Viscosity Of Nanolubricants Research Articles

Machine learning-based estimation of nano-lubricants viscosity in different operating conditions

The importance of improving and optimizing the energy transfer systems has been highlighted recently due to ever-increasing demand for energy in the world, and therefore the lubrication of systems has received significant attentions. The improvement of thermophysical properties by nano-particles can be considered as an efficient approach to this end. A comprehensive study dealing with constructing myriad machine learning-based models for predicting the viscosity of nano-lubricants is lacking in the literature. This research, therefore, aims at development of eight different artificial intelligence models including, Adaptive Boosting, Random Forest (RF), Ensemble Learning, Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), and Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) to estimate viscosity of different nano-lubricants as a function of amount of MWCNT, nanoparticle, type of lubricant, temperature, shear rate, and solid volume fraction. These models are prepared and tested based on a databank including 1086 viscosity points. The results show that the MLP and CNN models are the most accurate methods with R-squared of 0.950131 and 0.95035, respectively, while the estimation of computational time expresses that MLP and CNN models require the most computational time for training. Moreover, sensitivity analysis reveals that temperature has the most effect on viscosity of nano-lubricants.

A well-trained artificial neural network for predicting the optimum conditions of MWCNT–ZnO (10:90)/ SAE 40 nano-lubricant at different shear rates, temperatures, and concentration of nanoparticles

Fluid limitation in various industries due to their poor thermal conductivity has led to the improvement of the properties of the base fluid as a new method. With the development of nanofluid research, nanofluids are produced by adding metal nanoparticles and multi-walled carbon nanotubes (MWCNT). Due to the inability of theoretical models to predict the viscosity of nano-lubricants (μnf), mathematical models, especially artificial neural networks (ANNs), investigate the effect of various parameters on the properties of nanofluids and have replaced most of the usual statistical methods. This study investigates the effect of temperature, shear rate (SR), and volume fraction of nanoparticles (φ) on μnf of MWCNT –ZnO (10:90)/ SAE 40 nano-lubricant. Also, a nonlinear polynomial with terms up to power 3 is fitted in the experimental data, and its accuracy is compared to that of ANN in MATLAB. It was proposed that the ANN model has high accuracy (slightly better concerning nonlinear polynomial) for estimating the present study, the μnf of MWCNT–ZnO (10:90)/SAE 40 nano-lubricant. This ANN achieves 0.9995 and 0.00048 values for R2 and MSE, while the nonlinear polynomial showed 0.9983 and 4.0223 values, respectively, for the same parameters, which shows the good training status of the ANN. According to obtained results, the temperature and SR significantly influence the output. The experimental results showed that by increasing the temperature from 25 to 50 ˚C, the μnf of the nano-lubricant decreased from 397.5 to 90.5 cP (atφ = 1 % and SR = 400 rpm). So, the results show that with increasing temperature to 50 ˚C, the viscosity of the nano-lubricant decreases by about 77 %. ​ With increasing SR from 400 to 1000 rpm(at T = 50 ˚C and φ = 1 %), the viscosity of the nano-lubricant decreases from 90.5 to 85.3 cP. On the other hand, φ has a direct but negligible effect on μnf. In other words, the nanoparticle fraction change from 0 to 1 %, changes the μnf from 150 cP to around 200 cP. This model can be used as a design tool in future research or as an objective function in optimization problems.

An experimental investigation and comprehensive modelling of thermal and rheological behaviour of graphene oxide nano platelets suspended mineral oil nano lubricant

