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Mahidzal Dahari - One of the best experts on this subject based on the ideXlab platform.
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predicting the Viscosity of multi walled carbon nanotubes water nanofluid by developing an optimal artificial neural network based on experimental data
International Communications in Heat and Mass Transfer, 2016Co-Authors: Masoud Afrand, Afshin Ahmadi Nadooshan, Mohsen Hassani, Hooman Yarmand, Mahidzal DahariAbstract:Regarding the Viscosity of the fluids which is an imperative parameter for calculating the required pumping power and convective heat transfer, based on experimental data, an optimal artificial neural network was designed to predict the Relative Viscosity of multi-walled carbon nanotubes/water nanofluid. Solid volume fraction and temperature were used as input variables and Relative Viscosity was employed as output variable. Accurate and efficient artificial neural network was obtained by changing the number of neurons in the hidden layer. The dataset was divided into training and test sets which contained 80 and 20% of data points respectively. The results obtained from the optimal artificial neural network exhibited a maximum deviation margin of 0.28%. Eventually, the ANN outputs were compared with results obtained from the previous empirical correlation and experimental data. It was found that the optimal artificial neural network model is more accurate compared to the previous empirical correlation.
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prediction of dynamic Viscosity of a hybrid nano lubricant by an optimal artificial neural network
International Communications in Heat and Mass Transfer, 2016Co-Authors: Masoud Afrand, Karim Nazari Najafabadi, Nima Sina, Mohammad Reza Safaei, Sh A Kherbeet, Somchai Wongwises, Mahidzal DahariAbstract:In this paper, at first, a new correlation was proposed to predict the Relative Viscosity of MWCNTs-SiO2/AE40 nano-lubricant using experimental data. Then, considering minimum prediction error, an optimal artificial neural network was designed to predict the Relative Viscosity of the nano-lubricant. Forty-eight experimental data were used to feed the model. The data set was derived to training, validation and test sets which contained 70%, 15% and 15% of data points, respectively. The correlation outputs showed that there is a deviation margin of 4%. The results obtained from optimal artificial neural network presented a deviation margin of 1.5%. It can be found from comparisons that the optimal artificial neural network model is more accurate compared to empirical correlation.
Davood Toghraie - One of the best experts on this subject based on the ideXlab platform.
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an optimal feed forward artificial neural network model and a new empirical correlation for prediction of the Relative Viscosity of al2o3 engine oil nanofluid
Scientific Reports, 2021Co-Authors: Mohammad Hemmat Esfe, Davood ToghraieAbstract:This study presents the design of an artificial neural network (ANN) to evaluate and predict the Viscosity behavior of Al2O3/10W40 nanofluid at different temperatures, shear rates, and volume fraction of nanoparticles. Nanofluid Viscosity ( $${\mu }_{nf}$$ ) is evaluated at volume fractions ( $$\varphi$$ =0.25% to 2%) and temperature range of 5 to 55 °C. For modeling by ANN, a multilayer perceptron (MLP) network with the Levenberg–Marquardt algorithm (LMA) is used. The main purpose of this study is to model and predict the $${\mu }_{nf}$$ of Al2O3/10W40 nanofluid through ANN, select the best ANN structure from the set of predicted structures and manage time and cost by predicting the ANN with the least error. To model the ANN, $$\varphi$$ , temperature, and shear rate are considered as input variables, and $${\mu }_{nf}$$ is considered as output variable. From 400 different ANN structures for Al2O3/10W40 nanofluid, the optimal structure consisting of two hidden layers with the optimal structure of 6 neurons in the first layer and 4 neurons in the second layer is selected. Finally, the R regression coefficient and the MSE are 0.995838 and 4.14469E−08 for the optimal structure, respectively. According to all data, the margin of deviation (MOD) is in the range of less than 2% < MOD < + 2%. Comparison of the three data sets, namely laboratory data, correlation output, and ANN output, shows that the ANN estimates laboratory data more accurately.
