Investigation of the thermal conductivity, viscosity, and thermal performance of graphene nanoplatelet-alumina hybrid nanofluid in a differentially heated cavity

This paper investigates the thermophysical properties and heat transfer performance of graphene nanoplatelet (GNP) and alumina hybrid nanofluids at different mixing ratios. The electrical conductivity and viscosity of the nanofluids were obtained at temperatures between 15–55°C. The thermal conductivity was measured at temperatures between 20–40°C. The natural convection properties, including Nusselt number, Rayleigh number, and heat transfer coefficient, were experimentally obtained at different temperature gradients (20, 25, 30, and 35°C) in a rectangular cavity. The Mouromtseff number was used to theoretically estimate all the nanofluids’ forced convective performance at temperatures between 20–40°C. The results indicated that the thermal conductivity and viscosity of water are increased with the hybrid nanomaterial. On the other hand, the viscosity and thermal conductivity of the hybrid nanofluids are lesser than that of mono- GNP nanofluids. Notwithstanding, of all the hybrid nanofluids, GNP-alumina hybrid nanofluid with a mixing ratio of 50:50 and 75:25 were found to have the highest thermal conductivity and viscosity, enhancing thermal conductivity by 4.23% and increasing viscosity by 15.79%, compared to water. Further, the addition of the hybrid nanomaterials improved the natural convective performance of water while it deteriorates with mono-GNP. The maximum augmentation of 6.44 and 10.48% were obtained for Nuaverage and haverage of GNP-Alumina (50:50) hybrid nanofluid compared to water, respectively. This study shows that hybrid nanofluids are more effective for heat transfer than water and mono-GNP nanofluid.http://www.frontiersin.org/Energy_Researcham2022Mechanical and Aeronautical Engineerin

The effects of uncertainly of nanolayer properies on the heat transfer through nanofluids

Paper presented to the 10th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Florida, 14-16 July 2014.Nanofluids which are suspension of nanoparticles in conventional heat transfer fluids attracted researches on different heat transfer applications while they enhance thermal transport properties in comparison with conventional base fluids. Recently, utilizing these new fluids is growing increasingly, but the ambiguities of their thermal-physical properties cause to do not involve efficiently in the industrial design to date. The recognized important parameters effecting on properties of nanofluids include the volume fraction of the nanoparticles, temperature, nanoparticle size, nanolayer (thickness and properties), thermal conductivity of the base fluid, PH of the nanofluid, and the thermal conductivity of the nanoparticles. However, there is a distinct lack of enough investigation and reported research on the nanolayer thickness and its properties. In this work, the effect of uncertainty of the nanolayer thickness and properties on effective thermal conductivity and effective viscosity of nanofluids and as a result on heat transfer is discussed in details. The results show that the uncertainties can cause 20% error in calculation of Nusselts number and 24% for Reynolds number. Therefore, more investigation needs to be done into properties of nanolayer in order to identify them accurately.cf201

Electrical conductivity and pH modelling of magnesium oxide–ethylene glycol nanofluids

Nanofluids as new composite fluids have found their place as one of the attractive research areas. In recent years, research has increased on using nanofluids as alternative heat transfer fluids to improve the efficiency of thermal systems without increasing their size. Therefore, the examination and approval of different novel modelling techniques on nanofluid properties have made progress in this area. Stability of the nanofluids is still an important concern. Research studies on nanofluids have indicated that electrical conductivity and pH are two important properties that have key roles in the stability of the nanofluid. In the present work, three different sizes of magnesium oxide (MgO) nanoparticles of 20, 40 and 100 nm at different volume fractions up to 3% of the base fluid of ethylene glycol (EG) were studied for pH and electrical conductivity modelling. The temperature of the nanofluids was between 20 and 70◦C for modelling. A genetic algorithm polynomial neural network hybrid system and an adaptive neuro-fuzzy inference system approach have been utilized to predict the pH and the electrical conductivity of MgO–EG nanofluids based on an experimental data set.http://www.ias.ac.in/matersci/index.htmlhttp://link.springer.com/journal/12034am2020Mechanical and Aeronautical Engineerin

