Thermal Conductivity Enhancement Of Nanofluids Research Articles

Novel strategy of mixing MgO in CuO/water nanofluid for thermal conductivity improvement: Experimental study

The objective of the research is to address how polyhedron shaped MgO nanoparticles can solve the poor thermal conductivity issues due to spherical form of CuO nanoparticles. The novelty is the enhancement of thermal conductivity due to the combined effect of specific heat of MgO and two-step approach for the preparation of nanofluid. In the current study, the novel strategy for addition of fraction of MgO nanoparticles to improve the inferior thermal conductivity (TC) resulted due to agglomeration of CuO in water is addressed with proper experimental data. This novel approach was done with CuO/MgO weight ratio 7:3 and at volume fraction (VOF) of nanoparticles from 0.25 to 1.5 % in water from 30 to 55 °C. The present investigation resulted that marginal improvement in TC with a well defined contribution of nanoparticle in base fluid. Additionally, the outcome demonstrated that, particularly at higher temperatures, the thermal conductivity enhancement of nanofluid was proportional to both volume fraction and temperature. One such evidence was 12.5 % increase in TC at 55 °C and 1.5 % VOF. Likewise, the thermal conductivity's experimental result at various temperature and particle fraction was highlighted with proper literatures.

Experimental and Theoretical Studies of Different Parameters on the Thermal Conductivity of Nanofluids.

This work experimentally investigated the effects of different factors, including nanoparticle size and type, volume fraction, and base fluid, on the thermal conductivity enhancement of nanofluids. The experimental results indicate that the thermal conductivity enhancement of nanofluids is proportional to the thermal conductivity of the nanoparticles, with the enhancement being more pronounced for fluids with lower thermal conductivity. Meanwhile, the thermal conductivity of nanofluids decreases with increasing particle size and increases with increasing volume fraction. In addition, elongated particles are superior to spherical ones for thermal conductivity enhancement. This paper also proposes a thermal conductivity model by introducing the effect of nanoparticle size based on the previous classical thermal conductivity model via the method of dimensional analysis. This model analyzes the magnitude of influencing factors on the thermal conductivity of nanofluid and proposes suggestions for an improvement in thermal conductivity enhancement.

Anomalous behavior of liquid molecules near solid nanoparticles: Novel interpretation on thermal conductivity enhancement in nanofluids

HypothesisRecently, it has been reported that anomalous improvement in the thermal conductivity of nanofluid composed of base liquids and dispersed solid nanoparticles, compared to the theoretically predicted value calculated from the particle fraction. Generally, the thermal conductivity values of gases and liquids are dominated by the mean free path of the molecules during translational motion. Herein, we present solid evidence showing the possible contribution of the vibrational behavior of liquid molecules around nanoparticles to increasing these thermal conductivities. ExperimentsThe behavior of liquid molecules in nanofluids containing SiO2 particles larger than 100 nm, which were dispersed in a 50 wt% aqueous solution of ethylene glycol, was investigated by means of small-angle neutron scattering, quasi-elastic neutron scattering, wide-angle X-ray scattering, and Raman spectroscopy. FindingsThe vibrational changes in the liquid molecules caused by the interactions between the nanoparticles and liquid molecules surrounding the nanoparticles contributed majorly to the increase in the thermal conductivity values of the SiO2 nanofluids. Because the vibration of liquid molecules is equivalent to phonon conduction in solids, the increase in thermal conductivity of the suspension due to the presence of nanoparticles was inferred to be derived from the limitation of the translational diffusion, which induces a solid-like behavior in the liquid.

