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Mohammad Hossein Ahmadi - One of the best experts on this subject based on the ideXlab platform.
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comparing various machine learning approaches in modeling the Dynamic Viscosity of cuo water nanofluid
Journal of Thermal Analysis and Calorimetry, 2020Co-Authors: Mohammad Hossein Ahmadi, Ravindra D. Jilte, Marjan Goodarzi, Mahyar Ghazvini, Behnam Mohsenigharyehsafa, Ravinder KumarAbstract:Nanofluids are broadly employed in heat transfer mediums to enhance their efficiency and heat transfer capacity. Thermophysical properties of nanofluids play a crucial role in their thermal behavior. Among various properties, the Dynamic Viscosity is one of the most crucial ones due to its impact on fluid motion and friction. Applying appropriate models can facilitate the design of nanofluidics thermal devices. In the present study, various machine learning methods including MPR, MARS, ANN-MLP, GMDH, and M5-tree are used for modeling the Dynamic Viscosity of CuO/water nanofluid based on the temperature, concentration, and size of nanostructures. The input data are extracted from various experimental studies to propose a comprehensive model, applicable in wide ranges of input variables. Moreover, the relative importance of each variable is evaluated to figure out the priority of the variables and their influences on the Dynamic Viscosity. Finally, the accuracy of the models is compared by employing the statistical criteria such as R-squared value. The models’ outputs disclosed that employing ANN-MLP approach leads to the most precise model. R-square value and average absolute percent relative error (AAPR) value of the model by using ANN-MLP model are 0.9997 and 1.312%, respectively. According to these values, ANN-MLP is a reliable approach for predicting the Dynamic Viscosity of the studied nanofluid. Additionally, based on the relative importance of the input variables, it is concluded that concentration has the highest relative importance; while the influence of size is the lowest one.
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Comparing various machine learning approaches in modeling the Dynamic Viscosity of CuO/water nanofluid
Journal of Thermal Analysis and Calorimetry, 2019Co-Authors: Mohammad Hossein Ahmadi, Ravindra D. Jilte, B. Mohseni-gharyehsafa, Marjan Goodarzi, Mahyar Ghazvini, Ravinder KumarAbstract:Nanofluids are broadly employed in heat transfer mediums to enhance their efficiency and heat transfer capacity. Thermophysical properties of nanofluids play a crucial role in their thermal behavior. Among various properties, the Dynamic Viscosity is one of the most crucial ones due to its impact on fluid motion and friction. Applying appropriate models can facilitate the design of nanofluidics thermal devices. In the present study, various machine learning methods including MPR, MARS, ANN-MLP, GMDH, and M5-tree are used for modeling the Dynamic Viscosity of CuO/water nanofluid based on the temperature, concentration, and size of nanostructures. The input data are extracted from various experimental studies to propose a comprehensive model, applicable in wide ranges of input variables. Moreover, the relative importance of each variable is evaluated to figure out the priority of the variables and their influences on the Dynamic Viscosity. Finally, the accuracy of the models is compared by employing the statistical criteria such as R -squared value. The models’ outputs disclosed that employing ANN-MLP approach leads to the most precise model. R -square value and average absolute percent relative error (AAPR) value of the model by using ANN-MLP model are 0.9997 and 1.312%, respectively. According to these values, ANN-MLP is a reliable approach for predicting the Dynamic Viscosity of the studied nanofluid. Additionally, based on the relative importance of the input variables, it is concluded that concentration has the highest relative importance; while the influence of size is the lowest one.
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rigorous smart model for predicting Dynamic Viscosity of al2o3 water nanofluid
Journal of Thermal Analysis and Calorimetry, 2019Co-Authors: Mahdi Ramezanizadeh, Mohammad Hossein Ahmadi, Mohammad Ali Ahmadi, Mohammad Alhuyi NazariAbstract:Due to the enhanced thermophysical specifications of nanofluids, such as thermal conductivity, these types of fluids are appropriate candidates for heat transfer fluids. Nanostructure dispersion in the base fluid increases the Dynamic Viscosity which affects fluid flow in thermal devices. In order to facilitate design of thermal devices, it is crucial to have accurate predictive models for thermophysical properties of nanofluids. Dimensions of nanoparticles, working temperature and the concentration of nano-sized particles in the fluid are among the most influential factors in predicting Dynamic Viscosity of nanofluids. In the present research, four LSSVM-based algorithms including GA-LSSVM, PSO-LSSVM, HGAPSO-LSSVM and ICA-LSSVM are employed to model the Dynamic Viscosity of Al2O3/water. Results revealed that the generated models are accurate tools to calculate the Dynamic Viscosity of the nanofluid on the basis of the mentioned variables. The highest obtained coefficient of correlation belongs to GA-LSSVM which is equal to 0.9871, while this value for PSO-LSSVM, HGAPSO-LSSVM, and ICA-LSSVM algorithms are 0.9855, 0.9855, and 0.9846, respectively. Another utilized criterion for evaluating model accuracy is MSE value. Results revealed that the MSE values for HGAPSO-LSSVM, GA-LSSVM, PSO-LSSVM, and ICA-LSSVM are 0.00854, 0.00855, 0.00896 and 0.00979, respectively.