Suspension of nano additives in lubricants enhances its thermo-physical behaviour and prolongs the compressor durability in eco-friendly refrigeration systems. Therefore, this study investigates the influence of graphene oxide (GO) nano platelet concentration range of 0.05 – 0.75 vol%, temperature range of 30 – 90 °C and shear rate range of 5.48 s−1 to 190 s−1 on the thermal and rheological behaviour of mineral oil lubricant. Field Emission Scanning Electron Microscopy (FESEM) analysis confirms the platelet structure of GO with a maximum sheet thickness of 5 nm. FT-IR result confirms the pure physical interaction of GO with lubricant. Thermal conductivity and dynamic viscosity of nano lubricants ascended with increasing GO concentration and descended with increasing temperature. The maximum thermal conductivity enhancement of 70% was achieved for the nano lubricant with 0.75 vol% at 30 °C with respect to pure oil. Non - Newtonian shear thinning behaviour was possessed by both pure and nano lubricants at all temperatures. The Artificial Neural Networking (ANN) and mathematical correlation were developed to predict the thermo physical properties of nano lubricants. The maximum R-square value of 0.99998 and 0.9988 was achieved for ANN models of thermal conductivity ratio and relative viscosity of nano lubricants. The ANN model outperforms mathematical correlation by achieving maximum R-square and minimum mean square error (MSE).

Experimental analysis and modeling of viscosity and thermal conductivity of GNPs/SAE 5W40 nanolubricant

PurposeThe purpose of this study is to investigate thermal conductivity and dynamic viscosity of graphene nanoplatelet-based (GNP) nanolubricant.Design/methodology/approachNanolubricants in concentrations of 0.025, 0.05, 0.1 and 0.5 Wt% were prepared by means of two-step method. The stability of nanolubricants was monitored by visual inspection and dynamic light scattering tests. Thermal conductivity and dynamic viscosity of nanolubricants in various temperatures between 25°C–70°C were measured with KD2-Pro analyser device and a rotational viscometer MRC VIS-8, respectively. A comparison between experimentally achieved results and those obtained from existing models was performed. New correlations were proposed and artificial neural network (ANN) model was used for predicting thermal conductivity and dynamic viscosity.FindingsThe designed nanolubricant showed good stability after at least 21 days. Thermal conductivity and dynamic viscosity increased with particles concentration. In addition, as the temperature increased, thermal conductivity increased but dynamic viscosity decreased. Compared to the base oil, maximum enhancements were achieved at 70°C with the concentration of 0.5 Wt.% for dynamic viscosity and at 55°C with the same concentration for thermal conductivity. Besides, ANN results showed better performance than proposed correlations.Practical implicationsThis study outcomes will contribute to enhance thermophysical properties of conventional lubricating oils.Originality/valueTo the best of our knowledge, there is no paper related to experimental study, new correlations and modelling with ANN of thermal conductivity and dynamic viscosity of GNPs/SAE 5W40 nanolubricant in the available literature.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0088/

Analysis of rheological properties of MWCNT/SiO2 hydraulic oil nanolubricants using regression and artificial neural network

In this article, the rheological behavior of MWCNT/SiO2 based nano-hydraulic oil nanolubricant is evaluated using experimental and Artificial Neural Network (ANN) approach. Viscosities of the hybrid nanolubricant samples were measured at temperature and shear rate range of 10–80 °C and 10–200 s−1 respectively. A new regression model is being proposed to predict the dynamic viscosity of nanolubricants. The proposed regression model (R2 0.98338–0.99583) predicts the viscosity of nanolubricants closer to experimental results (least deviation 2.62%). Consistency index (m) and power law index (n) values reveal that nanolubricant samples are non-Newtonian fluid with shear thinning behavior. To improve the accuracy in predicting the viscosity of nanolubricants, the ANN model was designed having input variables among temperature, solid volume fraction and shear rate. In the first phase, temperature and solid volume fraction were taken as input variables, and in the second phase shear rate was introduced as an additional input parameter. The entire data was split into 70:30 proportions for the training and testing phases of the ANN model. The testing results of ANN revealed better accuracy than the proposed correlation in terms of average values of Root Mean Square Error (RMSE) and R2.

Elasto-hydrodynamic analysis of journal bearing operating with nanolubricants

In this study, performance parameters of a rigid and flexible journal bearing using non-Newtonian nanolubricants have been investigated and compared. Krieger-Dougherty viscosity model has been implemented with a non-Newtonian model (power law) to evaluate the effective viscosity of nanolubricants. In order to attain the pressure field of nanolubricants, a numerical solution of the modified Reynolds equation has been derived with initial assumptions and boundary conditions using finite difference method. The static performance parameters for both the bearings have been examined at different values of power law index and eccentricity ratio. The results demonstrate that the load-carrying capacity, maximum pressure and friction force increase significantly using nanolubricant in comparison to base lubricant. Also, the values of these parameters increase with increase of power law index. An insignificant effect of elastic deformation is observed on performance parameters except attitude angle at a particular power law index value.

