity and viscosity. The findings show improvements in the thermal conductivity of ZnO NPs / DW nanofluid. A maximum value of 6.67% of the thermal conductivity is achieved at a temperature of 343K using 1 % ZnO NPs. Nusselt Number also shown an improvement of 38% at a temperature of 343K using 0.2% wt of ZnO NPs under the turbulent condition (Reynolds Number range of 8000 to 20000). Experimental results are compared with previous correlations and an acceptable agreement is observed.

Effect of TiO2-Al2O3 nanoparticle mixing ratio on the thermal conductivity, rheological properties, and dynamic viscosity of water-based hybrid nanofluid

Many researchers in the past decade have focused on the influence of nanoparticle volume concentration on the thermophysical properties of hybrid nanofluid. However, less research is available on the effect of the mixture composition between two different nanoparticles to the thermophysical properties of the fluid. This study means to research the influence of TiO2-Al2O3 nanoparticle mixing ratio on the thermal conductivity, rheological properties and dynamic viscosity of water-based hybrid nanofluid. The water-based hybrid nanofluid was prepared using five different proportion of TiO2 and Al2O3 nanoparticles at a fixed 1.0% volume concentration. The temperature range selected for the experimental test procedure is between 30−70 °C. The mean crystallite size of TiO2 and Al2O3 nanoparticles calculated using the results from XRD characterization is about 19.1 and 8.31 nm, respectively. The highest thermal conductivity value of 1.134 W/m K is observed for hybrid nanofluid with the nanoparticle mixing ratio of 50:50 at 70 °C, with an average thermal conductivity augmentation of 71% comparative to deionized (DI) water. The developed hybrid nanofluid found to exhibit Newtonian flow behaviour at all nanoparticle mixing ratios and temperature. Moreover, the hybrid nanofluid with 80:20 ratio recorded the highest dynamic viscosity of 1.98 mpa s. Besides, the prepared hybrid nanofluid was found to achieve moderate stability with zeta potential value of 29.6 mV. Finally, two different correlation model was developed using regression analysis to predict the thermal conductivity and relative dynamic viscosity for various TiO2-Al2O3 water-based hybrid nanofluid.

Application of Artificial Neural Networks (ANNs) for Predicting the Viscosity of Tungsten Oxide (WO3)-MWCNTs/Engine Oil Hybrid Nanofluid

In the current work, the viscosity of hybrid WO3-MWCNTs/Engine Oil nanofluids for the different volume fraction of nanoparticles (φ), temperatures, and shear rates has been measured, and then using Artificial Neural Network (ANN) has been predicted. The statistical reports of ANN have been presented in figures and tables, and it could be found that the designed ANN has an acceptable accuracy in predicting the viscosity of hybrid nanofluids. It can be seen that the optimum neuron number for this data set is 39 and the ANN outputs are very close to the experimental data points. Finally, it is found that in the fitting method, the correlation between experimental data points and the output values is 0.999 424, but in the ANN method, the correlation was 0.999 837 454.

Enhancing Thermophysical Characteristics and Heat Transfer Potential of TiO2/Water Nanofluid

The present study investigates the ability to improve the stability and heat transfer performance of coolants by dispersing TiO2 nanoparticles in water. Surfactant-free nanofluid with 5 different weight fractions of 1 %, 0.5 %, 0.1 %, 0.05 %, and 0.01 % were prepared by two-step method. The stability of nanofluid was examined. The thermal conductivity and viscosity of TiO2/water nanofluid of 5 concentrations were determined over the temperatures ranging from 25 °C to 60 °C for every 5 °C interval. New correlations (with R2 = 99 %) based on the experimental data of thermal conductivity and viscosity of nanofluids has been presented. Several performance criteria were determined to investigate the heat transfer and pumping power performance of nanofluid. TiO2/water nanofluid showed some interesting results and enhanced heat transfer capability which enables it as a potential candidate for the future cooling medium.