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A review on the utilized machine learning approaches for modeling the Dynamic Viscosity of nanofluids
Renewable and Sustainable Energy Reviews, 2019Co-Authors: Ramezanizadeh, Mohammad Hossein Ahmadi, Mohammad Alhuyi Nazari, Milad Sadeghzadeh, Lingen ChenAbstract:Abstract Nanofluids are broadly applied in energy systems such as solar collector, heat exchanger and heat pipes. Dynamic Viscosity of the nanofluids is among the most important features affecting their thermal behavior and heat transfer ability. Several predictive models, by employing various methods such as Artificial Neural Network, Support Vector Machine and mathematical correlations, have been proposed for estimating Dynamic Viscosity based on the influential factors such as size, type and volume fraction of nano particles and their temperature. The precision of the models depends on different elements such as the employed approach for modeling, input variables and the structure of the model. In order to have an accurate model for estimating the Dynamic Viscosity, it is necessary to consider all of the affecting factor. In this regard, the current study aim to review the researches concerns the applications of machine learning methods for Dynamic Viscosity modeling of nanofluids in order to provide deeper insight for the scientists. According to the reviewed scientific sources, the structure of model, such as number of neurons and layers in artificial neural network (ANN), the applied activation function, and utilized algorithm are the most influential factors on the accuracy of the model. Moreover, based on the studies considered both ANN and mathematical correlations, ANNs are more accurate and confident for estimating the nanofluids’ Dynamic Viscosity. The majority of the studies in this field used temperature and concentration of nanofluids as input data for their models, while size of nanostructures and shear rate are considered in some researches in addition to mentioned variables.
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Applicability of connectionist methods to predict Dynamic Viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms
Taylor & Francis Group, 2019Co-Authors: Mohammad Hossein Ahmadi, Ravindra D. Jilte, B. Mohseni-gharyehsafa, Ravinder Kumar, Mahmood Farzaneh-gord, Kwok-wing ChauAbstract:Dynamic Viscosity considerably affects the heat transfer and flow of fluids. Due to improved thermophysical properties of fluids containing nanostructures, these types of fluids are widely employed in thermal mediums. The nanofluid's Dynamic Viscosity relies on different variables including size of solid phase, concentration and temperature. In the present study, three algorithms including multivariable polynomial regression (MPR), artificial neural network–multilayer perceptron (ANN-MLP) and multivariate adaptive regression splines (MARS) are applied to model the Dynamic Viscosity of silver (Ag)/water nanofluid. Recently published experimental investigations are employed for data extraction. The input variables considered in the modeling process to be the most important ones are the size of particles, fluid temperature and the concentration of Ag nanoparticles in the base fluid. The R2 values for the studied models are 0.9998, 0.9997 and 0.9996 for the ANN-MLP, MARS and MPR algorithms, respectively. In addition, based on importance analysis, the temperature is highly effective and the dominant parameter for the Dynamic Viscosity of the nanofluid in comparison with size and concentration
Amin Asadi - One of the best experts on this subject based on the ideXlab platform.
