Thermal Conductivity Of Water-based Nanofluids Research Articles

Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids using Machine-Learning Techniques

In this study, a variety of machine-learning algorithms are used to predict the viscosity and thermal conductivity of several water-based nanofluids. Machine learning algorithms, namely decision tree, random forest, extra tree, KNN, and polynomial regression, have been used, and their performances have been compared. The input parameters for the prediction of the thermal conductivity of nanofluids include temperature, concentration, and the thermal conductivity of nanoparticles. A three-input and a two-input model were utilized in modelling the viscosity of nanofluid. Both models considered temperature and concentration as input parameters, and additionally, the type of nanoparticle was considered for the three-input model. The order of importance of the most influential parameters in predicting both viscosity and thermal conductivity was studied. A wider range of input parameters have been considered in an open-access database. With the existing experimental data, all of the developed machine learning models exhibit reasonable agreement. Extra trees were found to provide the best results for estimating thermal conductivity, with a value of 0.9403. In predicting viscosity using a three-input model, extra trees were found to provide the best result with a value of 0.9771, and decision trees were found to provide the best results for estimating the viscosity using a two-input model with a value of 0.9678. In order to study heat transport phenomena through mathematical modelling, it is important to have an explicit mathematical expression. Therefore, the formulation of mathematical expressions for predicting viscosity and thermal conductivity has been carried out. Additionally, a comparison with the Xue and Maxwell thermal conductivity models is made to validate the results of this study, and the results are observed to be reliable.

Comparison of thermal conductivity of water-based nanofluids with various combinations of MWCNT, CuO, and SiO2 nanoparticles for using in heating systems

The application of nanofluids (NFs) with improved efficiency and performance is currently a major problem for many industrial sectors. By comparing two water-based NFs (with identical nanoparticles (NPs) but with different participation percentages), this research is attempted to present a more optimal NF for industrial applications. As a result, MWCNT (50%)-CuO(25%)-SiO2(25%)/Water NFs and MWCNT (10%)-CuO (20%)-SiO2(70%)/Water NFs were synthesized, and their thermal conductivities (TC) were compared. In the same experimental settings as the second NF, the first NF with the mixture of MWCNT (50%)-CuO (25%)-SiO2(25%)/Water possesses more thermal conductivity enhancement (TCE). TCE increases by 37.10% for the first NF but only by 16.20% for the second.

Enhanced thermophysical properties of water-based single and hybrid metallic nanofluids: Insights from Equilibrium Molecular Dynamics

The effect of single (Cu or Ag) and hybrid (Cu+Ag) metallic nanoparticles on the thermophysical properties, namely viscosity and thermal conductivity of water-based nanofluids, has been studied using Equilibrium Molecular dynamics (EMD) simulation. The TIP3P (three-site transferrable intermolecular potential) water model has been chosen. The interaction of water molecules has been modelled by the Lennard-Jones (L J) potential in combination with Coulomb potential. The embedded-atom (EAM) potential method has been used for hybrid (Cu and Ag) atom interaction. Simulation has been performed at 303 K and atmospheric pressure using the Berendsen algorithm under NVT (constant number, constant volume, and constant temperature) ensemble with production steps of 2 ns and integral step of 1fs. Interestingly, nanofluids containing one metallic nanoparticle (Cu or Ag) have lower thermal conductivity and viscosity than nanofluids having hybrid metallic nanoparticles with the same volume fraction.

An Expert Model Based on Physics-Aware Neural Network for the Prediction of Thermal Conductivity of Nanofluids

Abstract Operating fluids are always a significant factor for not achieving a good enough performance of heat transfer equipment and also for growing the energy costs. To resolve this issue, nanofluids are considered a potential choice for conventional heat transfer fluids due to their efficiency for the improvement of overall thermal performance. The aim of this research is to propose a physics-guided machine learning approach by incorporating physics-based relations at the initial stage and into traditional loss functions for predicting the thermal conductivity of water-based nanofluids using a wide range of both experimental and simulated data of nanoparticles Al2O3, CuO, and TiO2. Further, smart connectionist methods, viz., ridge regression, lasso regression, random forest, extreme gradient boosting (xgboost (XGB)), and black-box multilayer perceptron (MLP) are applied to compare the present physics-aware MLP model based on different statistical indicators. The accuracy analyses reveal that the use of physical views to monitor the learning of neural networks shows better results with mean absolute percentage error (MAPE) = 0.7075%, root-mean-squared error (RMSE) = 0.0042 W/mK, and R2 = 0.9525. The temperature and volume concentration variations are discussed graphically. Furthermore, the outcomes of applied algorithms confirm that the well-known theoretical and computer-aided models show substandard results than the proposed model.

