Influence of Radiative Heat Energy on the MHD Flow of Cu-kerosene Nanofluid Over a Vertical Plate: Laplace Transform Technique

The present study reveals the heat transfer phenomena of magnetohydrodynamic (MHD) nanofluid over a moving vertical plate due to the interaction of thermal radiation. Due to enhancing thermal properties, instead of water, kerosene is treated as the base fluid with the inclusion of Cu (Copper) nanoparticles. In addition to that, for the nanofluid viscosity and thermal conductivity the Einstein’s model and Mintsa’s model, respectively. The transformed models for the governing equations proposed here are handled analytically employing Laplace Transform Technique. However, the variations of various parameters are obtained because the constructed flow phenomena are presented via graph. In the particular case, the consequence obtained is compared with the earlier study to get the validation which provides a road map for further investigation.

Thermal Conductivity and Viscosity: Review and Optimization of Effects of Nanoparticles

This review was focused on expressing the effects of base liquid, temperature, possible surfactant, concentration and characteristics of nanoparticles including size, shape and material on thermal conductivity and viscosity of nanofluids. An increase in nanoparticle concentration can lead to an increase in thermal conductivity and viscosity and an increase in nanoparticle size, can increase or decrease thermal conductivity, while an increase in nanoparticle size decreases the viscosity of the nanofluid. The addition of surfactants at low concentrations can increase thermal conductivity, but at high concentrations, surfactants help to reduce thermal conductivity of the nanofluid. The addition of surfactants can decrease the nanofluid viscosity. Increasing the temperature, increased the thermal conductivity of a nanofluid, while decreasing its viscosity. Additionally, the effects of material of nanoparticles on the thermal conductivity and viscosity of a nanofluid need further investigations. In the case of hybrid nanofluids, it was observed that nanofluids with two different particles have the same trend of behavior as nanofluids with single particles in the regard to changes in temperature and concentration. Additionally, the level of accuracy of existing theoretical models for thermal conductivity and viscosity of nanofluids was examined.

Comparative Analysis of Machine Learning Models for Nanofluids Viscosity Assessment

The process of selecting a nanofluid for a particular application requires determining the thermophysical properties of nanofluid, such as viscosity. However, the experimental measurement of nanofluid viscosity is expensive. Several closed-form formulas for calculating the viscosity have been proposed by scientists based on theoretical and empirical methods, but these methods produce inaccurate results. Recently, a machine learning model based on the combination of seven baselines, which is called the committee machine intelligent system (CMIS), was proposed to predict the viscosity of nanofluids. CMIS was applied on 3144 experimental data of relative viscosity of 42 different nanofluid systems based on five features (temperature, the viscosity of the base fluid, nanoparticle volume fraction, size, and density) and returned an average absolute relative error (AARE) of 4.036% on the test. In this work, eight models (on the same dataset as the one used in CMIS), including two multilayer perceptron (MLP), each with Nesterov accelerated adaptive moment (Nadam) optimizer; two MLP, each with three hidden layers and Adamax optimizer; a support vector regression (SVR) with radial basis function (RBF) kernel; a decision tree (DT); tree-based ensemble models, including random forest (RF) and extra tree (ET), were proposed. The performance of these models at different ranges of input variables was assessed and compared with the ones presented in the literature. Based on our result, all the eight suggested models outperformed the baselines used in the literature, and five of our presented models outperformed the CMIS, where two of them returned an AARE less than 3% on the test data. Besides, the physical validity of models was studied by examining the physically expected trends of nanofluid viscosity due to changing volume fraction.

Non-equilibrium natural convection in a differentially-heated nanofluid cavity partially filled with a porous medium

Purpose This study aims to numerically analyze natural convection of alumina-water nanofluid in a differentially-heated square cavity partially filled with a heat-generating porous medium. A single-phase nanofluid model with experimental correlations for the nanofluid viscosity and thermal conductivity has been considered for the description of the nanoparticles transport effect in the present study. Local thermal non-equilibrium approach for the porous layer with the Brinkman-extended Darcy model has been used. Design/methodology/approach Dimensionless governing equations formulated using stream function, vorticity and temperature have been solved by the finite difference method. The effects of the Rayleigh number, Ostrogradsky number, Nield number and nanoparticles volume fraction on nanofluid flow, heat and mass transfer have been analyzed. Findings It has been revealed that the dimensionless heat transfer coefficient at the fluid/solid matrix interface can be a very good control parameter for the convective flow and heat transfer intensity. The present results are original and new for the study of non-equilibrium natural convection in a differentially-heated nanofluid cavity partially filled with a porous medium. Originality/value The results of this paper are new and original with many practical applications of nanofluids in the modern industry.