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an experimental study on characterization stability and Dynamic Viscosity of cuo tio2 water hybrid nanofluid
Journal of Molecular Liquids, 2020Co-Authors: Amin Asadi, Ibrahim M. Alarifi, Loke Kok FoongAbstract:Abstract One of the essential specifications of nanofluids is the rheological behavior that has a significant effect on the system's performance. The primary objective of the present study is to investigate the rheological behavior and Dynamic Viscosity of the CuO-TiO2/water hybrid nanofluid. First of all, the chemical, atomic, and surface structures of the CuO and TiO2 nanoparticles were examined using X-ray diffraction, Fourier-Transform Infrared Spectroscopy, and Field Emission Scanning Electron Microscopy tests. Then, the two-step method was employed to prepare the needed samples. At the next step, the Dynamic Light Scattering test was used to examine the stability of the nanofluid. Furthermore, Dynamic Viscosity was measured in the different temperatures, ranging from 25 to 55 °C, and solid concentrations, ranging from 0.1 to 1 vol%. Moreover, the effect of shear rate on the rheological properties of the hybrid nanofluid was investigated. The results revealed that the prepared nanofluid is a Newtonian fluid. The results of Dynamic Viscosity measurements indicated that the maximum Dynamic Viscosity is at the solid concentration of 1 vol%, and a temperature of 25 °C. Finally, based on the obtained results, it can be said that considering different cost-benefit analyses, including high stability and low production cost, the studied nanofluids have a high quality and can be considered as an appropriate alternative for replacing the water-based fluid.
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An experimental study on characterization, stability and Dynamic Viscosity of CuO-TiO2/water hybrid nanofluid
Journal of Molecular Liquids, 2020Co-Authors: Amin Asadi, Ibrahim M. Alarifi, Loke Kok FoongAbstract:Abstract One of the essential specifications of nanofluids is the rheological behavior that has a significant effect on the system's performance. The primary objective of the present study is to investigate the rheological behavior and Dynamic Viscosity of the CuO-TiO2/water hybrid nanofluid. First of all, the chemical, atomic, and surface structures of the CuO and TiO2 nanoparticles were examined using X-ray diffraction, Fourier-Transform Infrared Spectroscopy, and Field Emission Scanning Electron Microscopy tests. Then, the two-step method was employed to prepare the needed samples. At the next step, the Dynamic Light Scattering test was used to examine the stability of the nanofluid. Furthermore, Dynamic Viscosity was measured in the different temperatures, ranging from 25 to 55 °C, and solid concentrations, ranging from 0.1 to 1 vol%. Moreover, the effect of shear rate on the rheological properties of the hybrid nanofluid was investigated. The results revealed that the prepared nanofluid is a Newtonian fluid. The results of Dynamic Viscosity measurements indicated that the maximum Dynamic Viscosity is at the solid concentration of 1 vol%, and a temperature of 25 °C. Finally, based on the obtained results, it can be said that considering different cost-benefit analyses, including high stability and low production cost, the studied nanofluids have a high quality and can be considered as an appropriate alternative for replacing the water-based fluid.
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Dynamic Viscosity of mwcnt zno engine oil hybrid nanofluid an experimental investigation and new correlation in different temperatures and solid concentrations
International Communications in Heat and Mass Transfer, 2016Co-Authors: M. Asadi, Amin AsadiAbstract:Abstract The major objective of the present study was to investigate the Dynamic Viscosity of MWCNT/ZnO–engine oil hybrid nanofluid. The experiments carried out in different temperatures ranging from 5 °C to 55 °C and solid concentrations ranging from, 0.125% to 1%. The Viscosity of the MWCNT/ZnO nanoparticle with the mean diameter of 30 nm dispersed in engine oil was measured using Brookfield cone and plate viscometer. The effect of temperature and solid concentration on Dynamic Viscosity of the nanofluid has been experimentally investigated. The results indicated that increasing the temperature resulted in decreasing the Dynamic Viscosity of the nanofluid by 85% while the Dynamic Viscosity increased as the solid concentration increased by 45%. Furthermore, based on the experimental data, a new model to predict the Dynamic Viscosity of the studied nanofluid has been proposed.
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Dynamic Viscosity of MWCNT/ZnO–engine oil hybrid nanofluid: An experimental investigation and new correlation in different temperatures and solid concentrations
International Communications in Heat and Mass Transfer, 2016Co-Authors: M. Asadi, Amin AsadiAbstract:Abstract The major objective of the present study was to investigate the Dynamic Viscosity of MWCNT/ZnO–engine oil hybrid nanofluid. The experiments carried out in different temperatures ranging from 5 °C to 55 °C and solid concentrations ranging from, 0.125% to 1%. The Viscosity of the MWCNT/ZnO nanoparticle with the mean diameter of 30 nm dispersed in engine oil was measured using Brookfield cone and plate viscometer. The effect of temperature and solid concentration on Dynamic Viscosity of the nanofluid has been experimentally investigated. The results indicated that increasing the temperature resulted in decreasing the Dynamic Viscosity of the nanofluid by 85% while the Dynamic Viscosity increased as the solid concentration increased by 45%. Furthermore, based on the experimental data, a new model to predict the Dynamic Viscosity of the studied nanofluid has been proposed.