Experimental Measurement of Thermophysical Properties of Alumina- MWCNTs/Salt–Water Hybrid Nanofluids

Background: Hybrid nanofluids are considered as an extension of conventional nanofluids which are prepared through suspending two or more nanoparticles in the base fluids. Previous studies on hybrid nanofluids have measured their thermal conductivity overlooking other thermophysical properties such as viscosity and electrical conductivity. Objective: An experimental investigation is undertaken to measure thermal conductivity, viscosity, and electrical conductivity of a hybrid nanofluid prepared through dispersing alumina nanoparticles and multiwall carbon nanotubes in saltwater. These properties are the main important factors that must be assessed before performance analysis for industrial applications. Methods: The experimental data were collected for different values of the nanoparticle volume fraction, temperature, salt concentration, and pH value. Attention was paid to explore the consequences of these parameters on the nanofluid’s properties and to find optimal conditions to achieve the highest value of the thermal conductivity and the lowest values of the electrical conductivity and the viscosity. Results: The results demonstrate that although the impacts of the pH value and the nanoparticle volume fraction on the nanofluid’s thermophysical properties are not monotonic, optimal conditions for each of the properties are reachable. It is found that the inclusion of the salt in the base fluid may not change the thermal conductivity noticeably. However, a considerable reduction in the viscosity and substantial elevation in the electrical conductivity occur with an increase in the salt concentration. Conclusion: With the addition of salt to a base fluid, the thermophysical properties of a nanofluid can be controlled.

Experimental studies on the heat transfer characteristics of alumina/water nanofluid inside a helical coil tube

Laminar convective heat transfer and pressure drop characteristics of Al2O3-water nanofluid were experimentally investigated inside a horizontal Helical-Coil Tube (HCT) under constant heat flux boundary conditions for Dean numbers between 400 and 1600. The experiments have been performed using Al2O3-water nanofluid of three mass concentrations and heat fluxes at different flow rates. Thermal conductivity and viscosity of the fluids are measured experimentally, and a proper agreement was observed with the new model predictions for thermo-physical properties of nanofluids. Based on the experimental results, the thermal conductivity and viscosity of Al2O3-water nanofluid were almost 2.12% and 3.1% higher than water in 1 wt% concentration. According to the observations, the heat transfer coefficients in the fully developed region increases by 6.4%, 19%, and 23.7% under 2283, 3774, and 4975 W/m2 heat fluxes, respectively, at 1 wt% concentration of Al2O3-water nanofluid with respect to pure water. Besides, it has been noticed that pressure drop through the helical coil is increased with adding nanoparticles, enhancement of heat flux, and De number. Finally, based on the thermal performance factor, it has been concluded that using nanofluids in the helical coil is a feasible method for improving energy efficiency.

Thermal conductivity, viscosity and heat transfer process in nanofluids: A critical review

Heat transfer efficiency has always been at the center of attractions for many researchers and industries and demand for higher efficiency methods and materials are increased in the last decades. One of the effective methods to enhance the thermal performance of instruments is using nanofluids as a solid-liquid suspension.  In the present paper, the thermal conductivity, viscosity, and heat transfer process of the nanofluids are discussed. The effect of volume fraction, temperature, particle size, shape, base fluid type, and other factors on thermal conductivity, viscosity, and convective heat transfer coefficient of nanofluids are presented. Also, the preparation methods of nanofluids are exhibited and the stability and the effective factors on the stability of nanofluids are brought in the manuscript. Besides, a summarized number of experimental and mathematical studies about the thermal conductivity, viscosity, and stability of nanofluids are listed, compared, and analyzed. The works about the Nusselt number in fluids and nanofluids are presented in detail to determine the future challenges of nanofluids.

