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.

     

Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al<Subscript>2</Subscript>O<Subscript>3</Subscript> 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.     

Effect of Reynolds number on heat transfer and flow for multi-oxide nanofluids using numerical simulation

A numerical simulation model for laminar flow of nanofluids in a pipe with constant heat flux at the wall has been built to study the effect of Reynolds number on heat transfer and pressure loss. The investigation was performed for metallic oxide and multi-oxide nanoparticles suspended in water. The thermal conductivity and dynamic viscosity were measured for a range of temperature (10–60 °C) and volume fraction of multi-oxide nanofluid. Comparison of the thermal conductivity for monocular oxide and multi-oxide nanofluids reveals a new way to control the enhancement in nanofluid conductivity. The numerical results obtained were compared with existing well-established correlations. The predictions of the Nusselt number for nanofluids are in agreement with the Shah correlation, and the deviation in the results is less than 1 %. It is found that the pressure loss increases with the Reynolds number, nanoparticle density, and volume fraction for multi-oxide nanoparticles. However, the flow demonstrates enhancement in heat transfer which improves with increasing Reynolds number of the flow.     

A model of nanofluids effective thermal conductivity based on dimensionless groups

Thermal conductivity is an important parameter in the field of nanofluid heat transfer. This article presents a novel model for the prediction of the effective thermal conductivity of nanofluids based on dimensionless groups. The model expresses the thermal conductivity of a nanofluid as a function of the thermal conductivity of the solid and liquid, their volume fractions, particle size and interfacial shell properties. According to this model, thermal conductivity changes nonlinearly with nanoparticle loading. The results are in good agreement with the experimental data of alumina-water and alumina-ethylene glycol based nanofluids.     

The noble effect of aging on the thermal conductivity of modified CNTs-ethylene glycol nanofluids

In order to improve the heat transfer process by using nanofluids, different nanoparticles and base fluids have been studied. In this work, stability and effect of aging and temperature on the thermal conductivity of CNTs-ethylene glycol (EG) nanofluids were investigated. Chemical functionalisation was used to oxidise the surface of CNTs. The functionalised CNTs were used to prepare the nanofluids by a two-step method. The stability of nanofluids was measured by UV-vis spectroscopy and the results showed that the nanofluids had a good stability over several days. Immediately after nanofluid preparation not too much increase was observed for thermal conductivity but the nanofluid aging had a great influence on the improvement of the thermal conductivity, as after 65 days, about 50% increase was observed. The increase has been attributed to forming an ordered nanolayer of EG molecules around the CNTs. Also no significant temperature dependence of thermal conductivity was observed up to 50°C possibly due to the lack of temperature dependence of CNTs Brownian motions.     

Effect of copper nanoparticle aggregation on the thermal conductivity of nanofluids

The thermal conductivity of water and glycerol is investigated via the transient hot wire method by adding small amounts of copper nanoparticles to solutions. At a 0.2% copper nanoparticle concentration, the thermal conductivity coefficient rises to 25% for the Cu + glycerol system, and to 35% for Cu + water system. A mechanism and mathematical model for describing the nanoparticle aggregation effect on the thermal properties of nanofluids are proposed, based on an analysis of the accumulated experimental data. It is shown that the enhancement of nanofluid thermal conductivity at low nanoparticle concentrations is directly proportional to their volume fraction and thermal conductivity coefficient, and (in accordance with the literature data) is inversely proportional to the radius and the aggregation ratio. The proposed model describes the existing experimental data quite well. The results from this work can be applied to the rapid cooling of electronic components, in the power engineering for ensuring the rapid and effective transfer of thermal energy in a nuclear reactor, and in the oil industry for thermal stimulation.     

Viscosity and thermal conductivity of MgO–EG nanofluids

Nanofluids are a group of novel engineering materials that are increasingly being used, particularly in the processes of heat exchange. One of the most promising materials in this group is magnesium oxide–ethylene glycol (MgO–EG) nanofluid. The literature informs that this material is characterized by an significant increase in thermal conductivity with low dynamic viscosity increase. The aim of this paper is to provide experimental data on the dynamic viscosity and thermal conductivity of nanofluids containing MgO nanoparticles with 20 nm average size and ethylene glycol as base fluid. To determine dynamic viscosity and thermal conductivity of samples, a HAAKE MARS 2 rheometer (Thermo Electron Corporation, Karlsruhe, Germany) and KD2 Pro Thermal Properties Analyzer (Decagon Devices Inc., Pullman, Washington, USA) were used. Additionally, a comparison of the experimental results and the predictions of theoretical models was presented. It was presented that the vast majority of theoretical models does not describe in a correct way both viscosity and thermal conductivity. It was also shown that the enhancement of this basic physical properties might be described with good result with second degree polynomials. Finally, evaluation of the heat transfer performance was presented.     