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Development of new model to predict Dynamic Viscosity of ethylene glycol based nanofluid containing Mg(OH)2
2014Co-Authors: Mohammad Hemmat Esfe, Amin Asadi, Seyfollah Sadodin, Seyed Hadi Rostamian, Mohammad GhavidelAbstract:In this study, the Dynamic Viscosity of Mg(OH)2ethylene glycol(EG) is measured. Also, new model for determining the Dynamic Viscosity of the nanofluid is proposed. Due to the limitation of the previous correlations in order to predict the Dynamic Viscosity of the mentioned nanofluid, a new practical equation is suggested base on the experimental correlations. The results indicate that while the solid volume fraction is increased, the Dynamic Viscosity is increased simultaneously. It can be interesting to note that at lower temperatures this increase is more noticeable than those in higher temperatures. In addition, it is shown that a special temperature of 55⁰C, the solid volume fraction has no significant impact on the Dynamic Viscosity of the nanofluid. This unique consequence can be considered as a paramount breakthrough in the engineering and industrial applications.
Mohammad Alhuyi Nazari - One of the best experts on this subject based on the ideXlab platform.
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rigorous smart model for predicting Dynamic Viscosity of al2o3 water nanofluid
Journal of Thermal Analysis and Calorimetry, 2019Co-Authors: Mahdi Ramezanizadeh, Mohammad Hossein Ahmadi, Mohammad Ali Ahmadi, Mohammad Alhuyi NazariAbstract:Due to the enhanced thermophysical specifications of nanofluids, such as thermal conductivity, these types of fluids are appropriate candidates for heat transfer fluids. Nanostructure dispersion in the base fluid increases the Dynamic Viscosity which affects fluid flow in thermal devices. In order to facilitate design of thermal devices, it is crucial to have accurate predictive models for thermophysical properties of nanofluids. Dimensions of nanoparticles, working temperature and the concentration of nano-sized particles in the fluid are among the most influential factors in predicting Dynamic Viscosity of nanofluids. In the present research, four LSSVM-based algorithms including GA-LSSVM, PSO-LSSVM, HGAPSO-LSSVM and ICA-LSSVM are employed to model the Dynamic Viscosity of Al2O3/water. Results revealed that the generated models are accurate tools to calculate the Dynamic Viscosity of the nanofluid on the basis of the mentioned variables. The highest obtained coefficient of correlation belongs to GA-LSSVM which is equal to 0.9871, while this value for PSO-LSSVM, HGAPSO-LSSVM, and ICA-LSSVM algorithms are 0.9855, 0.9855, and 0.9846, respectively. Another utilized criterion for evaluating model accuracy is MSE value. Results revealed that the MSE values for HGAPSO-LSSVM, GA-LSSVM, PSO-LSSVM, and ICA-LSSVM are 0.00854, 0.00855, 0.00896 and 0.00979, respectively.
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A review on the utilized machine learning approaches for modeling the Dynamic Viscosity of nanofluids
Renewable and Sustainable Energy Reviews, 2019Co-Authors: Ramezanizadeh, Mohammad Hossein Ahmadi, Mohammad Alhuyi Nazari, Milad Sadeghzadeh, Lingen ChenAbstract:Abstract Nanofluids are broadly applied in energy systems such as solar collector, heat exchanger and heat pipes. Dynamic Viscosity of the nanofluids is among the most important features affecting their thermal behavior and heat transfer ability. Several predictive models, by employing various methods such as Artificial Neural Network, Support Vector Machine and mathematical correlations, have been proposed for estimating Dynamic Viscosity based on the influential factors such as size, type and volume fraction of nano particles and their temperature. The precision of the models depends on different elements such as the employed approach for modeling, input variables and the structure of the model. In order to have an accurate model for estimating the Dynamic Viscosity, it is necessary to consider all of the affecting factor. In this regard, the current study aim to review the researches concerns the applications of machine learning methods for Dynamic Viscosity modeling of nanofluids in order to provide deeper insight for the scientists. According to the reviewed scientific sources, the structure of model, such as number of neurons and layers in artificial neural network (ANN), the applied activation function, and utilized algorithm are the most influential factors on the accuracy of the model. Moreover, based on the studies considered both ANN and mathematical correlations, ANNs are more accurate and confident for estimating the nanofluids’ Dynamic Viscosity. The majority of the studies in this field used temperature and concentration of nanofluids as input data for their models, while size of nanostructures and shear rate are considered in some researches in addition to mentioned variables.