Influence of MgO concentration and water content on thermal conductivity enhancement of nanofluids based on deep eutectic solvent (choline chloride:glycerol)

The development and extensive studies on cost-effective and eco-friendly nanofluids with efficacious thermophysical properties are essential regarding the ever-increasing concerns about environmental hazards. Thus, the present research intended to offer novel green nanofluids based on a two-step method for the dispersion of magnesium oxide (MgO) nanoparticles in two binary DESs and four ternary DESs. The binary DESs were composed of choline chloride (ChCl) and glycerol (Gly) in 1:2 and 1:5 M ratios, and the four ternary DESs consisted of ChCl, Gly, and water in 1:2:1 1:2:2, 1:5:1, and 1:5:2 M ratios. Dynamic light scattering analyzed the stability of samples for several days. Moreover, a comprehensive experimental analysis of thermophysical characteristics of DESs and DES-based nanofluids was executed by defining specific classifications, including type of base solvents, MgO mass concentration (1, 5, and 10 wt%), and temperature range (283.15 to 333.15 K). In addition, the isobaric thermal expansivities were calculated by empirical data. The experimental findings revealed that adding nanoparticles and water content in DES structure caused an augment in thermal conductivity. Around 4–21% increase in thermal conductivity was observed for nanofluids made up of binary DESs, and around 10% for those made up of ternary DESs. Furthermore, DES-based nanofluids showed an average 1.2% drop in density versus temperature, while around 3% enhancement was achieved for 5 wt% MgO suspended in 1ChCl:2Gly. Based on comparisons with the most relevant nanofluids reported in the previous studies, it was determined that the nanofluids presented in the current study were superior. Eventually, it was demonstrated that the nanofluid of 10 wt% MgO in binary 1ChCl:5Gly mixture achieved the highest thermal conductivity (0.304 W.m-1K−1) can be considered as a high-potential candidate for heat transfer purposes.

Significant enhancement of thermal conductivity in toluene-based nanofluids employing highly dispersed and concentrated ZrO2 nanodots

This study deals with the influence of high concentration and high dispersion nanodots in toluene-based fluids on the improvement of the thermal conductivity of ZrO2/toluene nanofluids by experimental measurement and theoretical analysis. For this purpose, the high concentration (13.21 vol%) ZrO2/toluene nanofluids were diluted to 2.20%, 4.41%, 6.61%, 8.81% and 11.01%, respectively. And their thermal conductivities were measured using the transient planar heat source method (TPHS). Subsequently, the predicted values of four classical theoretical models were compared and discussed with experimental values in detail to verify the applicability of theoretical models. The results indicate that almost all of the thermal conductivities of ZrO2/toluene nanofluids are higher than that of the pure toluene fluid, and the trend is rising with the increase in temperature. Moreover, the thermal conductivity enhancement of ZrO2/toluene nanofluid with 13.21 vol% has achieved 38.7% and 59.9% at room temperature and 62.8 °C, respectively. The visualized behaviors of nanodots, including migration, collision, energy exchange between the nanodots reveal the physical mechanism that contributes to the thermal conductivity of nanofluids. And the thermal conductivity enhancement of nanofluid per unit concentration quantifies the mechanism. The Brownian motion of nanodots may play a crucial role, which influences the move, collision, and micro-convection. The predicted values of four models were compared and discussed with experimental values to verify the applicability of theoretical models. The analysis reveals that all predicted values are closer to experimental results at low temperatures. Compared with other models, the thermal conductivity value predicted by the Shukla model is closer to the experimental value at middle and high temperatures. In this work, the thermal conductivity of ZrO2/toluene nanofluids has been greatly improved, which has shown its promising applicability in thermal management in practical engineering applications.

Analysis of enhancement mechanism for thermal conductivity of nanofluids by inelastic X-ray scattering

The inelastic X-ray scattering for the nanofluid composed of silicon dioxide (SiO2) nanoparticles (various sizes exceeding 100 nm) and ethylene glycol aqueous solution has revealed one of the mechanisms for the thermal conductivity enhancement of nanofluids, which the addition of a small amount of nanoparticles can improve the thermal conductivity of nanofluids beyond the theoretical value. The high-frequency sound velocity derived from the inelastic X-ray scattering spectra of nanofluids may be generally correlated with the structural relaxation of solvent molecules and corresponding thermal conductivity. It can also clarify the effect of nanoparticles on the thermal transport in nanofluids and the molecular dynamics of the solvent around nanoparticles. The size of the SiO2 particles in the nanofluid, which are 100, 300, 500, and 1000 nm, is approximately 10 times larger than the typical size of nanoparticles considered in research. Nevertheless, the experimental results indicate that SiO2 particles may increase the high-frequency sound velocity in the ethylene glycol aqueous solution because of the highly restricted movement of the solvent molecules around these nanoparticles. The high-frequency sound velocity in the SiO2 nanofluid is proportional to the thermal conductivity and that containing particles 300 nm in size is higher than those of other nanofluids containing particles 100, 500, and 1000 nm in size.