Rheological and Thermal Properties of Lubricating Oil Enhanced by the Effect of CuO and TiO2 Nano-Additives

The specifications of lubricating oil are fundamentally the final product of materials that have been added for producing the desired properties. In this research, spherical nanoparticles copper oxide (CuO) and titanium oxides (TiO2) are added to SAE 15W40 engine oil to study the thermal conductivity, stability, viscosity of nano-lubricants, which are prepared at different concentrations of 0.1%, 0.2%, 0.5%, and 1% by weight, and also their pour point, and flash point as five quality parameters. The obtained results show that CuO nanoparticles in all cases, give the best functionality and effect on engine oil with respect to TiO2. With 0.1 wt. % concentration, the thermal conductivity of CuO/oil and TiO2/oil increased by 7.27% and 4.54%, respectively. In the same time, the flash point of them increased by 12.62% and 9.3%, respectively in comparison with parent oil.

Mechanism for improvement of friction/wear by using Al2O3 and SiO2/Gear oil nanolubricants

Gear oil of heavy mining machineries are designed to provide anti-wear and anti-friction protection under extreme load condition. In present study thermophysical and tribological properties of industrial gear oil with nanoparticles additives (Al2O3 and SiO2) has been experimentally evaluated. Morphology of dry nanoparticles under FE-SEM shows that both nanoparticles are spherical. DLS test shows the presence of mild-agglomeration of nanoparticles in the dispersion. Density and viscosity of nanolubricants is tested with varying temperature and particle volume fraction. Measured viscosity results for Al2O3/gear oil nanolubricants follow the existing empirical model and new model has been proposed for SiO2/gear oil nanolubricants. Anti-friction and anti-wear properties of nanolubricants are tested on pin-on-disc test apparatus. FE-SEM and energy dispersive spectroscopy (EDS) analysis of wear scar (pin SS-304), evident the occurrence of ball bearing and surface polishing effects, which may be responsible for advanced tribological properties of the oil. Test results reveals that ball bearing have dominating effect in enhancement of tribological performance.

Experimental Study on the Stability and Viscosity for the Blends of Functionalized MWCNTs with Refrigeration Compressor Oils

Background: Adding nanoparticles to working fluids such as compressor oil can be a solution to ameliorate the efficiency of refrigeration systems. Using the mixture of nanoparticles and oil that is called nanolubricant (or nanofluid) can augment the heat removal in refrigeration systems, however, in the same time, the pumping power will be increased as the viscosity of nanolubricants is higher than that of usual oils. Therefore, the measurement of nanolubricant viscosity is a prerequisite to estimating the pumping power in refrigeration systems. Methods: Experiments section has been divided into three sections. First, the functionalization method of MWCNTs is presented. Then, the preparation of nanolubricants is explained. Finally, the viscosity measurement approach is explained. Results: To increase the dispersibility of MWCNTs in compressor oil, functionalization of MWCNTs was done through attaching −OH and −COOH groups with the aid of 65 wt% nitric acid solution. It was observed that with increasing the viscosity of the base oil, the stability of MWCNTs based nanofluids improves. Then, the viscosity of nanofluids has been measured at a temperature range of 15°C to 50°C. It was found that at 50°C and mass concentration of 0.1%, viscosity enhances between 40 and 90%, depends on the type of base compressor oil. Conclusion: Four correlations involving plane, paraboloid, Gaussian, and Lorentzian functions were suggested for the viscosity of nanolubricants. In the near future, it is expected from the results of this study that refrigeration systems will have included their Freon based refrigerants with nanoparticles.

