Viscosity Of Base Fluid Research Articles

A new empirical correlating equation for calculating effective viscosity of nanofluids

AbstractIn this paper, an empirical correlation for the nanofluid viscosity is proposed. The new equation for the nanofluid effective dynamic viscosity, normalized by the dynamic viscosity of the base liquid, is derived from a wide selection of experimental data available in the literature. This correlation presented the viscosity of the nanofluid as a function of the base fluid viscosity, nanoparticle volume fraction, nanoparticle diameter, nanoparticle temperature, and mass density of the base fluid. The new correlation was evaluated against 898 experimental data for the viscosities of nanofluid collected from the literature. The experimental data included different working nanofluids, such as alumina, Iron, and silica, where the diameter of nanoparticles was ranging between 10 and 350 nm, suspended in water, propylene glycol, and kerosene. The predicted results were then compared with many other published experimental results for different nanofluids and very good concordance between these results was observed. In general, this correlation has higher accuracy and precision.

Dissipative particle dynamics simulation of magnetorheological fluids in shear flow

In the present study, the behavior of a magnetorheological fluid (MRF) in shear flow is investigated using dissipative particle dynamics (DPD), a particle-based method. The solid particles in the MRF are modeled by a magnetization model, and Lennard-Jones potential is used to simulate the interaction between the solid and the liquid phases. The columnar structure (chain) of the solid particles along the applied magnetic field direction is modeled that is similar to previous experimental observations. Subsequently, this complex fluid is simulated under shear flow and the velocity profiles depict the “plug region”. The effect of shear rate and area fraction on the rheological properties is investigated by Irving–Kirkwood model. The DPD simulation results indicate a shear-thinning and viscoplastic behavior of MRF which is similar to experimental reports. Following, the effect of magnetization of solid particles is also studied and it is shown that the MR effect is significantly dependent to the solid particle magnetism. Furthermore, the results show that the viscosity of base fluid has a considerable effect on the dynamic range of this smart fluid.

Rheological Behavior of Water-Ethylene Glycol Based Graphene Oxide Nanofluids

Traditionally water-ethylene glycol mixture based nanofluids are used in cold regions as a coolant in the car radiators. In the present study, the rheological properties of water-ethylene glycol based graphene oxide nanofluid are studied using Non-Equilibrium Molecular Dynamics (NEMD) method at different temperatures, volume concentrations, and shear rates. NEMD simulations are performed with considering 75/25, 60/40, and 40/60 ratios of water/ethylene glycol as the base fluids at volume concentrations of 3%, 4%, and 5% graphene oxide nanosheets. The results, which demonstrated good agreement with experimental data, show that the viscosity and density of base fluids significantly decrease with temperature and increases with ethylene glycol volume fraction. Also, the viscosity and density of nanofluids depends directly on the volume concentrations of nanoparticles and decreases with increasing temperature. For example, at 289.85 K, the viscosity of water (75%)-ethylene glycol (25%) based nanofluids containing 3%, 4% and 5% volume concentrations of nanoparticles increased by 33%, 43%, and 56%, respectively. Similarly, the density of the same nanofluids increased by 1%, 1.7 %, and 2.2%, respectively. Moreover, the theoretical models confirm the obtained results. According to the shear rate analysis, the water-ethylene glycol based graphene oxide nanofluid behaves as a non-Newtonian fluid.

Experimental Investigation on the Compatibility of Nanoparticles with Vegetable Oils for Nanofluid Minimum Quantity Lubrication Machining

Several health and environmental related issues caused by the application of traditional cutting fluids in machining can be solved by implementing eco-friendly technologies such as minimum quantity lubrication (MQL). Moreover, nanofluid MQL has been proposed to enhance the cooling/lubricating properties of pure MQL and displays significantly good results for machinability. However, the mechanism on compatibility of nanoparticles with cutting fluids has not been explored. In this study, nanoparticles with different hardness and vegetable oils with different viscosity were selected for nanofluids preparation. The end milling experiments were carried out on 7050 material by applying MQL with particularly prepared nanofluids. The cutting force and surface roughness were measured corresponding to the machining performance. The compatibility of hardness of nanoparticles with viscosity of base fluids has been evaluated, and the mechanism has been analyzed by new-designed tribology tests. Results show that canola oil-based diamond nanofluids MQL exhibit the lowest cutting force and natural77 oil-based diamond nanofluids perform the lowest surface roughness with reduction of 10.71 and 14.92%, respectively, compared to dry machining condition. The research is novel and contributes to the machining of such materials at the industry level.