Experimental study on the effect of different surfactants on the thermophysical properties of graphene filled nanofluids

In this study, the effects of different surfactants on the stability and thermophysical properties of graphene (GR) filled water (W) nanofluids were investigated in an experimental manner. Nanofluids filled by 0.05% mass fraction of GR nanoparticles with a surfactant concentration of 0.02%, 0.04%, 0.06%, 0.08%, and 0.1% were studied. The effects of surfactants on stability, viscosity, and thermophysical properties of prepared nanofluids were analyzed experimentally. The results show that the nonionic surfactants displayed best performance in terms of static stability, and the anionic surfactants stand in the second position. Further study reveals that thermal conductivity enhancement of nanofluids could reach 2.0% by filling only 0.05% mass fraction of GR and the heat transfer coefficient of nanofluids up to 27%.

A molecular dynamics study of the effect of nanoparticles coating on thermal conductivity of nanofluids

Nanofluids have become the subject of theoretical and experimental researches over the last few decades due to their enhanced heat transfer performance. In this study, thermal conductivity of copper argon nanofluids containing coated and non-coated copper particles is determined through MD simulation. Coating over the nanoparticles is applied to provide long-term stable dispersion and also to enhance the heat transfer performance. Thermal conductivity of nanofluids depends on the types of coating applied over the nanoparticles. The role of surface coating over the nanoparticles in enhancing thermal properties of the nanofluids is discussed and compared to that of non-coated particles. The silver coating is applied over the copper nanoparticles. Green Kubo method is employed to determine the thermal conductivity of the nanofluids. Result shows that, for volume fraction 3% and 86 K system temperature, thermal conductivity enhancement of nanofluid containing coated particle is 17% compared to that of base fluid and without coating, thermal conductivity enhancement is 12%. The mechanism of increased heat transfer performance for surface coating over the nanoparticles is discussed in this paper.

A Review on Heat Transfer of Nanofluids by Applied Electric Field or Magnetic Field.

Nanofluids are considered to be a next-generation heat transfer medium due to their excellent thermal performance. To investigate the effect of electric fields and magnetic fields on heat transfer of nanofluids, this paper analyzes the mechanism of thermal conductivity enhancement of nanofluids, the chaotic convection and the heat transfer enhancement of nanofluids in the presence of an applied electric field or magnetic field through the method of literature review. The studies we searched showed that applied electric field and magnetic field can significantly affect the heat transfer performance of nanofluids, although there are still many different opinions about the effect and mechanism of heat transfer. In a word, this review is supposed to be useful for the researchers who want to understand the research state of heat transfer of nanofluids.

Contributory effect of diffusive heat conduction and Brownian motion on thermal conductivity enhancement of nanofluids

The effect of diffusive heat conduction and Brownian motion on the enhancement of thermal conductivity in nanofluids is presented here. $${\hbox {Al}}_{2} {\hbox {O}}_{3}$$ and $${\hbox {TiO}}_{2}$$ nanofluids were prepared at four different wt. fractions of 1%, 0.5%, 0.1% and 0.05% and their thermal conductivity values were measured over temperatures ranging from 25 to $$55^{\circ }\hbox {C}$$ for every $$10 ^{\circ }\hbox {C}$$ interval. The thermal conductivity of nanofluids increased with the increase in concentration and temperature. Diffusive thermal conduction and Brownian motion contribute to thermal conductivity enhancement. However, diffusive heat conduction has major contribution to thermal conductivity enhancement in nanofluids. The thermal boundary resistance was found to be increasing with wt. fraction and decreasing with temperature elevation. Finally, a correlation is presented using group method of data handling (GMDH) neural network to predict the thermal conductivity of nanofluids.