EMD analysis on the impact of temperature, volume fraction and molecular weight on the thermal conductivity of water-based nanofluids

The development of high-performance thermal systems for heat transfer enhancement is crucial since the reliability and performance of chips and processors are heavily dependent on the operating temperature. The space constraints also need to be simultaneously met. Miniaturization, optimum power consumption and enhancement in the efficiency of electronic systems can be achieved by the judicious selection of nanofluids. But the enhancement in thermal conductivity of nanofluids is dependent on a number of parameters such as volume fraction of nanoparticles, nanoparticle used, size and shape of the nanoparticle, base fluid used and temperature. For studying the impact of volume fraction, system temperature and weight of nanoparticle on thermal conductivity enhancement of water-based aluminium/aluminium oxide nanofluids, equilibrium molecular dynamics is applied. The numerical estimation of thermal conductivity is performed in the temperature range of 290–350 K for volume fraction ranging between 1 and 4%. From the analysis, it is observed that although there is a definite thermal conductivity enhancement for nanofluids when compared to water, an increase in volume fraction and temperature may not always result in a proportionate enhancement. At low temperatures, a nanofluid with a heavier nanoparticle and low volume fraction is effective, but the enhancement at higher temperatures and higher volume fractions is found to be more for a nanofluid having a lighter nanoparticle. Hence, in addition to temperature and volume fraction, weight of the nanoparticle is also a critical factor in the selection of nanofluids. For a given nanoparticle, there exist a particular temperature and volume fraction at which the nanofluid gives maximum heat transfer effectiveness. The base fluid used is also important as nanofluids exhibit a delayed convection onset when compared to base fluid, and the delay is more as volume fraction increases. Nanofluid is more effective in heat transfer only when its layer thickness is greater than the thickness required for convection to onset.

The thermal conductivity modeling of nanofluids involving modified Cu nanorods by Ag nanoparticles

Single Cu nanorods and decorated Cu nanorods with Ag nanoparticles (Cu-Ag nanorods) are synthesized by hydrothermal procedure for investigation the thermal conductivity of water-based nanofluids. The characterization of the synthesized samples is carried out using XRD and TEM. The XRD results show that the observed characteristic peaks belong to crystalline structure of Cu and Ag. The results of TEM reveal that spherical Ag nanoparticles with attach on the surface of Cu with rod morphology. The thermal conductivity variation of the prepared nanofluids is studied by changing the temperature (ranging from 25°C to 55°C) and weight fraction (ranging from 0.25%wt to 1%wt). The results show that the thermal conductivity enhances by increasing the both of temperature and weight fraction. The collation between the thermal conductivity of Cu nanofluids and Cu-Ag nanofluids verifies that the thermal conductivity of Cu-Ag nanofluids is higher than that of Cu nanofluids. Theoretical models compare the measured values of the thermal conductivity in the both nanofluids. The results show that in both of studied nanofluids, the measured magnitude of thermal conductivity is more than that of estimated amount by theoretical equations.

The effect of surfactant and sonication time on the stability and thermal conductivity of water-based nanofluid containing Mg(OH)2 nanoparticles: An experimental investigation

The main objective of the present study was to investigate the effect of surfactant and sonication time on the stability and thermal conductivity of Mg (OH)2-water nanofluid over the different solid concentrations (0.1, 0.2, 0.4, 0.8, 1, 1.5, and 2%) and temperatures (25, 30, 35, 40, 45, 50°C). The nanofluid has been prepared using two-step method and applying different sonication time (10, 30, 50, 80, and 160min). Furthermore, three different surfactants (CTAB, SDS, Oleic Acid) have been added during the preparation process so as to investigate which surfactant shows better impact on the stability of the nanofluid. The results of Zeta potential analysis indicated that CTAB surfactant shows the best impact on the stability of the nanofluid applying 30-min sonication. Furthermore, it is found that the relative thermal conductivity of the studied nanofluid relatively increased as the solid concentration and temperature increased. The maximum increase in thermal conductivity took place at the solid concentration of 2% and temperature of 50°C by approximately 28%. Moreover, in order to investigate the effect of sonication time of thermal properties of the nanofluid, the relative thermal conductivity of the nanofluid versus sonication time in different solid concentrations and at the temperature of 30°C has been measured. The results indicated that the relative thermal conductivity showed decreasing trend as the sonication time increased in all the selected solid volume fractions.

Phenomenological formula for thermal conductivity coefficient of water-based nanofluids

A phenomenological formula has been proposed to describe the thermal conductivity of waterbased nanofluids. The formula has been derived based on available experimental data on nanofluids containing Al2O3 particles. It takes into account the dependence of the thermal conductivity coefficients of the nanofluids on both volume concentration and sizes of the particles. The formula has also been shown to describe with an accuracy of about 3% the thermal conductivity coefficients of nanofluids containing TiO2, SiO2, ZrO2, and CuO particles with sizes of 8–150 nm and volume concentrations as high as 8%.