A smart model for prediction of viscosity of nanofluids using deep learning

ABSTRACT A novel approach is presented in this paper to predict the viscosity of nanofluids by developing a deep neural network (DNN) based smart generalized model. This study is conducted with a large experimental dataset containing Al2O3, CuO, SiO2, TiO2, Ag, and Fe2O3 nanoparticles and the DNN model is trained by Nadam optimization technique. This proposed DNN model has the learning capability of non-linearities from a training dataset automatically. The novelty of this study with the advantages of deep learning has been described in this paper. To the best of the author’s knowledge, deep learning-based model was never used to predict viscosity before. The detailed analysis of the performance of this DNN model shows that it performs better than any other existing model and overcomes all of their limitations. Also, it gives an excellent prediction on the unseen data and it takes very less amount of time to train this DNN model comparing to other traditional data-driven models. A sensitivity analysis of this smart model has also be presented. This novel DNN-based smart model can predict the viscosity of nanofluids with the highest level of accuracy with a coefficient determination 0.9999.

EFFECT OF AGGREGATION MORPHOLOGY ON THERMAL CONDUCTIVITY AND VISCOSITY OF Al2O3-CO2 NANOFLUID: A MOLECULAR DYNAMICS APPROACH

CO2 cooling systems are the wave of the future for industrial refrigeration. CO2 refrigeration systems are gaining traction in recent years which involves heat transfer between CO2 and the base fluid. The high viscosity of CO2 is of interest to the oil and gas industry in enhanced oil recovery and well-fracturing applications. A need arises to improve the thermal conductivity and viscosity of CO2 to increase the efficiency of these significant applications. Aggregation of nanoparticles is one of the crucial mechanisms to improve the thermal conductivity and viscosity of nanofluids. Since the aggregation morphology of nanoparticles is unclear so far, we have evaluated the stable configurations of the aggregation of nanoparticles by determining the potential energy of the different configurations system. In this paper, Green-Kubo formalism is used to calculate the mentioned thermo-physical properties of the different aggregated nanofluids. The nanofluid in this study consists of alumina (Al2O3) nanoparticles and CO2 as a base fluid. Results indicate that the enhancement in the thermal conductivity and viscosity of nanofluid is inversely proportional to the potential energy of the system. The results also mark that various morphologies of the aggregated nanoparticles have different enhancements of thermo-physical properties of the nanofluid. This study is conducive for the researchers to perceive the importance and influence of aggregation morphology of nanoparticles and their stability on the thermal conductivity and viscosity of nanofluid.

Convective Heat Transfer Coefficient Model Under Nanofluid Minimum Quantity Lubrication Coupled with Cryogenic Air Grinding Ti–6Al–4V

Under the threat of serious environmental pollution and resource waste, sustainable development and green manufacturing have gradually become a new development trend. A new environmentally sustainable approach, namely, cryogenic air nanofluid minimum quantity lubrication (CNMQL), is proposed considering the unfavorable lubricating characteristic of cryogenic air (CA) and the deficient cooling performance of minimum quantity lubrication (MQL). However, the heat transfer mechanism of vortex tube cold air fraction by CNMQL remains unclear. The cold air fraction of vortex tubes influences the boiling heat transfer state and cooling heat transfer performance of nanofluids during the grinding process. Thus, a convective heat transfer coefficient model was established based on the theory of boiling heat transfer and conduction, and the numerical simulation of finite difference and temperature field in the grinding zone under different vortex tube cold air fractions was conducted. Simulation results demonstrated that the highest temperature initially declines and then rises with increasing cold air fraction. Afterward, this temperature reaches the lowest peak (192.7 °C) when the cold air fraction is 0.35. Experimental verification was conducted with Ti–6Al–4V to verify the convective heat transfer coefficient model. The results concluded that the low specific grinding energy (66.03 J/mm3), high viscosity (267.8 cP), and large contact angle (54.01°) of nanofluids were obtained when the cold air fraction was 0.35. Meanwhile, the lowest temperature of the grinding zone was obtained (183.9 °C). Furthermore, the experimental results were consistent with the theoretical analysis, thereby verifying the reliability of the simulation model.

Investigation of Heat Transfer Characteristics of Al2O3-Water Nanofluids in an Electric Heater

Commonly, heat transfer performance of a nanofluid is enhanced by increasing the mass fraction of nanoparticles due to an improvement of the thermo-physical properties. However, increasing the number of nanoparticles in the base fluid will result in an increase of the nanofluid viscosity. This research aims to investigate natural convective heat transfer for an electric heater filled with various Al2O3-water nanofluids. The effects of various mass fractions of the nanofluid (0.1–2.0 wt.%) on the heating efficiency of the electric heater were analyzed over time, and results indicate that the electric heater with the 1.0% Al2O3-water nanofluid has the highest heating efficiency of the external environment.