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statistical investigation for developing a new model for rheological behavior of silica ethylene glycol water hybrid newtonian nanofluid using experimental data
Physica A-statistical Mechanics and Its Applications, 2019Co-Authors: Behrooz Ruhani, Pouya Barnoon, Davood ToghraieAbstract:Abstract In this experimental investigation, we developed a new model for rheological behavior of Silica–Ethylene glycol/Water (30: 70 vol. %) hybrid Newtonian nanofluid. The tests were carried out in volume fractions of 0.1%, 0.25%, 0.5%, 0.75%, and 1.5%, the temperature range from 25 °C to 50 °C, and in the shear rate range 24.48 s − 1 to 73.44 s − 1 . It can be deduced that the obtained correlation is a suitable model for estimating the desired nanofluid Viscosity. Also, as the volume fraction increases, the Relative Viscosity increases due to the greater dispersion of the nanoparticles in the base fluid. The maximum marginal deviation values in this graph are shown to be equal to 1.37%. This value is acceptable for an experimental correlation. We find that the relationship between shear stress and shear rate is linear; then the desired fluid is Newtonian. At the maximum volume fraction, the percentage of loss of Viscosity from the minimum temperature to the maximum temperature is 89%. In addition, at maximum operating temperature, the percentage increase in Relative Viscosity in the maximum volume fraction Relative to the minimum fraction is 48%.
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statistical investigation for developing a new model for rheological behavior of zno ag 50 50 water hybrid newtonian nanofluid using experimental data
Physica A-statistical Mechanics and Its Applications, 2019Co-Authors: Behrooz Ruhani, Davood Toghraie, Maboud Hekmatifar, Mahdieh HadianAbstract:Abstract In this paper, the test results, along with the results of data analysis, are presented by graphs and tables. The effect of volume fraction and temperature on Viscosity of a hybrid nanofluid, i.e. ZnO–Ag (50%–50%)-Water, is presented. Finally, a model for calculating nanofluid’s Viscosity is based on available data was proposed. The results show that the dynamic Viscosity decreases with increasing temperature and increases with increasing volume fraction of nanoparticles. Also, an increase in volume fraction at all temperatures is associated with an increase in Relative Viscosity. This increase was reported modest and linear in very low volume fractions. The margin of deviation between laboratory results and extracted experimental equations is equal to 1.8%.
Masoud Afrand - One of the best experts on this subject based on the ideXlab platform.
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predicting the Viscosity of multi walled carbon nanotubes water nanofluid by developing an optimal artificial neural network based on experimental data
International Communications in Heat and Mass Transfer, 2016Co-Authors: Masoud Afrand, Afshin Ahmadi Nadooshan, Mohsen Hassani, Hooman Yarmand, Mahidzal DahariAbstract:Regarding the Viscosity of the fluids which is an imperative parameter for calculating the required pumping power and convective heat transfer, based on experimental data, an optimal artificial neural network was designed to predict the Relative Viscosity of multi-walled carbon nanotubes/water nanofluid. Solid volume fraction and temperature were used as input variables and Relative Viscosity was employed as output variable. Accurate and efficient artificial neural network was obtained by changing the number of neurons in the hidden layer. The dataset was divided into training and test sets which contained 80 and 20% of data points respectively. The results obtained from the optimal artificial neural network exhibited a maximum deviation margin of 0.28%. Eventually, the ANN outputs were compared with results obtained from the previous empirical correlation and experimental data. It was found that the optimal artificial neural network model is more accurate compared to the previous empirical correlation.