A Novel Experimental Study on the Rheological Properties and Thermal Conductivity of Halloysite Nanofluids.

Nanofluids obtained from halloysite and de-ionized water (DI) were prepared by using surfactants and changing pH for heat-transfer applications. The halloysite nanotubes (HNTs) nanofluids were studied for several volume fractions (0.5, 1.0, and 1.5 vol%) and temperatures (20, 30, 40, 50, and 60 °C). The properties of HNTs were studied with a scanning electron microscope (SEM), energy-dispersive X-ray analysis (EDX), Fourier-transform infrared (FT-IR) spectroscopy, X-ray powder diffraction (XRD), Raman spectroscopy and thermogravimetry/differential thermal analysis (TG/DTA). The stability of the nanofluids was proven by zeta potentials measurements and visual observation. With surfactants, the HNT nanofluids had the highest thermal conductivity increment of 18.30% for 1.5 vol% concentration in comparison with the base fluid. The thermal conductivity enhancement of nanofluids containing surfactant was slightly higher than nanofluids with pH = 12. The prepared nanofluids were Newtonian. The viscosity enhancements of the nanofluid were 11% and 12.8% at 30 °C for 0.5% volume concentration with surfactants and at pH = 12, respectively. Empirical correlations of viscosity and thermal conductivity for these nanofluids were proposed for practical applications.

Entropy generation of turbulent Cu–water nanofluid flows inside thermal systems equipped with transverse-cut twisted turbulators

In the present study, numerical simulations have been carried out on thermal characteristics and second-law analysis of turbulent Cu–H2O nanofluid flow with the nanoparticle volume fraction of $$0 < \phi < 1.5\%$$ inside heat exchangers fitted by transverse-cut twisted tapes (TCTTs) with alternate axis. The transverse-cut ratios are in the range of 0.7 < b/c < 0.9 and 2 < s/c < 2.5, and the Reynolds number is varied between 5000 and 15,000. The impacts of the design variables on the turbulent kinetic energy, temperature distribution, thermal and frictional entropy generations and Bejan number have been evaluated. The simulations show that the TCTTs with b/c = 0.7 generate higher turbulent kinetic energy compared to the b/c = 0.9 due to higher swirl generation and flow disturbance. The additional recirculating flow produced near the alternate edges is another main physical factor for heat transfer augmentation. It is found that raising the nanoparticles volume concentration reduces the thermal entropy generation which is attributed to the thermal conductivity enhancement of nanofluids. Besides, raising the nanoparticles volume concentration from 0 to 1.5% reduces the $${{N}_{\text{g,thermal}}}$$ by 23%.

Thermal conductivity enhancement of nanofluid by adding multiwalled carbon nanotubes: Characterization and numerical modeling patterns

Nanofluid is divided in two major section, mono nanofluid (MN) and hybrid nanofluid (HN). MN is created when a solid nanoparticle disperses in a fluid, whereas HN has more than one solid nanomaterial. In this research, iron (III) oxide (Fe3O4) is MN, and Fe3O4 plus multiwalled carbon nanotube (MWCNT) is HN, whereas both are mixed and dispersed into the water basefluid. Thermal conductivity (TC) of Fe3O4/water and MWCNT/Fe3O4/water was measured after preparation and numerical model performed on the resulted data. After that, field emission scanning electron microscope (FESEM) was studied for microstructural observation of nanoparticles. MN and HN TC were studied at temperature ranges of 25 to 50°C and volume fractions of 0.2% to 1.0%. For MN and HN, thermal conductivity enhancement (TCE) of 32.76% and 33.23% was measured at 50°C temperature—1.0% volume fraction, individually. Different correlations have been calculated for numerical modeling, with R2 = 0.9. Deviation of 0.6007% and 0.6096% was calculated for given correlations for MN and HN individually. Deviation of 0.5862% and 0.6057% was calculated for trained models, for MN and HN individually. Thus, by adding MWCNT to Fe3O4‐H2O nanofluid, TC is enhanced 0.47%, and this HN has agreeable heat transfer potential.

