Rheological behavior of oil- silicon dioxide- multi walled carbon nanotube hybrid nanofluid: Experimental study and neural network prediction (original) (raw)
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Ain Shams Engineering Journal, 2023
In this paper, the rheological performance and dynamic viscosity of hybrid nanofluid containing SiO 2 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 R 2 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.
International Communications in Heat and Mass Transfer, 2020
In this article, the rheological behavior of MWCNT/SiO 2 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 (R 2 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 R 2 .
In this article, rheological behavior of TiO2-MWCNT (45–55%)/10w40 hybrid nano-oil was studied experimentally. The nano-oils were tested at temperature ranges of 5–55 °C and in shear rates up to 11,997 s −1. With respect to viscosity, shear stress and shear rate variations it was cleared that either of the base oil and nano-oil were non-Newtonian fluids. New equations which were based on thickness of the fluid were presented for different temperature values, R-squared values were between 0.9221 and 0.9998 (the precise of correlation changes depend on temperature). Also to predict the nano-oil behavior, neural network method was utilized. an artificial neural network (MLP type) were used to estimate and predict the viscosity in terms of temperature, solid volume fraction and shear stress. to compare the estimation precise of neural network and correlation the results of these two were compared with together. ANN showed more accurate results in comparison with correlation results. R 2 and (MSE) were 0.9979 and 0.000016 respectively for the ANN.
Journal of Thermal Analysis and Calorimetry, 2020
The main objective of the present paper was to investigate the feasibility of the least-square support vector machine (LSSVM) in predicting the effects of shear rate on the dynamic viscosity of a hybrid oil-based nanolubricant containing MWCNT and MgO nanoparticles in different solid concentrations and temperatures. Firstly, measuring the dynamic viscosity of the nanofluid revealed that the nanofluid is a non-Newtonian fluid at the temperatures of 10 °C and 20 °C in all the studied shear rates and solid concentrations while it showed Newtonian behavior at the rest of the studied temperatures. Then the effects of solid concentration and temperature on the dynamic viscosity have been experimentally studied, and it is found that the dynamic viscosity increased as the solid concentration increased; the maximum increase has been observed at the solid concentration of 1.5% and temperature of 60 °C by 52 vol.%, while the minimum increase has been observed at the solid concentration of 0.125 vol.% and temperature of 10 °C by 11%. Based on the experimental data, a new correlation to predict the dynamic viscosity of the nanofluid in terms of shear rate, solid concentration, and the temperature has been proposed. Then, the LSSVM has been employed to predict the dynamic viscosity behavior of the nanofluid considering the shear rate, temperature, and solid concentration as the input variables and the dynamic viscosity as the output variable and the results showed the excellent capability of the LSSVM in predicting the dynamic viscosity. Finally, the effects of adding the hybrid nanoparticles on the pumping power have been studied.
Digital Chemical Engineering, 2024
Nanofluids possess enhanced viscous and thermal features that can be utilized to improve the heat transfer performance of several applications involving sustainable manufacturing and industrial ecology, such as in heating/cooling systems, electronics, transportation etc. Therefore, it is important to understand and optimize the flow pattern of these fluids. This research emphasizes the predictions of viscosity of water/ethylene-glycol (EG) based non-Newtonian nanofluids. Four experiment-based data sets are used to predict and validate the effective viscosity, i.e., Fe 3 O 4-Ag/EG, MWCNT-alumina/water-EG, Fe 3 O 4-Ag/water-EG, and MWCNT-SiO 2 /EGwater, via existing correlations and artificial neural network (ANN). The modeling is based on three input parameters, i.e., particle concentrations, temperatures, and shear rates, and one output parameter, i.e., viscosity. The predicted outcomes are compared to the three existing correlation structures. The error matrix consists of the coefficient of determination (R 2), average absolute deviation (AAD %), the sum of squared error (SSE), that are employed to evaluate the performance of the model. Results from ANN are found to be more precise, with R 2 values greater than 0.99 for all datasets, compared to data fitting into existing correlations, in which Fe 3 O 4-Ag/ water-EG resulted in an R 2 value as low as 0.72, to determine the nanofluids' effective viscosity.