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prediction of dynamic Viscosity of a hybrid nano lubricant by an optimal artificial neural network
International Communications in Heat and Mass Transfer, 2016Co-Authors: Masoud Afrand, Karim Nazari Najafabadi, Nima Sina, Mohammad Reza Safaei, Sh A Kherbeet, Somchai Wongwises, Mahidzal DahariAbstract:In this paper, at first, a new correlation was proposed to predict the Relative Viscosity of MWCNTs-SiO2/AE40 nano-lubricant using experimental data. Then, considering minimum prediction error, an optimal artificial neural network was designed to predict the Relative Viscosity of the nano-lubricant. Forty-eight experimental data were used to feed the model. The data set was derived to training, validation and test sets which contained 70%, 15% and 15% of data points, respectively. The correlation outputs showed that there is a deviation margin of 4%. The results obtained from optimal artificial neural network presented a deviation margin of 1.5%. It can be found from comparisons that the optimal artificial neural network model is more accurate compared to empirical correlation.
Rajinder Pal - One of the best experts on this subject based on the ideXlab platform.
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Relative Viscosity of non newtonian concentrated emulsions of noncolloidal droplets
Industrial & Engineering Chemistry Research, 2000Co-Authors: Rajinder PalAbstract:The Relative Viscosity of non-Newtonian concentrated emulsions of nearly spherical, non-Brownian droplets, in the absence of Coulombic and van der Waals interactions, is a function of four variables: the Viscosity ratio (K), the volume fraction of dispersed phase (Φ), the particle Reynolds number (N Re,p ), and the maximum packing volume fraction (Φ m ). The maximum packing volume fraction depends on the droplet size distribution. Based on five sets of data for monomodal and bimodal emulsions, a novel correlation is proposed to describe the Relative Viscosity of non-Newtonian concentrated emulsions. The proposed correlation can be used to predict the Relative Viscosity-Reynolds number behavior of non-Newtonian concentrated emulsions from the knowledge of the volume fraction of the dispersed phase, the Viscosity ratio, and the maximum packing volume fraction of dispersed phase.
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scaling of Relative Viscosity of emulsions
Journal of Rheology, 1997Co-Authors: Rajinder PalAbstract:A scaling analysis of the Relative Viscosity of two-phase liquid-liquid emulsions is presented. According to the analysis, the Relative Viscosity of two oil-in-water emulsions at the same oil concentration and particle Reynolds number is the same, even if the emulsions differ greatly in droplet size. Experimental results are presented to confirm the validity of the proposed scaling relations.
Shuji Miyazaki - One of the best experts on this subject based on the ideXlab platform.
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flow characteristics of a coal oil water mixture prepared by disintegration of de ashed coal agglomerates
Advanced Powder Technology, 1999Co-Authors: Hitoshi Takase, Shuji MiyazakiAbstract:The flow characteristics of a coal-oil-water mixture (COW) were investigated. COW was prepared by disintegrating de-ashed coal agglomerates that had been obtained by oil agglomeration of coal. When the volume fraction of dispersed waterdrops in COW was smaller than 0.2, the dispersed waterdrops had a similar effect to that of the dispersed coal particles on the flow characteristics of COW. COW behaved as a Bingham fluid. Both the yield value and Bingham Viscosity increased with increases in the total disperse-phase volume fraction in COW and with decreases in temperature. The Relative Viscosity of COW was well-correlated by an equation derived from Mooney's equation.
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flow characteristics of coal oil water mixture prepared by disintegration of deashed coal agglomerates
Journal of The Society of Powder Technology Japan, 1998Co-Authors: Hitoshi Takase, Shuji MiyazakiAbstract:The flow characteristics of coal-oil-water mixture (COW) was investigated. COW was prepared by a disintegration of deashed coal agglomerates, which were obtained by the oil agglomeration of coal. When the volume fraction of dispersed waterdrops in COW was smaller than 0.2, the dispersed waterdrops influenced the flow characteristics of COW to the same extent as the dispersed coal particles. COW behaved as Bingham fluid. Both the yield value and Bingham Viscosity increased with increasing total disperse phase volume fraction in COW, and with decreasing temperature. The Relative Viscosity of COW was well correlated by the equation derived with reference to Mooney's equation.