Viscosity of nanofluids can significantly affect pumping power, pressure drop, workability of the nanofluid as well as its convective heat transfer coefficient. Experimental measurements of this property for different nanoparticles and base fluids at various temperatures is cumbersome and expensive. In this communication, a comprehensive review of the most important modeling works on viscosity of nanofluids including theoretical models, empirical correlations, and computer-aided models is conducted. Next, four multilayer perceptron (MLP) models optimized with Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled conjugate gradient (SCG), and Resilient Backpropagation (RB), two radial basis function (RBF) neural network models optimized with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and one least square support vector machine (LSSVM) model optimized with coupled simulated annealing (CSA) were developed for the prediction of nanofluid viscosity based on 3144 data points. These data sets include 42 nanofluid systems under a wide range of operating conditions; including temperature from −35 to 80 °C, particle volume fraction from 0% to 10%, nanoparticle size from 4.6 to 190 nm, and viscosity of base fluid from 0.24 to 452.6 cP. Then, these seven models were combined in a single model using a committee machine intelligent system (CMIS). The proposed CMIS predicts all of the data with excellent accuracy with an average absolute relative error of less than 4%. Furthermore, the developed model was compared with five theoretical models and four empirical correlations through statistical and graphical error analyses. The results demonstrate that the proposed CMIS model significantly outperforms all of the existing models and correlations in terms of accuracy and range of validity. Finally, the quality of the experimental data was examined both graphically and statistically and the results suggested good reliability of the experimental data.

On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment

The enhanced thermal characteristics of nanofluids have made it one of the most raplidly growing research areas in the last decade. Numerous researches have shown the merits of nanofluids in heat transfer equipment. However, one of the problems is the increase in viscosity due to the suspension of nanoparticles. This viscosity increase is not desirable in the industry, especially when it involves flow, such as in heat exchanger or microchannel applications where lowering pressure drop and pumping power are of significance. In this regard, a critical review of the theoretical, empirical, and numerical models for effective viscosity of nanofluids is presented. Furthermore, different parameters affecting the viscosity of nanofluids such as nanoparticle volume fraction, size, shape, temperature, pH, and shearing rate are reviewed. Other properties such as nanofluid stability and magnetorheological characteristics of some nanofluids are also reviewed. The important parameters influencing viscosity of nanofluid...

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The Viscosity of Nanofluids: A Review of the Theoretical, Empirical, and Numerical Models

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 predict the viscosity in terms of temperature, solid volume fraction and shear stress. to compare the prediction 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.

Rheological behavior characteristics of TiO2-MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: An experimental study and a neural network simulating

A review on current trends in potential use of metal-organic framework for hydrogen storage

CO2 separation plays an important role in energy saving and CO2 emission reduction, both of which are necessary to address the issue of global warming Ionic liquids (ILs) have been proposed to be “green” solvents for CO2 separation Unfortunately, the high cost, toxicity, and poor biodegradability of these compounds limit their large-scale application Deep eutectic solvents (DESs) were recently considered a new type of IL with additional advantages in terms of cost, environmental impact, and synthesis DESs based on choline salts (ie, choline-based DESs) are promising candidates for CO2 separation In this work, the microstructures, physicochemical properties, and water effect of choline-based DESs are surveyed and compared with those of conventional ILs The properties of choline-based DESs are similar to those of conventional ILs, but research on the latter remains limited Further study on the microstructures, properties, and separation performance of choline-based DESs considering dynamic factors must be carried out through experimental measurements and model development Thermodynamic analysis based on Gibbs free energy change is conducted to investigate the performances of choline-based-DESs during CO2 separation from biogas Choline-based-DESs are screened on the basis of energy use and amount of absorbent needed The performances of the screened choline-based-DESs are further compared with those of conventional ILs screened in our previous work, as well as commercial CO2 absorbents Comparisons indicate that the screened DES-based absorbents show great application potential due to their nonvolatility, low energy use, or low amount required The performances of physical choline-based-DES and 30 wt% MEA for CO2 separation from other gas streams (eg, flue gas, lime kiln gas, and bio-syngas) are discussed Considering the high amounts of physical absorbents required to enable separation, further study with techno-economic analysis needs to be carried out

Choline-based deep eutectic solvents for CO2 separation: Review and thermodynamic analysis

Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system

The objective of this study is to develop a model to determine the thermal conductivity of pure ionic liquids and ionanofluids. In order to estimate the thermal conductivity of pure ionic liquids, a group method of data handling model is proposed based on 23 ionic liquids corresponding to 216 experimental data points. The average absolute relative deviation for all studied systems was 1.79%, which is a satisfactory degree of accuracy for the proposed model. Furthermore, the Maxwell model is modified to correlate the thermal conductivity of ionanofluids as a function of temperature and volume fraction of nanoparticles. The average absolute relative deviation for this model is 0.61%. Additionally, Maxwell and modified geometric mean (mGM) models are used to evaluate the models that predict the thermal conductivity of ionanofluids. The results show that mGM is more accurate for prediction of thermal conductivity of ionanofluids.

Modeling the Thermal Conductivity of Ionic Liquids and Ionanofluids Based on a Group Method of Data Handling and Modified Maxwell Model

Eyring’s absolute rate theory was applied for evaluation of the viscosity of ionic liquids (ILs) containing binary mixtures. Considering the mathematical simplicity of the two-suffix-margules model, the Gibbs energy model was further modified. Furthermore, for viscosity evaluation, the proposed Gibbs energy model was coupled with Eyring’s theory. To validate the accuracy of the proposed model, a large set of data containing the binary mixtures of 122 ILs with a total number of 5512 experimental data points was collected from the literature. Moreover, the average absolute relative deviation (AARD %) was obtained as 2.07 %. Also, the capability of the Eyring–MTSM model was tested for the prediction of viscosity for binary and ternary systems. Additionally, comparison of the proposed model with the Eyring–NRTL model indicated a higher accuracy for our model. Finally, the Eyring–UNIFAC model was also checked, and it was found that this model is not accurate enough in its present form.

Estimation of the Viscosity of Ionic Liquids Containing Binary Mixtures Based on the Eyring’s Theory and a Modified Gibbs Energy Model

Modeling gas/vapor viscosity of hydrocarbon fluids using a hybrid GMDH-type neural network system

The group method of data handling (GMDH) was utilized to estimate the surface tension of 59 ionic liquids. In this regard, an extensive experimental data was selected from literature over the range 268.3 to 532.4 K. The GMDH model can predict the surface tension of ionic liquids by a grand polynomial correlation function of molar density, reduced boiling temperature, reduced temperature and pressure, acentric factor, and critical compressibility factor. The values of the GMDH model showed a very good regression with the experimental data. The average absolute relative deviation (AARD%) of the GMDH model for all ionic liquids was 4.59 % which indicated a good precision in comparison with those obtained from the generalized dimensionless equation, Mousazadeh–Faramarzi equation, and Parachor equation with AARDs% of 10.31, 13.02, and 11.89, respectively.

Saeid Atashrouz

Papers

Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system

Modeling the Thermal Conductivity of Ionic Liquids and Ionanofluids Based on a Group Method of Data Handling and Modified Maxwell Model

Estimation of the Viscosity of Ionic Liquids Containing Binary Mixtures Based on the Eyring’s Theory and a Modified Gibbs Energy Model

Modeling gas/vapor viscosity of hydrocarbon fluids using a hybrid GMDH-type neural network system

Modeling of surface tension for ionic liquids using group method of data handling