Optimizing density, dynamic viscosity, thermal conductivity and specific heat of a hybrid nanofluid obtained experimentally via ANFIS-based model and modern optimization

In this study, rGO/Co3O4 nanocomposite was synthesized, characterized, and then the thermophysical properties were obtained experimentally, after which the experimental data at varying values of temperature and particle loadings was used for optimization purposes. The study was concerned with different values of the controlling parameters. The in-situ/chemical reduction technique was used to synthesize the rGO/Co3O4 nanocomposite and then characterized with x-ray diffraction, transmission electron microscope, and magnetometry. The system was studied at temperature values ranging at 20, 30, 40, 50, and 60 °C and with particle loadings of 0.05%, 0.1%, and 0.2% wt%. The authors in this article have introduced a novel population-based algorithm that is known as Marine Predators Algorithm to obtain the optimal values of the controlling parameters (i.e., temperature and nanofluid mixture percentage) that minimize two controlled variables (i.e., density and viscosity) as well as maximize the other two controlled variables (thermal conductivity and specific heat). The rGO/Co3O4 nanocomposite nanofluid thermal conductivity and viscosity were investigated experimentally, and a maximum increment of 19.14% and 70.83% with 0.2% particle loadings at 60 °C was obtained. At 0.05%, 0.1%, and 0.2% particle loading wt%, the density increased by 0.115%, 0.23%, and 0.451% at a temperature of 20 °C; simultaneously, density increased by 0.117%%, 0.235%, and 0.469% at 60 °C, respectively as compared to water. At 0.2 wt%, the maximum decreased specific heat was 0.192% and 0.194% at 20 °C and 60 °C. When compared with water, no effect was observed with an increase in temperature/: a similar trend as that of the water was followed. The optimal values were found to be at a temperature of 60 °C and for 0.05% particle loading of the prepared nanofluid. However, among the conducted experiments, the optimizer pointed out that the optimal experiment was the one conducted at a temperature of 60 °C and a nanofluid percentage at 0.05. In conclusion, the proposed methodology of modelling with an artificial intelligence tool such as an adaptive network-based fuzzy inference system technique and then determining the optimal parameters with the marine predators algorithm accomplished the goal of the study with major success.

Comparative Analysis of Machine Learning Models for Nanofluids Viscosity Assessment.

The process of selecting a nanofluid for a particular application requires determining the thermophysical properties of nanofluid, such as viscosity. However, the experimental measurement of nanofluid viscosity is expensive. Several closed-form formulas for calculating the viscosity have been proposed by scientists based on theoretical and empirical methods, but these methods produce inaccurate results. Recently, a machine learning model based on the combination of seven baselines, which is called the committee machine intelligent system (CMIS), was proposed to predict the viscosity of nanofluids. CMIS was applied on 3144 experimental data of relative viscosity of 42 different nanofluid systems based on five features (temperature, the viscosity of the base fluid, nanoparticle volume fraction, size, and density) and returned an average absolute relative error (AARE) of 4.036% on the test. In this work, eight models (on the same dataset as the one used in CMIS), including two multilayer perceptron (MLP), each with Nesterov accelerated adaptive moment (Nadam) optimizer; two MLP, each with three hidden layers and Adamax optimizer; a support vector regression (SVR) with radial basis function (RBF) kernel; a decision tree (DT); tree-based ensemble models, including random forest (RF) and extra tree (ET), were proposed. The performance of these models at different ranges of input variables was assessed and compared with the ones presented in the literature. Based on our result, all the eight suggested models outperformed the baselines used in the literature, and five of our presented models outperformed the CMIS, where two of them returned an AARE less than 3% on the test data. Besides, the physical validity of models was studied by examining the physically expected trends of nanofluid viscosity due to changing volume fraction.