Convection heat transfer, entropy generation analysis and thermodynamic optimization of nanofluid flow in spiral coil tube

In this study, heat transfer, flow characteristics, and entropy generation of turbulent TiO2/water nanofluid flow in the spiral coil tube were analytically investigated considering the nanoparticle volume fraction, curvature ratio, flow rate and inlet temperature between 0.01–0.05 percent, 0.03–0.06, 1.3–3.3 l/min, and 15–27 °C, respectively. Results showed that the augmentation of the nanoparticle volume fraction increased the Nusselt number and friction factor up to 11.9% and 1.1%, respectively, while it reduced the entropy generation number up to 10.9%. Reducing the curvature ratio led to a maximum of 11.1% increase in the Nusselt number, while it resulted in a 5.6% increase in the entropy generation number. A decline in the inlet temperature from 21 °C to 15 °C proceeded a 28.4% and 7.1% increase in the heat transfer and pressure drop, respectively. The total entropy generation reduced with increasing nanoparticle volume fraction. For a low Reynolds number, a decrease in the curvature ratio led to a reduction in the total entropy generation, while reducing the curvature ratio was detrimental for a high Reynolds number. Analytical relations for optimum curvature ratio and optimum Reynolds number were derived. For the range of parameters studied in this paper, a range of optimum Reynolds number from 9000 to 12,000 was proposed.http://www.tandfonline.com/loi/uhte20hj2022Mechanical and Aeronautical Engineerin

A novel combined model of discrete and mixture phases for nanoparticles in convective turbulent flow

In this study, a new combined model presented to study the flow and discrete phase features of nano-size particles for turbulent convection in a horizontal tube. Due to the complexity and also many phenomena involved in particleliquid turbulent flows, the conventional models are not able to properly predict some hidden aspects of the flow. Therefore, Brownian motion is implemented in discrete phase model to predict the migration of the particles as well as energy equation has modified for particles. Then, the final results are exported to the mixture equations of the flow. The effects of the mass diffusion due to thermophoresis, Brownian motion and turbulent dispersion are implemented as source terms in equations. The results are compared with the experimental measurements from literature and are adequately validated. The accuracy of predicted heat transfer and friction coefficients are also discussed versus measurements. The migration of the particles toward the centre of the tube is properly captured. The results show the non-uniform distribution of particles in turbulent flow due to strong turbulent dispersion. The proposed combined model can open new viewpoints of particle-fluid interaction flows.http://aip.scitation.org/journal/phfam2017Mechanical and Aeronautical Engineerin

Viscosity of nanofluids based on an artificial intelligence model

By using an FCM-based Adaptive neuro-fuzzy inference system (FCM-ANFIS) and a set of experimental data, models were developed to predict the effective viscosity of nanofluids. The effective viscosity was selected as the target parameter, and the volume concentration, temperature and size of the nanoparticles were considered as the input (design) parameters. To model the viscosity, experimental data from literature were divided into two sets: a train and a test data set. The model was instructed by the train set and the results were compared with the experimental data set. The predicted viscosities were compared with experimental data for four nanofluids, which were Al2O3, CuO, TiO2 and SiO2, and with water as base fluid. The viscosities were also compared with several of themost cited correlations in literature. The results, which were obtained by the proposed FCM-ANFIS model, in general compared very well with the experimental measurement.NRF, Stellenbosch University/University of Pretoria Solar Hub, CSIR, EEDSM Hub and NAC.http://www.elsevier.com/locate/ichmthb201

Application of the FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network approaches to modelling the thermal conductivity of alumina-water nanofluids

By using an FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network as well as experimental data, two models were established in order to predict the thermal conductivity ratio of alumina (Al2O3)-water nanofluids. In these models, the target parameter was the thermal conductivity ratio, and the nanoparticle volume concentration, temperature and Al2O3 nanoparticle size were considered as the input (design) parameters. The empirical data were divided into train and test sections for developing the models. Therefore, they were instructed by 80% of the experimental data and the remaining data (20%) were considered for benchmarking. The results, which were obtained by the proposed FCM-based Neuro-Fuzzy Inference System (FCMANFIS) and Genetic Algorithm-Polynomial Neural Network (GA-PNN) models, were provided and discussed in detail.http://www.elsevier.com/locate/ichmtai201