Application of artificial neural networks for viscosity of crude oil-based nanofluids containing oxides nanoparticles

In this research, the effect of general performances of radial basis function (RBF) method of artificial neural networks (ANN) with laboratory data on NiO, WO3, TiO2, ZnO and FeO3 nanoparticles in different temperatures and mass fractions on viscosity of crude oil were studied. The morphology and nanoparticles stability were analyzed with the DLS and TEM analysis. The results showed that the average nanoparticles diameter ranged from 10 to 40 nm for different oxide nanoparticles. For learning RBF networks, the major method for calculating isotropic Gaussian basis functions span for RBF networks containing special algorithm were presented. The results declared RBF neural networks had an acceptable performance because of having strong academic basic and ability of filtering the noises. This method contain all the experimental data perfectly, and provides the possibility of using different mass fractions and temperatures and simulated charts for knowing the information about the viscosity. For TiO2, ZnO and FeO3 nanoparticles, adding small amounts of nanoparticles decreased the relative viscosity comparing to the base fluid viscosity. But for WO3 and NiO nanoparticles the viscosity of nanofluids was higher than base fluid with any mass fractions.

An experimental study on rheological behavior of a nanofluid containing oxide nanoparticle and proposing a new correlation

In this paper, the nanofluid dynamic viscosity composed of CeO2- Ethylene Glycol is examined within 25–50 °C with 5 °C intervals and at six volume fractions (0.05, 0.1, 0.2, 0.4, 0.8 and 1.2%) experimentally. The nanofluid was exposed to ultrasound waves for various durations to study the effect of this parameter on dynamic viscosity of the fluid. We found that at a constant temperature, nanofluid viscosity increases with increases in the volume fraction of the nanoparticles. Also, at a given volume fraction, nanofluid viscosity decreases when temperature is increased. Maximum increase in nanofluid viscosity compared to the base fluid viscosity occurs at 25 °C and volume fraction of 1.2%. It can be inferred that the obtained mathematical relationship is a suitable predicting model for estimating dynamic viscosity of CeO2- Ethylene Glycol (EG) at different volume fractions and temperatures and its results are consistent to laboratory results in the set volume fraction and temperature ranges.

A Clot Model Examination: with Impulsion of Nanoparticles under Influence of Variable Viscosity and Slip Effects

In this speculative analysis, our main focused is to address the neurotic condition that occurs due to accumulation of blood components on the wall of the artery that results in blood coagulation. Specifically, to perceive this phenomena clot model is considered. To discuss this analysis mathematical model is formed in the presence of the effective thermal conductivity and variable viscosity of base fluid. Appropriate slip conditions are employed to obtain the close form solutions of temperature and velocity profile. The graphical illustrations have been presented for the assessment of pressure rise, pressure gradient and velocity profile. The effects of several parameters on the flow quantities for theoretical observation are investigated. At the end, the results confirmed that the impulsion of copper and silver nanoparticles as drug agent enlarges the amplitude of the velocity and hence nanoparticles play an important role in engineering and biomedical applications such as drug delivery system.

Water driven Cu nanoparticles between two concentric ducts with oscillatory pressure gradient

Current study is devoted to examine the magneto-hydrodynamics (MHD) flow of water based Cu Nanoparticles with oscillatory pressure gradient between two concentric cylinders. Arrived broad, it is perceived that the inclusion of nanoparticles has increased considerably the heat transfer near the surface of both laminar and turbulent regimes. Mathematical model is constructed in the form of partial differential equations which contains the effective thermal conductivity and viscosity of base fluid and nanoparticles. Close form solution is attained corresponding to the momentum and energy equation and results are evaluated for velocity, temperature and pressure gradient in the restricted domain. Graphical results for numerical values of the flow control parameters: Hartmann number M, Reynolds number Reω, the solid volume fraction of nanoparticles ϕ and the pulsation parameter based on the periodic pressure gradient have been presented for the pressure difference, frictional forces, velocity profile, temperature profile, and vorticity phenomena have been discussed. The assets of various parameters on the flow quantities of observation are investigated. To the same degree a concluding crux, the streamlines are examined and plotted. The results confirmed that the velocity and temperature may be controlled with the aid of the outside magnetic field and due to the growth in the nanoparticles and considerable enhancement in the heat transfer rate can be found by adding/removing the strength of magnetic field and nanoparticle volume fraction.

Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN)

Viscosity is a significant physical property of nanofluids in practical heat transfer applications. No general model is capable for accurate prediction of nanofluid viscosity in a broad range of effective parameters. In the present work, 1490 experimental data points on relative viscosity of different nanofluids have been collected by comprehensive literature search. Then, a feed-forward back-propagation multilayer perceptron artificial neural network was developed and tested via employing Levenberg–Marquardt training algorithm in order to predict nanofluid viscosity in broad ranges of operating parameters. The model input parameters are temperature, nanoparticle size, density, volume fraction, and base fluid viscosity. Regression statistical analysis results considering training and test data (R2=0.99998), comparison of the ANN predicted values with corresponding experimental data (average absolute relative deviation (AARD)=0.41%, and maximum average relative deviation (ARD)=6.44 %) and some available theoretical models have revealed high prediction capability of the developed neural network.

Experimental studies on the viscosity of Fe nanoparticles dispersed in ethylene glycol and water mixture

In this paper, experimental studies are conducted in order to measure the viscosity of Fe nanoparticles dispersed in various weight concentration (25/75%, 45/55% and 55/45%) of ethylene glycol and water (EG-water) mixture. The experimental measurements are performed at various volume concentrations up to 2% and temperature ranging from 10?C to 60?C. The experimental results disclose that the viscosity of nanofluids increases with increase in Fe particle volume fraction, and decreases with increase in temperature. Maximum enhancement in viscosity of nanofluids is 2.14 times for 55/45% EG-water based nanofluid at 2% volume concentration compared to the base fluid. Moreover, some comparisons between experimental results and theoretical models are drawn. It is also observed that the prior theoretical models do not estimate the viscosity of nanofluid accurately. Finally, a new empirical correlation is proposed to predict the viscosity of nanofluids as a function of volume concentration, temperature, and the viscosity of base fluid.

Influence of Components on the Rheological Property of Shear Thickening Polishing Slurry

Shear thickening polishing (STP) method was newly developed to achieve high efficient and high quality finishing of complex curved surface. The shear thickening fluid based slurry is one of the key factors in STP process. Viscosity of different shear thickening polishing slurry (STPS) was tested by rheometer in this study. The influences of dispersed particle size and concentration, abrasive material, abrasive particle size and concentration on the rheological property of STPS were analyzed. The results show that smaller dispersed particle (5.5 or 13μm in this study) and relative higher concentration (50-55 wt.%) are better for shear thickening effect of the base fluid. The viscosity of base fluid increases from 0.15-0.3 Pa·s to 0.8-1.1 Pa·s under high shear rate. The participation of Al2O3 and diamond abrasive changes the rheological property little, and the viscosity of STPS reaches the highest value 1.8 Pa·s at shear rate 300 s-1. But SiC abrasive obviously destroys the shear thickening effect. SPTS with different Al2O3 abrasive concentration in this study presents almost same viscosity curve. It is inferred that the number of the abrasive particle but not the weight ratio plays the role to effect the rheological property of STPS.

Viscosity of carbon nanotube suspension using artificial neural networks with principal component analysis

This paper applies the model including back-propagation network (BPN) and principal component analysis (PCA) to estimate the effective viscosity of carbon nanotubes suspension. The effective viscosities of multiwall carbon nanotubes suspension are examined as a function of the temperature, nanoparticle volume fraction, effective length of nanoparticle and the viscosity of base fluids using artificial neural network. The obtained results by BPN–PCA model have good agreement with the experimental data.