Microwave synthesis of silver nanofluids with polyvinylpyrrolidone (PVP) and their transport properties

Microwave synthesis has been applied to prepare stable silver nanofluids in ethanol by reduction of AgNO₃ with polyvinylpyrrolidone (PVP), used as stabilizing agent, having Ag concentrations of 1% by volume. The nanofluids were characterized by UV-vis spectroscopy, Fourier transform infrared, energy-dispersive X-ray spectroscopy, and transmission electron microscopy and systematically investigated for refractive index, electrical and thermal conductivity, and viscosity for different polymer concentrations. The size of nanoparticles was found to be in the range of 30–60 nm for two different salt-to-PVP ratios. For higher concentration of polymer in nanofluid, nanoparticles were 30 nm in size showing increase in thermal conductivity but a decrease in viscosity and refractive index, which is due to the polymer structure around nanoparticles. Thermal conductivity measurements of nanofluids show substantial increment in the thermal conductivity of nanofluid relative to the base fluid and nonlinear enhancement over the 283–323 K temperature range. Rheology of nanofluids was studied at room temperature showing effect of polymer on viscosity and confirming the Newtonian behavior of nanofluid.     

Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. In this paper, first the results of experiments on the thermal conductivity of MgO/ethylene glycol (EG) nanofluids in a temperature range of 25–55 °C and volume concentrations up to 5 % are presented. Different sizes of MgO nanoparticles are selected to disperse in EG, including 20, 40, 50, and 60 nm. Based on the results, an empirical correlation is presented as a function of temperature, volume fraction, and nanoparticle size. Next, the model of thermal conductivity enhancement in terms of volume fraction, particle size, and temperature was developed via neural network based on the measured data. It is observed that neural network can be used as a powerful tool to predict the thermal conductivity of nanofluids.     

Synthesis and thermo-physical properties of water-based novel Ag/ZnO hybrid nanofluids

The comparative study on the thermo-physical properties of water-based ZnO nanofluids and Ag/ZnO hybrid nanofluids is reported in the present study. The outer surface of ZnO nanoparticles was modified with a thin coating of Ag nanoparticles by a wet chemical method for improved stability and heat transfer properties. The ZnO and Ag/ZnO nanofluids were prepared with varying volume concentration (??=?0.02–0.1%). The synthesized nanoparticles and nanofluids were characterized with different characterization methods viz., scanning electron microscopy, X-ray diffraction, dynamic light scattering, thermal conductivity measurement, and viscosity measurement. Results show that thermal conductivity of Ag/ZnO hybrid nanofluids is found to be significantly higher compared to ZnO nanofluids. The maximum thermal conductivity an enhancement for Ag/ZnO nanofluid (??=?0.1%) is found to 20% and 28% when it compared with ZnO nanofluid (??=?0.1%) and water, respectively.     

Thermal performance evaluation of a nanofluid‐based flat‐plate solar collector

The thermal performance of a flat-plate solar collector (FPSC) is investigated experimentally and analytically. The studied nanofluid is SiO₂/deionized water with volumetric concentration up to 0.6% and nanoparticles diameter of 20–30 nm. The tests and also the modeling are performed based on ASHRAE standard and compared with each other to validate the developed model. The dynamic model is based on the energy balance in a control volume. The system of derived equations is solved by employing an implicit finite difference scheme. Moreover, the thermal conductivity and viscosity of SiO₂ nanofluid have been investigated thoroughly. The measurement findings indicate that silica nanoparticles, despite their low thermal conductivity, have a great potential for improving the thermal performance of FPSC. Analyzing the characteristic parameters of solar collector efficiency reveals that the effect of nanoparticles on the performance improvement is more pronounced at higher values of reduced temperature. The thermal efficiency, working fluid outlet temperature and also absorber plate temperature of the modeling have been confirmed with experimental verification. A satisfactory agreement has been achieved between the results. The maximum percentage of deviation for working fluid outlet temperature and collector absorber plate temperature is 0.7% and 3.7%, respectively.

     

Prediction of the mass gain during high temperature oxidation of aluminized nanostructured nickel using adaptive neuro-fuzzy inference system

In this paper, the applicability of ANFIS as an accurate model for the prediction of the mass gain during high temperature oxidation using experimental data obtained for aluminized nanostructured (NS) nickel is presented. For developing the model, exposure time and temperature are taken as input and the mass gain as output. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the network. We have compared the proposed ANFIS model with experimental data. The predicted data are found to be in good agreement with the experimental data with mean relative error less than 1.1%. Therefore, we can use ANFIS model to predict the performances of thermal systems in engineering applications, such as modeling the mass gain for NS materials.     

