Nanofluid

About: Nanofluid is a research topic. Over the lifetime, 23986 publications have been published within this topic receiving 677384 citations.

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

Abstract: 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.

Mixed convection flow in single- and double-lid driven square cavities filled with water–Al2O3 nanofluid: Effect of viscosity models

Abstract: This work is focused on the numerical modeling of steady laminar mixed convection flow in single and double-lid square cavities filled with a water–Al2O3 nanofluid. Two viscosity models are used to approximate nanofluid viscosity, namely, the Brinkman model and the Pak and Cho correlation. The developed equations are given in terms of the stream function–vorticity formulation and are non-dimensionalized and then solved numerically by a second-order accurate finite-volume method. Comparisons with previously published work are performed and found to be in good agreement. A parametric study is conducted and a selective set of graphical results is presented and discussed to illustrate the effects of the presence of nanoparticles and the Richardson number on the flow and heat transfer characteristics in both cavity configurations and to compare the predictions obtained by the two different nanofluid models. It is found that significant heat transfer enhancement can be obtained due to the presence of nanoparticles and that this is accentuated by increasing the nanoparticle volume fractions at moderate and large Richardson numbers using both nanofluid models for both single- and double-lid cavity configurations. However, for small Richardson number, the Pak and Cho model predicts that the presence of nanoparticle causes reductions in the average Nusselt number in the single-lid cavity configuration.