Characterization of Al2O3-SAE 15W40 engine oil nanolubricant and performance evaluation in 4-stroke diesel engine

This work aims to characterize Al2O3-SAE 15W40 engine oil nanolubricant and its performance evaluation in 4-stroke diesel engine. Field emission scanning electron microscope (FESEM) images of nanoparticle reveal the spherical morphology of nanoparticle and Fourier transform infrared spectroscopic results show chemical stability of nanolubricants. Dynamic light scattering test display the presence of mild agglomeration of particles in nanolubricants, further FESEM images of dispersed nanoparticles re-confirms it. Density and viscosity of nanolubricants are measured with varying particle volume fraction and temperature. The overall performance of nanolubricant is tested on a 4-stroke diesel engine. The nanolubricant with 0.3% volume fraction of Al2O3 nanoparticles shows less frictional power loss and hence higher brake thermal efficiency as compared to other nanolubricants.

Rheological behavior characteristics of ZrO2-MWCNT/10w40 hybrid nano-lubricant affected by temperature, concentration, and shear rate: An experimental study and a neural network simulating

In this study, the ZrO2-MWCNT (70%–30%)/10w40 hybrid Nano-lubricant was experimentally evaluated in terms of rheological changes. ZrO2 (40 nm) and MWCNTs nanoparticles with an inner diameter of 3–5 nm were used to prepare the Nano-lubricant. Particles were weighted for solid volume fractions of 0.05%, 0.1%, 0.25%, 0.5%, 0.75%, and 1% and the nano-lubricant was prepared. Then, the viscosity of Nano-lubricants was measured at different shear rates between 5 and 55 °C. The results showed that the pure oil was non-Newtonian and the nano-lubricant was pseudoplastic. Calculation and checking the power law and consistency coefficients showed that the increase in temperature intensifies the non-Newtonian behavior. Based on the results, at temperature of 55 °C and a volume fraction of 1% condition, maximum of dynamic viscosity enhancement (DVE) was reported. Based on experimental data, a new correlation was proposed at different temperatures and the solid volume fraction, and the sensitivity of nano-lubricant was measured. An Artificial Neural Network (ANN) with two hidden layers and six neurons was designed to predict the viscosity. R2, MSE, and AARD values were obtained as much as 0.9905, 7.0631e-05, and 0.0051 ANN, respectively. Comparison of experimental data with new correlations and ANN showed that the performance of neural network was better in predicting the viscosity data.

Study of dispersion stability and thermo-physical properties of CuO-Jatropha oil-based nanolubricants

In the present study, nanoparticles of surface-modified copper oxide and Jatropha oil are used to prepare stable nanolubricants of various concentrations by a two-step method. Viscosity and thermal conductivity of nanolubricants for different concentrations of CuO ranging from 0 to 5 wt% have been experimentally measured and strong dependence of both the properties on concentration of CuO in the Jatropha oil is observed. About 40 and 10% enhancement in viscosity and thermal conductivity of nanolubricant is observed for 5 wt% concentrations of CuO and the viscosity of nanolubricant comes closer to ISO VG46 mineral oil. Stability of nanolubricants at different temperatures has been checked by measuring viscosity at regular intervals of time and it is observed that stability decreases with increase of CuO loading. The experimental data of viscosity are matched with two viscosity models proposed by different authors, but the experimental data of viscosity and thermal conductivity are observed over prediction by classical theoretical models of viscosity and thermal conductivity. Based on the experimental data, various constants of correlations are calculated for predicting viscosity of CuO-Jatropha oil nanolubricants at various concentrations and temperatures.

Thermal and Rheological Properties of Industrial Mineral Gear Oil and Paraffinic Oil/CNTs Nanolubricants

Carbon nanotubes (CNTs) are used as a nanoadditive due to their unique properties and for improving the lubricant’s properties. In this research, the effect of CNTs in different concentrations (0.1, 0.5, 1 and 2 wt%) on kinematic viscosity, pour point, flash point and thermal conductivity coefficient as four quality parameters which are effective in the functionality of oil is evaluated and compared against the standard Mobil gear 627 and paraffinic mineral oils. The samples were tested according to ASTM D-445, ASTM D-97 and ASTM D-92 standards, respectively. The results indicated that the thermal conductivity, flash point, pour point and kinematic viscosity of nanolubricants with low concentration of CNTs improved with respect to the base oil. Dispersion of CNTs as additive has improved the lubricant properties and operational characteristics of Mobil gear 627 and paraffinic mineral oils.