油基-金纳米流体的制备及热稳定性

分别采用N-十六烷基-N-(羟乙基)-N,N-二甲基溴化铵(CHDAB)和丁烷-1,4-二(N-十六烷基-N,N-二甲基溴化铵)(G16-4-16)2种阳离子表面活性剂作为金属表面修饰剂, 在石油醚/正丁醇/水混合体系中用KBH₄ 还原HAuCl₄制备出亲油性纳米金. 其中, 双子表面活性剂G16-4-16显示出更好的包裹分散作用, 其包裹的纳米金粒径分布范围较窄, 平均粒径为5.2 nm. 将该纳米金颗粒分散在液态烷烃、 甲苯和长链烷基醇等溶剂中可制成稳定的油基纳米流体. 采用紫外-可见光谱法跟踪热稳定性随时间的变化, 结果表明, 该纳米流体显示了较好的热稳定性, 在130 ℃稳定时间达20 h. 采用点热源法测定了该纳米流体的导热系数, 结果表明, 50 ℃时添加质量分数1.5%的纳米金可以使其导热系数增大约17%.     

The thermal conductivity of alumina nanoparticles dispersed in ethylene glycol

The thermal conductivities of several nanofluids (dispersions of alumina nanoparticles in ethylene glycol) were measured at temperatures ranging from 298 to 411 K using a liquid metal transient hot wire apparatus. Our measurements span the widest range of temperatures that have been investigated to date for any nanofluid. A maximum in the thermal conductivity versus temperature behavior was observed at all mass fractions of nanoparticles, closely following the behavior of the base fluid (ethylene glycol). Our results confirm that additional temperature contributions inherent in Brownian motion models are not necessary to describe the temperature dependence of the thermal conductivity of nanofluids. Our results also show that the effect of mass or volume fraction of nanoparticles on the thermal conductivity of nanofluids can be correlated using the Hamilton and Crosser or Yu and Choi models with one adjustable parameter (the shape factor in the Hamilton and Crosser model, or the ordered liquid layer thickness in the Yu and Choi model).     

Combination of RSM and NSGA-II algorithm for optimization and prediction of thermal conductivity and viscosity of bioglycol/water mixture containing SiO2 nanoparticles

The fluids containing nanoparticles have enhanced thermo-physical characteristics in comparison with conventional fluids without nanoparticles. Thermal conductivity and viscosity are thermo-physical properties that strongly determine heat transfer and momentum. In this study, the response surface method was firstly used to derive an equation for the thermal conductivity and another one for the viscosity of bioglycol/water mixture (20:80) containing silicon dioxide nanoparticles as a function of temperature as well as the volume fraction of silicon dioxide. Then, NSGA-II algorithm was used for the optimization and maximizing thermal conductivity and minimizing the nanofluid viscosity. Different fronts were implemented and 20th iteration number was selected as Pareto front. The highest thermal conductivity (0.576 W/m.K) and the lowest viscosity (0.61 mPa.s) were obtained at temperature on volume concentration of (80 °C and 2%) and (80 °C without nanoparticle) respectively. It was concluded that the optimum thermal conductivity and viscosity of nanofluid could be obtained at maximum temperature (80 °C) or a temperature close to this temperature. An increase in the volume fraction of silicon dioxide led to the enhancement of thermal conductivity but the solution viscosity was also increased. Therefore, the optimum point should be selected based on the system requirement.     

Synthesis of metal-based nanofluids and their thermo-hydraulic performance in compact heat exchanger with multi-louvered fins working under laminar conditions

Present experimental investigation incorporates characterization of Al nanopowder, synthesis of Al/water nanofluids, and effect of these nanofluids on thermal performance of compact heat exchanger. Al nanoparticles are characterized using TEM and XRD. Al/water nanofluid is prepared by dispersing metal basis aluminium nanoparticles of average 100 nm size into double distilled water at two different particle volume concentrations of 0.1 and 0.2%. The nanofluids are prepared by two-step method and cetyl trimethyl ammonium bromide surfactant is used to stabilize the nanofluid. Thermo-physical properties of nanofluids at two different concentrations and their variation with fluid temperature are measured experimentally. It is examined that thermal conductivity, viscosity, and density of the nanofluid increased with the increase of volume concentrations. Furthermore, by increasing the fluid temperature, thermal conductivity is intensified, while the viscosity and density are decreased. Heat transfer parameters are strong functions of these thermo-physical properties. Therefore, comprehensive findings on heat transfer coefficient, Nusselt number, colburn factor, friction factor, and effectiveness are determined experimentally for prepared nanofluids passing under laminar conditions through single-pass cross-flow compact heat exchanger attached with multi-louvered fins.

