Viscosity Of Water-based Nanofluids Research Articles

A new pressure and temperature dependent relative viscosity model for water-Al2O3 nanofluids using GMDH neural network

The introduction of nanoparticles into fluids have been used in various sciences such as; heat transfer, drilling mud, hydraulic fracturing, EOR, and widely in biotechnology science. The dispersion of nanoparticles in the fluid can affect significantly the viscosity of the system. In this study, a self-organizing polynomial neural network based on a group method of data handling (GMDH) is developed to investigate the viscosity of water-based nanofluids regarding temperature and especially pressure changes. The influence of high pressure has not been previously considered in any correlation. The results were estimated using the GMDH model and have higher accuracy than various theoretical models and empirical equations and show the coefficient of determination of 0.999.

Effect of SWCNT volume fraction on the viscosity of water-based nanofluids

Nanofluids have received a great deal of interest in recent years because of their various unique features. According to the findings, the addition of nanotubes to the base materials can drastically alter their properties. In the present work, the viscosity of a typical water-based nanofluid containing single-walled carbon nanotubes is estimated using the molecular dynamics simulation for different volume fractions ranging between 0.557 and 3% at two temperatures (298K and 313K). The temperature of the systems is controlled using a Nose-Hoover thermostat. For calculating viscosity, the Green-Kubo equilibrium method is used. The enthalpy, potential, kinetic, and total energies are calculated to determine the system's stability. In addition, the influence of molecular mass on these energies is studied. The nanotube under investigation is an armchair(6,6)-type single-walled carbon nanotube. The results highlight the promise of the molecular dynamics simulation technique as a powerful tool in the prediction of nanofluid properties besides the experimental results. The value of viscosity will decrease as the temperature rises, much like the base fluid. Furthermore, it is shown that the viscosity is proportional to the volume fraction of water-SWCNT nanofluid. According to the results, a new viscosity relationship for volume fractions in the range of ϕ ≤ 3% is proposed. The viscosity, temperature, and volume fraction are all linked together in this equation.

Designing a committee of machines for modeling viscosity of water-based nanofluids

Viscosity is a crucial thermophysical feature of a substance that must be accurately determined before designing a system with nanofluid as the working fluid. In this study, the modern technique of committee machine intelligent system (CMIS) is used for establishing a predictive model for the relative viscosity of the water-based nanofluids. The model was developed by considering 1440 experimental data points of different types of water-based nanofluids containing Al2O3, SiC, SiO2, TiO2, CuO, nanodiamond, and Fe3O4 nanoparticles. The CMIS model combines three intelligent models including a multilayer perceptron (MLP) model trained with Levenberg-Marquardt (LM), an MLP model trained by Bayesian Regularization (BR) and a radial basis function (RBF) approach to estimate the relative viscosity of different water-based nanofluids. Statistical and graphical error criteria revealed that the CMIS technique successfully estimates the relative viscosity of all data points over the whole ranges of operational conditions with a mean absolute relative error of approximately 1.25%. According to their precision and performance, the established CMIS system provides the best performance, followed by the BR-MLP, LM-MLP, and RBF models. Moreover, the performance and estimation capability of the CMIS model was verified against 13 theoretical and empirical models.

Experimental Study on Thermal Conductivity and Viscosity of Water-Based Nanofluids

nanofluids were investigated for various volumefractions of nanoparticles content and at different temperatures. A 3ωtechnique was developed for measuring thermal conductivity of nano-fluids. The theory and the experimental setup of the 3ω measuringsystem is explained; a conductive wire is used as both heater and sen-sor in this system. At first, the system is calibrated using water withknown thermophysical properties. Measured results showed that theeffective thermal conductivity of nanofluids increases as the concen-tration of the particles increases but not anomalously as indicated inthe majority of the literature and this enhancement is very close tothe Hamilton–Crosser model; also this increase is independent of thetemperature. The effective viscosities of these nanofluids increase by