Erratum to: Study on thermal conductivity of water-based nanofluids with hybrid suspensions of CNTs/Al2O3 nanoparticles

The aim of this work was to investigate the effects of temperature and solid volume fraction on thermal conductivity of CNTs–Al2O3/water nanofluids. Both Al2O3 nanoparticles and CNTs are dispersed in the base fluid with equal solid volume. Experiments were conducted with various solid volume fractions of 0.02, 0.04, 0.1, 0.2, 0.4, 0.8 and 1.0 % and various fluid temperatures of 303, 314, 323 and 332 K. Measured data reveal that the thermal conductivity of nanofluid highly depends on the solid volume fraction. Also, temperature may play an important role in enhancing thermal conductivity of CNTs–Al2O3/water, especially at high solid volume fractions. Based on experimental data, correlations are proposed for different temperatures by nonlinear regression. These correlations are able to predict thermal conductivity of nanofluid with high precision. Besides, a general correlation of thermal conductivity with function of temperature and solid volume fraction was proposed.

Study on thermal conductivity of water-based nanofluids with hybrid suspensions of CNTs/Al2O3 nanoparticles

Study on thermal conductivity of water-based nanofluids with hybrid suspensions of CNTs/Al2O3 nanoparticles

Influence of Controlled Aggregation on Thermal Conductivity of Nanofluids

Nanoparticles aggregation is considered, by the heat transfer community, as one of the main factors responsible for the observed enhancement in the thermal conductivity of nanofluids. To gain a better insight into the veracity of this claim, we experimentally investigated the influence of nanoparticles aggregation induced by changing the pH value or imposing a magnetic field on the thermal conductivity of water-based nanofluids. The results showed that the enhancement in thermal conductivity of TiO2–water nanofluid, due to pH-induced aggregation of TiO2 nanoparticles, fell within the ±10% of the mixture theory, while applying an external magnetic force on Fe3O4–water nanofluid led to thermal conductivity enhancements of up to 167%. It is believed that the observed low enhancement in thermal conductivity of TiO2–water nanofluid is because, near the isoelectric point (IEP), the nanoparticles could settle out of the suspension in the form of large aggregates making the suspension rather unstable. The magnetic field however could provide a finer control over the aggregate size and growth direction without compromising the stability of the nanofluid, and hence significantly enhancing the thermal conductivity of the nanofluid.

Thermal and Rheological Properties of Water-based Ferrofluids and Their Applicability as Quenching Media

Water-based ferrofluids present a new energy transfer fluid with tunable properties. Previous research has shown the increase in thermal conductivity of water-based nanofluids with the addition of iron oxide. Such increased thermal properties show great potential for use in heat transfer. In this paper, several nanofluids were prepared with two step method. Iron (II, III) oxide nanoparticles with average paerticle size less than 50nm were added to deionized water in following concentration: 0.01, 0.1, 0.5 and 1g/L. Their thermal and rheological properties were measured at 20, 40 and 60°C. Results showed increase in thermal conductivity and viscosity with increase in the addition of nanoparticles at all three temperature levels. The biggest increase was observed at 20°C. For this research, all of the prepared nanofluids were tested as immersion quenching liquid according to ISO 9950 standard. Besides still conditions, quenching experiments were conducted under the magnetic field at two levels, 500 and 1000 Gauss. The magnetic field effect was least present at 60°C with almost no influence on the cooling curve and cooling rates. At lower temperature levels quenching under the magnetic field shortened the full film boiling phase and increased the maximum cooling rate.

Lignin as dispersant for water-based carbon nanotubes nanofluids: Impact on viscosity and thermal conductivity

The viscosity and thermal conductivity of water-based nanofluids containing carbon nanotubes, stabilized by lignin as surfactant, are measured. These experiments were performed at 20°C and we study the effect of nanotube volume fraction which ranges from 0.0055% to 0.55%. In comparison with SDBS, we show that lignin reduces viscosity and shear-thinning behavior of nanofluids at high volume fraction, without penalizing thermal conductivity enhancement. Viscosity enhancement at high shear rate with respect to nanoparticle content is well modeled by the Maron–Pierce equation, suggesting that lignin is better than SDBS at dispersing carbon nanotubes within water at high volume fraction. The impact of surfactant on base fluid thermal conductivity is also reported and thermal conductivity enhancement of nanofluids with nanoparticle volume fraction is finally compared with previous published models.

Thermal conductivity of Water-based nanofluids: Prediction and comparison of models using machine learning

Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learning models were proposed to represent the thermal conductivity as a function based on the temperature, nanoparticles volume fraction and the thermal conductivity of the nanoparticles. The results of models were in appropriate agreement with the experimental data. This work represents 8 machine learning models for the predicting the thermal conductivity of water-based nanofluids. The models have been trained and tested on two separate sets of data. Three metrics have been employed to evaluate the performance of the models. The best method for each system is selected using results.