     

Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions

The purpose of this study is to predict the thermal conductivity of copper oxide (CuO) nanofluid by using feed forward backpropagation artificial neural network (FFBP-ANN). Thermal conductivity of CuO nanofluid is measured experimentally using transient hot-wire technique in temperature range of 20–60 °C and in volume fractions of 0.00125, 0.0025, 0.005 and 0.01% for neural network training and modeling. In addition, in order to evaluate accuracy of modeling in predicting the coefficient of nanofluid thermal conductivity, indices of root-mean-square error, coefficient of determination (R ²) and mean absolute percentage error have been used. FFBP-ANN with two input parameters (volume fraction and nanofluid temperature) and one output parameter (nanofluid thermal conductivity) in addition to two hidden layers and one outer layer which purelin, logsig and tansig functions are used was considered as the most optimum structure for modeling with neuron number of 4–10–1. In this study, among common methods of theoretical modeling of nanofluid thermal conductivity, theoretical method of Maxwell and also multivariate linear regression model was used for explaining the importance of modeling and predicting the results using neural network. According to this research, the results of indices and predictions show high accuracy and certainty of ANN modeling in comparison with empirical results and theoretical models.     

Experimental investigation of nanofluid stability on thermal performance and flow regimes in pulsating heat pipe

Pulsating heat pipe (PHP) is a type of wickless heat pipe that has a simple structure and an outstanding thermal performance. Nanofluid is a type of fluid in which nanoparticles are dispersed in a base fluid and have generally a better thermal conductivity in comparison with its base fluid. In this article, the performance of a nanofluid PHP is investigated. Graphene/water nanofluid with a concentration of 1 mg mL^?1 and TiO₂ (titania)/water nanofluid with a concentration of 10 mg mL^?1 are used as the working fluids. To simultaneously investigate the thermal performance and flow regimes in the PHP, a one-turn copper PHP with a Pyrex glass attached to its adiabatic section is used. A one-turn Pyrex PHP is also used to fully visualize flow patterns in the PHP. Our results show that the material for the fabrication of a PHP and temperature of the working fluid are the most important parameters that affect the stability of a nanofluid in the PHP. The more stable nanofluid keeps its stability in the cupper PHP, while the less stable nanofluid starts to aggregate right after the injection to the cupper PHP. The more stable nanofluid has a better thermal performance than water, while the less stable nanofluid has a worse thermal performance than water. In the case of flow regimes, no significant differences are observed between the nanofluid PHP and the water PHP which is different from the previous observations. These results can help researchers to choose the best working fluid for PHPs.

     

Experimental determination of thermal conductivity and viscosity of different nanofluids and its effect on a hybrid solar collector

In this research, three different volume concentrations (??=?0.05, 0.1 and 0.2%) of Al₂O₃/water, CuO/water and Al₂O₃–CuO/water (50:50) nanofluids are prepared by adopting a two-step nanofluid preparation method. Al₂O₃ and CuO nanoparticles with the average diameter of 50 nm and 27 nm were dispersed in distilled water. The thermal conductivity and viscosity of prepared nanofluids are measured for different temperatures by using KD2 Pro thermal property analyzed and Brookfield viscometer, respectively. The effects of nanofluids on the thermal, electrical and overall efficiency of photovoltaic thermal (PVT) solar collector are also studied. The experimental results revealed that the thermal conductivity and viscosity increase with the increase in percentage volume concentration and viscosity decreases with the increase in temperature. Furthermore, the obtained maximum thermal and electrical efficiencies of a PVT solar collector for 0.2% volume concentration of hybrid nanofluids are 82% and 15%, respectively, at peak solar radiation. The highest overall efficiency of a PVT collector with .2% volume concentration of hybrid nanofluid was 97% at peak solar radiation. Results recommend that nanofluids can be used as a heat transfer in PVT solar collector.

     

Determination of the Heat Transfer Coefficient of Metal Oxide Based Water Nanofluids in a Laminar Flow Regime Using an Adaptive Neuro-Fuzzy Inference System

In order to enhance the thermal properties of turbine oil (TO), three different nanoparticles (CuO, Al₂O₃, and TiO₂) are loaded into the TO. To measure the thermal performance of nanoparticle-based TO nanofluids at laminar flow and under constant heat flux boundary conditions, an experimental setup was applied. The obtained data clearly demonstrate the positive effect of all nanoparticles on the heat transfer rate of TO. As the most important factor, the heat transfer coefficient of the abovementioned two-phase systems is increased upon increasing both the volume concentration and the flow rate. An adaptive neuro-fuzzy inference system (ANFIS) is applied for modeling the effect of critical parameters on the heat transfer coefficient of nanoparticle-TO based nanofluids numerically. The results are compared with experimental ones for training and test data. The results suggest that the developed model is valid enough and promising for predicting the extant of the heat transfer coefficient. R² and MSE values for all data were 0.990208751 and 108.1150734, respectively. Based on the results, it is obvious that our proposed modeling by ANFIS is efficient and valid, which can be expanded for more general states.