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Rigorous smart model for predicting Dynamic Viscosity of Al2O3/water nanofluid
Journal of Thermal Analysis and Calorimetry, 2018Co-Authors: Ramezanizadeh, Mohammad Hossein Ahmadi, Mohammad Ali Ahmadi, Mohammad Alhuyi NazariAbstract:Due to the enhanced thermophysical specifications of nanofluids, such as thermal conductivity, these types of fluids are appropriate candidates for heat transfer fluids. Nanostructure dispersion in the base fluid increases the Dynamic Viscosity which affects fluid flow in thermal devices. In order to facilitate design of thermal devices, it is crucial to have accurate predictive models for thermophysical properties of nanofluids. Dimensions of nanoparticles, working temperature and the concentration of nano-sized particles in the fluid are among the most influential factors in predicting Dynamic Viscosity of nanofluids. In the present research, four LSSVM-based algorithms including GA-LSSVM, PSO-LSSVM, HGAPSO-LSSVM and ICA-LSSVM are employed to model the Dynamic Viscosity of Al2O3/water. Results revealed that the generated models are accurate tools to calculate the Dynamic Viscosity of the nanofluid on the basis of the mentioned variables. The highest obtained coefficient of correlation belongs to GA-LSSVM which is equal to 0.9871, while this value for PSO-LSSVM, HGAPSO-LSSVM, and ICA-LSSVM algorithms are 0.9855, 0.9855, and 0.9846, respectively. Another utilized criterion for evaluating model accuracy is MSE value. Results revealed that the MSE values for HGAPSO-LSSVM, GA-LSSVM, PSO-LSSVM, and ICA-LSSVM are 0.00854, 0.00855, 0.00896 and 0.00979, respectively.
Ali Akbar Abbasian Arani - One of the best experts on this subject based on the ideXlab platform.
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an experimental determination and accurate prediction of Dynamic Viscosity of mwcnt 40 sio2 60 5w50 nano lubricant
Journal of Molecular Liquids, 2018Co-Authors: Mohammad Hemmat Esfe, Ali Akbar Abbasian AraniAbstract:Abstract In the current research, Dynamic Viscosity of MWCNT(%40)-SiO2(%60)/5W50 nano-lubricant were investigated experimentally. Dynamic Viscosity of Nano-lubricant was measured at temperature range of 5 °C–55 °C, solid volume fraction between 0% and 1%, and fluid shear rate from 50 to 800 rpm. Study on rheological behavior of nanofluid against shear stress showed that the nanofluid has non-Newtonian behavior. For presenting a relation between relative Dynamic Viscosity and independent parameters two methods were employed that are: artificial neural network and mathematical correlation. Results showed that, proposed correlation can estimate the value of relative Dynamic Viscosity with an acceptable accuracy. As an example the coefficient of determination (R-squared) was 0.9914, which represents a desirable value. An artificial neural network (ANN) for relative Viscosity based on obtained data using the multi-layer perceptron (MLP) algorithm was designed. The results showed that the neural network with the appropriate instruction can estimate accurate value for Dynamic Viscosity.
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An experimental determination and accurate prediction of Dynamic Viscosity of MWCNT(%40)-SiO2(%60)/5W50 nano-lubricant
Journal of Molecular Liquids, 2018Co-Authors: Mohammad Hemmat Esfe, Ali Akbar Abbasian AraniAbstract:Abstract In the current research, Dynamic Viscosity of MWCNT(%40)-SiO2(%60)/5W50 nano-lubricant were investigated experimentally. Dynamic Viscosity of Nano-lubricant was measured at temperature range of 5 °C–55 °C, solid volume fraction between 0% and 1%, and fluid shear rate from 50 to 800 rpm. Study on rheological behavior of nanofluid against shear stress showed that the nanofluid has non-Newtonian behavior. For presenting a relation between relative Dynamic Viscosity and independent parameters two methods were employed that are: artificial neural network and mathematical correlation. Results showed that, proposed correlation can estimate the value of relative Dynamic Viscosity with an acceptable accuracy. As an example the coefficient of determination (R-squared) was 0.9914, which represents a desirable value. An artificial neural network (ANN) for relative Viscosity based on obtained data using the multi-layer perceptron (MLP) algorithm was designed. The results showed that the neural network with the appropriate instruction can estimate accurate value for Dynamic Viscosity.