Experimental investigation on the correlation between nano-fluid characteristics and thermal properties of Al2O3 nano-particles dispersed in ethylene glycol–water mixture

This paper presents a multi-parameter investigation of Al2O3 nano-particles dispersed in distilled water, ethylene glycol and ethylene glycol–distilled water mixture (50/50vol.%). The nano-fluid samples were prepared with ultrasonic processing with particle concentration up to 9wt.%. Samples were characterized by transmission electron microscope imaging, particle surface charge (zeta potential), particle aggregate size, light absorbance and pH. The thermal conductivity and viscosity of the nano-fluid samples were measured. 10.9% enhancement in thermal conductivity was observed in ethylene glycol–distilled water mixture based nano-fluid with 5wt.% Al2O3 nano-particle loading. The results show multiple-perspective correlations between nano-fluid characteristics and thermal properties. The findings are summarized as follows. Combined effect of base fluid viscosity and particle surface charge determines the degree of nano-particle aggregation. Higher enhancement in thermal conductivity is observed with moderate particle aggregation, while either light or heavy aggregation lowers the enhancement. The ethylene glycol–water mixture based nano-fluids show overall higher thermal conductivity enhancement than the distilled water and ethylene glycol based nano-fluids. The degradation of nano-fluid thermal conductivity enhancement over time is found to be related to the gravity-driven particle sedimentation. Nano-fluids containing larger particle aggregates show higher viscosity augmentation. However, the extent of viscosity augmentation is not proportional to the extent of particle aggregation.

Influence of Components on the Rheological Property of Shear Thickening Polishing Slurry

Shear thickening polishing (STP) method was newly developed to achieve high efficient and high quality finishing of complex curved surface. The shear thickening fluid based slurry is one of the key factors in STP process. Viscosity of different shear thickening polishing slurry (STPS) was tested by rheometer in this study. The influences of dispersed particle size and concentration, abrasive material, abrasive particle size and concentration on the rheological property of STPS were analyzed. The results show that smaller dispersed particle (5.5 or 13μm in this study) and relative higher concentration (50-55 wt.%) are better for shear thickening effect of the base fluid. The viscosity of base fluid increases from 0.15-0.3 Pa·s to 0.8-1.1 Pa·s under high shear rate. The participation of Al2O3 and diamond abrasive changes the rheological property little, and the viscosity of STPS reaches the highest value 1.8 Pa·s at shear rate 300 s-1. But SiC abrasive obviously destroys the shear thickening effect. SPTS with different Al2O3 abrasive concentration in this study presents almost same viscosity curve. It is inferred that the number of the abrasive particle but not the weight ratio plays the role to effect the rheological property of STPS.

Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks

Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable.

Study on the Effect of Nanoparticle Loadings in Base Fluids for Improvement of Drilling Fluid Properties

Nanotechnology is increasingly capturing the attention of material researchers as this technology pushes the limits and boundaries of the pure material itself. Liquids dispersed with nanoparticles generally have higher physical properties enhancements. In the current study, ball-milled functionalized –COOH carbon nanoparticles were introduced into the targeted base fluid of drilling mud. The investigating parameter involved in this study is carbon nanoparticle loadings, ranging from 0 wt% to 1.0 wt%, which is readily dispersed in the base fluid. The method of dispersion chosen is through indirect dispersion in ultrasonic bath. The effect and significance of the investigating parameters were studied based on the desired physical properties of ideal base fluids for drilling muds, mainly thermal conductivities and viscosity of the fluid. The conditions for dispersion in this study were ball-milled functionalized –COOH carbon nanoparticle size at an average size of 10 μm with 90 minutes of indirect ultrasonic dispersion. Result shows that addition of functionalized nanoparticles into base fluids yields as much as 6% of thermal conductivity enhancement while approaching viscosity of pure base fluid at higher shear rate.

