Conductivity Of Ethylene Glycol-based Nanofluids Research Articles

Thermal conductivity of ethylene glycol-based nanofluid containing SiO2 nanoadditives: experimental data and modeling through curve fitting

The work focuses on an experimental evaluation of the changes of thermal conductivity (TC) of ethylene glycol-based nanofluid containing SiO2 nanoadditives against volume concentration of nanoadditives (φ) and temperature. The experiments are carried out in the φ range of 0–2.5% and temperature range of 30–55 °C. The dynamic light scattering method is used to obtain the particle size distribution, while the transmission electron microscopy technique is utilized to visualize agglomerated particles in the prepared nanofluid samples. The outcomes revealed that the TC of the nanofluid grows by boosting both the φ and temperature. The percentage enhancement varied in the range of 0.72–26.66%. Furthermore, the curve-fitting method was utilized to model the TC of the nanofluid using experimental data. It was found that the developed model is able to properly forecast the TC of nanofluid with the maximum percentage error of 1.75%.

Modeling thermal conductivity of ethylene glycol-based nanofluids using multivariate adaptive regression splines and group method of data handling artificial neural network

Augmenting the thermal conductivity (TC) of fluids makes them more favorable for thermal applications. In this regard, nanofluids are suggested for achieving improved heat transfer owing to their modified TC. The TC of the base fluid, the volume fraction and mean diameter of particles, and the temperature are the main elements influencing the TC of nanofluids. In this article, two approaches, namely multivariate adaptive regression splines (MARS) and group method of data handling (GMDH), are applied for forecasting the TC of ethylene glycol-based nanofluids containing SiC, Ag, CuO, , and MgO particles. Comparison of the data forecast by the models with experimental values shows a higher level of confidence in GMDH for modeling the TC of these nanofluids. The values determined using MARS and GMDH for modeling are 0.9745 and 0.9332, respectively. Moreover, the importance of the inputs is ranked as volume fraction, TC of the solid phase, temperature and particle dimensions.

Experimental study on the thermal conductivity of ethylene glycol-based nanofluid containing Gr-CNT hybrid material

In this paper, we present the results of the preparation and thermal conductivity of ethylene glycol-based nanofluid containing graphene‑carbon nanotube (Gr-CNT) hybrid material. First, Gr and CNT were surfaces modified with different functional groups including hydroxyl (-OH) and carboxyl (-COOH), then mixed to attain Gr-CNT (1:1, v/v) hybrid material. The hybrid material was then dispersed in ethylene glycol (EG) to obtain hybrid nanofluid by ultrasonication technique. Thermal conductivity of nanofluids was measured using guarded hot plate (GHP) technique. The nanofluid containing 0.07 vol% Gr-CNT hybrid material showed the enhancement in thermal conductivity of 18 and 50% at 30 and 50 °C, respectively. The enhancement was attributed to the combination of high thermal conductivity of CNT and Gr and high surface area of Gr-CNT hybrid structure. By analyzing the experimental results using EMT and Murshed model, the thermal boundary resistance (TBR), thermal boundary conductance (TBC) and interface layer thermal conductivity (Ki) between Gr-CNT hybrid material and EG were estimated to be 0.8 × 10−8 m2 kW−1, 125 MW m−2 K−1 and 20.5 Wm−1·K−1, respectively.

Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al2O3 nanoparticles by correlation and ANN methods using experimental data

In the present paper, the effects of temperature and volume fraction on thermal conductivity of SWCNT–Al2O3/EG hybrid nanofluid are investigated. Single-walled carbon nanotube with outer diameter of 1–2 nm and aluminum oxide nanoparticles with mean diameter of 20 nm with the ratio of 30 and 70%, respectively, were dispersed in the base fluid. The measurements were conducted on samples with volume fractions of 0.04, 0.08, 0.15, 0.3, 0.5, 0.8, 1.5 and 2.5. In order to investigate the effects of temperature on thermal conductivity of the nanofluid, this characteristic was measured in five different temperatures of 30, 35, 40, 45 and 50 °C. The results indicate that enhancement of nanoparticles’ thickness in low volume fractions and at any temperature causes a considerable increment in thermal conductivity of the nanofluid. In this study, the highest enhancement of thermal conductivity was 41.2% which was achieved at the temperature of 50 °C and volume fraction of 2.5%. Based on the experimental data, an experimental correlation and a neural network are presented and for thermal conductivity of the nanofluid in terms of volume fraction and temperature. Comparing outputs of the experimental correlation and the designed artificial neural network with experimental data, the maximum error values for the experimental correlation and the artificial neural network were, respectively, 2.6 and 1.94% which indicate the excellent accuracy of both methods in prediction of thermal conductivity.

Strong enhancement in thermal conductivity of ethylene glycol-based nanofluids by amorphous and crystalline Al2O3 nanoparticles

In the present work, the temperature and concentration dependence of thermal conductivity (TC) enhancement in ethylene glycol (EG)-based amorphous and crystalline Al2O3 nanofluids have been investigated at temperatures ranging from 0 to 100 °C. In our prior study, nanometer-sized particles of amorphous-, γ-, and α-Al2O3 were prepared via a simple sol-gel process with annealing at different temperatures and characterized by various techniques. Building upon the earlier study, we probe here the crystallinity, microstructure, and morphology of the obtained α-Al2O3 nanoparticles (NPs) by using X-ray powder diffraction with Rietveld full-profile refinement, scanning electron microscopy, and high-resolution transmission electron microscopy, respectively. In this study, we achieved a 74% enhancement in TC at higher temperature (100 °C) of base fluid EG by incorporating 1.0 vol. % of amorphous-Al2O3, whereas 52% and 37% enhancement is accomplished by adding γ- and α-Al2O3 NPs, respectively. The amorphous phase of NPs appears to have good TC enhancement in nanofluids as compared to crystalline Al2O3. In a nutshell, these results are demonstrating the potential consequences of Al2O3 NPs for applications of next-generation efficient energy transfer in nanofluids.