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experimental determination of thermal conductivity and Dynamic Viscosity of ag mgo water hybrid nanofluid
International Communications in Heat and Mass Transfer, 2015Co-Authors: Mohammad Hemmat Esfe, Ali Akbar Abbasian Arani, Mohammad Rezaie, Weimon Yan, Arash KarimipourAbstract:Abstract The main goal of this experimental work is to investigate the effect of nanoparticle volume fraction on thermal conductivity and Dynamic Viscosity of Ag–MgO/water hybrid nanofluid with the particle diameter of 40(MgO) and 25(Ag) nm and nanoparticle volume fraction (50% Ag and 50% MgO by volume) range between 0% and 2% and presenting new correlations. Several existing theoretical and empirical correlations for thermal conductivity (four correlations) and Dynamic Viscosity (five correlations) of nanofluids have been examined for their accuracy in predicting the value of thermoDynamics properties by comparing the predicted values with experimental data. The examined correlations were found to present inaccuracies (under predictions) in the range of nanoparticle volume fraction under study. Predictions of the new developed correlations by comparing the predicted values with experimental data showed that the new correlations are within a very good accuracy.
Loke Kok Foong - One of the best experts on this subject based on the ideXlab platform.
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an experimental study on characterization stability and Dynamic Viscosity of cuo tio2 water hybrid nanofluid
Journal of Molecular Liquids, 2020Co-Authors: Amin Asadi, Ibrahim M. Alarifi, Loke Kok FoongAbstract:Abstract One of the essential specifications of nanofluids is the rheological behavior that has a significant effect on the system's performance. The primary objective of the present study is to investigate the rheological behavior and Dynamic Viscosity of the CuO-TiO2/water hybrid nanofluid. First of all, the chemical, atomic, and surface structures of the CuO and TiO2 nanoparticles were examined using X-ray diffraction, Fourier-Transform Infrared Spectroscopy, and Field Emission Scanning Electron Microscopy tests. Then, the two-step method was employed to prepare the needed samples. At the next step, the Dynamic Light Scattering test was used to examine the stability of the nanofluid. Furthermore, Dynamic Viscosity was measured in the different temperatures, ranging from 25 to 55 °C, and solid concentrations, ranging from 0.1 to 1 vol%. Moreover, the effect of shear rate on the rheological properties of the hybrid nanofluid was investigated. The results revealed that the prepared nanofluid is a Newtonian fluid. The results of Dynamic Viscosity measurements indicated that the maximum Dynamic Viscosity is at the solid concentration of 1 vol%, and a temperature of 25 °C. Finally, based on the obtained results, it can be said that considering different cost-benefit analyses, including high stability and low production cost, the studied nanofluids have a high quality and can be considered as an appropriate alternative for replacing the water-based fluid.
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An experimental study on characterization, stability and Dynamic Viscosity of CuO-TiO2/water hybrid nanofluid
Journal of Molecular Liquids, 2020Co-Authors: Amin Asadi, Ibrahim M. Alarifi, Loke Kok FoongAbstract:Abstract One of the essential specifications of nanofluids is the rheological behavior that has a significant effect on the system's performance. The primary objective of the present study is to investigate the rheological behavior and Dynamic Viscosity of the CuO-TiO2/water hybrid nanofluid. First of all, the chemical, atomic, and surface structures of the CuO and TiO2 nanoparticles were examined using X-ray diffraction, Fourier-Transform Infrared Spectroscopy, and Field Emission Scanning Electron Microscopy tests. Then, the two-step method was employed to prepare the needed samples. At the next step, the Dynamic Light Scattering test was used to examine the stability of the nanofluid. Furthermore, Dynamic Viscosity was measured in the different temperatures, ranging from 25 to 55 °C, and solid concentrations, ranging from 0.1 to 1 vol%. Moreover, the effect of shear rate on the rheological properties of the hybrid nanofluid was investigated. The results revealed that the prepared nanofluid is a Newtonian fluid. The results of Dynamic Viscosity measurements indicated that the maximum Dynamic Viscosity is at the solid concentration of 1 vol%, and a temperature of 25 °C. Finally, based on the obtained results, it can be said that considering different cost-benefit analyses, including high stability and low production cost, the studied nanofluids have a high quality and can be considered as an appropriate alternative for replacing the water-based fluid.