A critical review of the most popular mathematical models for nanofluid thermal conductivity

The following article is a critical review of the most popular hypotheses that have been used in the past to model analytically or numerically the thermal conductivity enhancement of nanofluids (Λ). This analysis starts first with the Effective Medium Theories (EMT), which have been used in the past to calculate the macroscopic properties of solid composites. Secondly, the hypotheses of a Λ due to Brownian movement are evaluated. All estimations and characteristic dimensions point to a negligible Λ due to Brownian motion. In third place, the hypotheses based on the agglomeration of nanoparticles provide a convincing explanation for the experimental measurements of Λ. Furthermore, the complicated dynamics of agglomeration could be the source of disparity between the published measurements of Λ. Lastly, the possible effect of liquid layering is briefly discussed. All in all, agglomeration as the principal mechanism for Λ might prove to be sufficient to explain the thermal behavior of nanofluids.

Thermophysical properties of water ethylene glycol (WEG) mixture-based Fe3O4 nanofluids at low concentration and temperature

In the present work, Fe3O4 nanoparticles produced by the ultrasonic precipitation method and characterized by XRD, SEM, and BET methods are used to produce nanofluids using a mixture of water and ethylene glycol (WEG 50:50) as a base fluid and both sodium dodecyl sulfonate and oleic acid as surfactants. The thermal conductivity, dynamic viscosity and surface tension of these Fe3O4 nanofluids are experimentally evaluated for temperatures ranging from 253.15 to 293.15 K and different volume concentrations of nanoparticles, 0.01, 0.05 and 0.1% respectively. Experiments indicate that the thermophysical properties of nanofluids are strongly dependant on concentrations of nanoparticles and temperatures, particularly at sub-zero temperatures. Actually, it is shown that the thermal conductivity of nanofluids increases with almost 9.5%, and 14.3%, at 263.15 K and 293.15 K respectively, with 0.1 vol%. The thermal conductivity enhancement of nanofluids with concentration and temperature is compared to some relevant theoretical models. A good agreement is achieved with a comprehensive model taking into consideration effective medium theory, the nanolayer effect of molecules around the solid particle, Brownian motion of nanoparticles encompassing aggregation and nano-convection. It is also found that the dynamic viscosity of nanofluids decreases with nanoparticle content in particular below 273.15 K, up to 40% at 0.1% in volume. Surface tension decreases by adding the surfactant to the base fluid and then increases with Fe3O4 concentration with nearly 38% and 33% with 0.1% in nanoparticle volume fraction at 253.15 and 293.15 K, respectively. Finally, these results are promising in view of Fe3O4 nanofluids use in cooling applications.

Glycerol based binary solvent: Thermal properties study and its application in nanofluids

Abstract Nanofluids are of great importance due to the superior performance on improvement of thermal performance. In this work, glycerol (GL) based binary solvent system, including GL/water (W), GL/ethylene glycol (EG), and GL/dimethyl sulfoxide (DMSO) were studied comprehensively regarding their melting points, boiling points, thermal conductivities and viscosities. Graphene oxide (GO), which was prepared through a chemical exfoliation method, was subsequently dispersed into those solvents to make nanofluids. Thermal conductivity and viscosities were studied to evaluate the performance of those nanofluids. It is revealed that a maximum 20% thermal conductivity enhancement of nanofluid is achieved by just filling 0.6 vol% of GO.

Thermo-economic performance analysis of Al2O3-water nanofluids — An experimental investigation

This paper reports an experimental investigation on the thermophysical characterization, cooling capability and economic performance of Al2O3-water nanofluid. Al2O3 (20 nm–30 nm) nanoparticles are dispersed in water to prepare nanofluids by two-step method with no application of surfactant. Thermal conductivity and viscosity of Al2O3-water nanofluids were measured at different solid weight fractions of 0.0001, 0.0005, 0.001, 0.005 and 0.01 over the temperature range of 25 °C–60 °C for every 5 °C. The effect of concentration and temperature on thermal conductivity and viscosity of nanofluids were examined thoroughly. Thermal conductivity enhancement in nanofluids was prominent with variation in concentration and temperature. However, viscosity enhancement in nanofluids was only influenced by change in concentration. New correlations of thermal conductivity and viscosity based upon the experimental results have been proposed. Cooling capacity of nanofluids based on properties enhancement ratio (PER) were investigated and finally, economic analysis of the cooling performance of nanofluids has been presented.