Computational Investigation of Heat Transfer of Nanofluids in Domestic Water Heat Exchanger

Recent development of nanotechnology led to the concept of using suspended nanoparticles in the heat transfer fluids to improve the heat transfer properties of the base fluids. The heat transfer enhancement by nanofluids is the significant concerns in the efficiency of domestic water heat exchanger system. A computational investigation of the heat transfer in a domestic water heat exchanger is conducted on the water and water-based nanofluids. Copper (Cu) nanoparticle and Alumina (Al2O3) nanoparticle are selected in the water-based nanofluids. Volume fraction of nanoparticle in the nanofluids is set at 0.5 %, 1.0 %, 1.5 %, 2.0 %, 2.5 %, and 3.0 %. Heat exchanger has been invented for the heat transfer from one medium to another medium in many heat transfer systems. Domestic water heat exchanger can be used in a heat pump domestic water heating system. The density, the thermal conductivity, and the dynamic viscosity of the water base fluid are increased while the specific heat capacity of the water base fluid is reduced with the addition of copper as well as alumina nanoparticle. Addition of copper nanoparticle into the water-based heat transfer fluid significantly increases the domestic hot water temperature. The efficiency of domestic water heat exchanger system is optimum when 1.5 % copper or alumina nanoparticle is added into the water-based heat transfer fluid.

Thermal Conductivity of Nanofluids

Enhanced thermal conductivity of nanofluids compared to that of the base fluid has received attention of many researchers in the last one decade. Experimental data on thermal conductivity of nanofluids using varied nanoparticles in the size range 10-100 nm have been reported. However, there is lot of variance in the data and needs critical analysis. Many models have been proposed by various research groups for predicting the thermal conductivity of nanofluids. Due to complexity of various parameters involved (size, % volume fraction, specific surface area and the type of nano particles, pH of nano fluid, thermal conductivity and viscosity of base fluid) no single model can be used for predicting the thermal conductivity of nanofluids. Inconsistent and conflicting results are reported on the enhanced thermal conductivity of nanofluids. Further, insufficient understanding and inconclusive mechanism behind enhanced thermal conductivity requires further attempt to work in this field. This article critically reviews the available literature on thermal conductivity of nanofluids.

Experimental study on the characteristics of thermal conductivity and shear viscosity of viscoelastic-fluid-based nanofluids containing multiwalled carbon nanotubes

In order to obtain a novel thermo-fluid with both turbulent drag reducing and heat transfer enhancement (compared with drag-reduced flow) abilities, we have prepared a viscoelastic-fluid-based nanofluid (VFBN) using viscoelastic aqueous solution of cetyltrimethyl ammonium chloride/sodium salicylate as base fluid and multiwalled carbon nanotubes (MWCNTs) as nanoparticles. The thermal conductivity and shear viscosity of the prepared VFBN with various particle volume fractions, temperatures and concentrations of the base fluid were then experimentally investigated. The results show that thermal conductivities of the tested VFBNs are significantly higher than that of the corresponding base fluid and increase with increasing particle volume fraction and fluid temperature, demonstrating potentials in heat transfer enhancement. A modified Li–Qu–Feng model (Y.H. Li, W. Qu, J.C. Feng, Chinese Phys. Lett. 25 (2008) 3319–3322), which includes the effect of liquid layering, particle clustering, particle shape factor, Brownian motion and viscosity of base fluid, is proposed in the present paper to predict thermal conductivity of VFBNs containing MWCNTs. The results predicted by this modified Li–Qu–Feng model show excellent agreements with the measured data. The VFBN with MWCNTs shows a non-Newtonian fluid behavior in its shear viscosity, and its shear viscosity increases with the increase of particle volume fraction and decrease of temperature. It is expectable that the prepared VFBNs may also have drag-reducing ability in turbulent flows.

Influence of the uniform electric field on viscosity of magnetic nanofluid (Fe3O4-EG)

Viscosity of Fe3O4/ethylene glycol nanofluids under electric field (ac and dc) was investigated experimentally. Magnetic nanofluids were prepared by dispersing Fe3O4 nanoparticles in ethylene glycol using a sonicator. Experiments showed that dilute magnetic nanofluids (<0.05 vol. %) as well as base fluid exhibit Newtonian behavior. Viscosity of Fe3O4 / ethylene glycol nanofluids in electric field was measured using capillary tube viscometer. Electric field decreased the viscosity of magnetic nanofluids and base fluid. The viscosity reduction was more profound in higher volume concentrations of nanoparticles. dc electric field caused greater viscosity reduction in magnetic nanofluids relative to ac electric field while ac electric field showed greater reduction effect for base liquid.