Begell House, Inc. www.begellhouse.com, 2020
In this paper, an artifi cial neural network (ANN) has been studied for the viscosity of MWCNTs–ZnO/water–ethylene glycol (80:20 vol.%) non-Newtonian nanofl uid. To evaluate the rheological behavior of the nanocoolants, for each solid volume fraction and temperature, all experiments were repeated at diff erent shear rates. Aft er generating the experimental data, an ANN method is applied. The ANN is selected based on the diff erent generating architectures (neuron numbers). The algorithm for choosing the best ANN is presented. Also, using the correlation method, the viscosity of nanofl uid is predicted. Finally, ANN and correlation results are compared with the obtained data from the correlation method. It was found that the ANN had a bett er ability in predicting the viscosity of nanofl uid compared with the correlation method because the (MSE) of ANN was 0.0885, and the MSE of the correlation method was 0.9531. However, both approaches are useful, but ANN had a bett er ability to model the viscosity of nanofl uid based on the input values.
Heat and Mass Transfer, 2017
The main objectives of this study have been measurement of the dynamic viscosity of CuO-MWCNT s /SAE 5w-50 hybrid nanofluid, utilization of artificial neural networks (ANN) and development of a new viscosity model. The new nanofluid has been prepared by a two-stage procedure with volume fractions of 0.05, 0.1, 0.25, 0.5, 0.75 and 1%. Then, utilizing a Brookfield viscometer, its dynamic viscosity has been measured for temperatures of 5, 15, 25, 35, 45, 55°C. The experimental results demonstrate that the viscosity increases by increasing the nanoparticles volume fraction and decreases by increasing temperature. Based on the experimental data the maximum and minimum nanofluid viscosity enhancements, when the volume fraction increases from 0.05 to 1, are 35.52% and 12.92% for constant temperatures of 55 and 15°C, respectively. The higher viscosity of oil engine in higher temperatures is an advantage, thus this result is important. The measured nanofluid viscosity magnitudes in various shear rates show that this hybrid nanofluid is Newtonian. An ANN model has been employed to predict the viscosity of the CuO-MWCNTs/SAE 5w-50 hybrid nanofluid and the results showed that the ANN can estimate the viscosity efficiently and accurately. Eventually, for viscosity estimation a new temperature and volume fraction based third-degree polynomial empirical model has been developed. The comparison shows that this model is in good agreement with the experimental data. Keywords Viscosity. Hybrid nanofluid. Newtonian fluid. Empirical correlation. Ann Highlights • Preparing the CuO-MWCNTs/SAE 5w-50 hybrid nanofluid. • Measuring the dynamic viscosity of CuO-MWCNTs/SAE 5w-50 hybrid nanofluid. • Comparison with predictions of existing models and presenting a new correlation. • The CuO-MWCNTs/SAE 5w-50 nanofluid shows Newtonian behavior. • An ANN method is employed to predict the viscosity of the hybrid nanofluid.
Scientific Reports
In the present study, the properties of ternary hybrid nanofluid (THNF) of oil (5W30) - Graphene Oxide (GO)-Silica Aerogel (SA)-multi-walled carbon nanotubes (MWCNTs) in volume fractions ($$\varphi )φ)of0.3φ ) of 0.3%, 0.6%, 0.9%, 1.2%, and 1.5% and at temperatures 5 to 65 °C has been measured. This THNF is made in a two-step method and a viscometer device made in USA is used for viscosity measurements. The wear test was performed via a pin-on-disk tool according to the ASTM G99 standard. The outcomes show that the viscosity increases with the increase in theφ)of0.3\varphiφ,andthereductionintemperature.Byenhancingthetemperatureby60°C,atφ , and the reduction in temperature. By enhancing the temperature by 60 °C, atφ,andthereductionintemperature.Byenhancingthetemperatureby60°C,at\varphi$$ φ = 1.2% and a shear rate (SR) of 50 rpm, a viscosity reduction of approximately 92% has been observed. Also, the results showed that with the rise in SR, the shear stress increased and the viscosity decreased. The estimated values of THNF viscosity at various SRs and temperatures show that its behavior is non-Newtonian. The effi...