Thermal conductivity enhancement of nanofluids composed of rod-shaped gold nanoparticles: Insights from molecular dynamics

Achieving a higher heat transfer in nanofluids is one of the most important scientific challenges in order to reach a better efficiency in several sectors of technology. Here, the thermal conductivity enhancement of nanofluids composed of rod-shaped gold nanoparticles (AuNPs) compared to that of nanofluids with spherical-shaped AuNPs has been investigated by means of Equilibrium Molecular Dynamics (EMD) simulations combined with the hybrid pair potential option in a LAMMPS software which incorporate the Embedded-Atom-Method (EAM) potential and the 12–6 Lennard-Jones (LJ) one. The EAM potential was used to describe solid-solid interatomic interactions, while the interactions solid-liquid and liquid-liquid were modeled by employing the LJ potential. The simulation results illustrated that the suspension of the rod AuNP in base fluids is more effective in improving the effective thermal conductivity of nanofluids than that of the spherical AuNP. Mechanisms behind this finding are discussed using the Mean-Square-Displacement (MSD) and Radial-Distribution-Function (RDF). Several parameters like NP surface effect, solid-liquid interface and the enhanced mobility of liquid atoms in nanofluids were considered to explain the results.

A comparative study in the prediction of thermal conductivity enhancement of nanofluids using ANN-MLP, ANN-RBF, ANFIS, and GMDH methods

In this work, four types of data mining methods, namely adaptive neuro-fuzzy inference system, artificial neural network—multilayer perceptron algorithm (ANN-MLP), artificial neural network—radial basis function algorithm (ANN-RBF), and group method of data handling (GMDH) have been used to predict the enhancement of the relative thermal conductivity of a wide range of nanofluids with different base fluids and nanoparticles. The total number of experimental data used in this work is 483 from 18 different nanofluids. The input parameters are thermal conductivity of base fluid and nanoparticles, volume fraction percent, the average size of nanoparticles, and temperature. Although the results showed that all four models are in relatively good agreement with experimental data, the ANFIS method is the best. The average absolute relative deviations (AARD%) between the experimental data and those of obtained using ANFIS, ANN-MLP, ANN-RBF, and GMDH methods were calculated as 2.7, 2.8, 4.2, and 4.3, respectively, for the test sets and as 1.1, 2.4, 3.9, and 4.5, respectively, for the training sets. Comparison between the predictions of the proposed ANN-MLP, ANN-RBF, ANFIS, and GMDH models and those predicted by traditional models, namely Maxwell and Bruggeman models showed that much better agreements can be obtained using the four models especially ANFIS model. Accordingly, the ANFIS method can able us to predict the relative thermal conductivity of new nanofluids in different conditions with good accuracy.

Automated thermal conductivity measurement algorithm for the transient hot wire method

Even after more than 20 years of research, establishment of a nanofluid thermal conductivity enhancement mechanism is impeded by differences in research results among researchers. Thermal conductivity measurement results may differ considerably depending on the selection of the temperature history range used to estimate thermal conductivity. This range should be selected carefully considering factors such as the hot wire specifications and the applied heat, but comparisons between researchers’ choices have rarely been reported. To resolve this problem, herein we present an algorithm that estimates test fluid thermal conductivity based upon the inputs of various hot wire specifications, wire resistance history, and applied voltage. We confirm that the proposed algorithm gives more accurate and precise results comparing with the cases of selecting the range based on solely on the determination coefficient R2 and is effective in eliminating data affected by the errors. The proposed method for fluid thermal conductivity measurement is robust to differences in measurement conditions including operator skill level, applied voltage, and hot wire specifications. It is expected that the discrepancies noted across the results of different research groups would be greatly reduced by adopting the proposed method.