Simulation study of convective and hydrodynamic turbulent nanofluids by turbulence models

The numerical study of nanofluids as a two-phase flow (both as solid nanoparticles and in a liquid phase) has brought about a new approach to simulation in this area. Due to the lack of hybrid models to fully predict the flow characteristics of nanofluids under different conditions, a case can be made for developing homogenous models from numerical simulations. In this study, the convective heat transfer and hydrodynamic characteristics of nanofluids are investigated by simulation with ANSYS-FLUENT. Accordingly, four common types of nanofluids in horizontal turbulent pipe flows have been chosen from experimental data available in literature for modelling purposes. These nanofluids are Al2O3, ZrO2, TiO2 and SiO2. The simulations are done using the built-in models of ANSYS-FLUENT, namely the Mixture model and Discrete Phase Modelling (DPM). Comparing various appropriate turbulence models, the Realisable and Standard k-ɛ models have provided the same results in most of the simulations. The Reynolds stress model (RSM) overestimates pressure drops compared with the other k-ɛ models, while the re-normalisation group (RNG) model overestimates heat transfer coefficient. The anisotropy of instantaneous velocity in the RSM gives higher turbulent kinetic energy, dissipation rate and slip velocity between the particles and the main flow, which makes it an essential part of simulations. All the DPM results have shown the same trend, but with different percentages from measured data, which means that the number of particles plays a key role in the simulations. Any small weaknesses in DPM have a significant influence on the results due to the higher number of nanoparticles.National Research Foundation of South Africa (NRF), the Council for Scientific and Industrial Research (CSIR), the National Hub for Energy-efficiency and Demand-side Management (EEDSM), NAC and EIRT-seed.http://www.elsevier.com/locate/ijts2017-12-31hb2016Mechanical and Aeronautical Engineerin

A new model for density of nanofluids including nanolayer

Nanofluids which are suspension of nanoparticles in conventional heat transfer fluids attracted researchers while they show higher thermal conductivity and specific heat capacity. The important parameters have influence on thermal-fluid properties of nanofluids include the volume fraction of the nanoparticles, temperature, density of fluid base and nanoparticles, nanoparticles size, nanolayer, thermal conductivity of base fluid and particles, and pH. Nanolayer which is an approved interfacial layer between particles and base fluid involved in some of modelling for effective thermal conductivity and effective viscosity of nanofluids. Therefore, this layer must effect on other properties of nanofluids such as density. In this study investigation into the density of nanofluids has done experimentally. The nanofluids were investigated for density measurements consist of SiO2-Water, MgO-Glycerol, CuO-Glycerol and SiOx-Ethylene Glycol /Water for range of 1 to 6% volume fraction as well as temperature range of 10 to 40oC. The results show that mixture model for density of nanofluids (density of nanofluid = density of base fluid multiply by volume fraction of base fluid + density of nanoparticles multiply by volume fraction of nanoparticles) which is generally cited in literature has higher value than experimental data. For higher volume fraction of nanoparticles, the gap between the experimental results and the mixture model gets more. This is due to the nanolayer which also shows nanolayer density can be between void and the base fluid density. Therefore, based on the experimental data a new model for density of nanofluids developed which includes nanolayer. It was also found that the amount of the void in the nanolayer is more sensitive to nanoparticle size and not to base fluids or nanoparticles material.National Research Foundation of South Africa (NRF) and EIRT-seed.http://www.elsevier.com/locate/ichmt2017-11-30hb2016Mechanical and Aeronautical Engineerin

Thermal management of solar photovoltaic cell by using single walled carbon nanotube (SWCNT)/water : numerical simulation and sensitivity analysis

Despite the attractiveness of Photovoltaic (PV) cells for electrification and supplying power in term of environmental criteria and fuel saving, their efficiency is relatively low and is further decreased by temperature increment, as a consequence of absorption of solar radiation. In order to prevent efficiency degradation of solar cells due to temperature increment, thermal management is suggested. Active cooling of solar cells with use of liquid flow is one of the most conventional techniques used in recent years. By use of nanofluids with improved thermophysical properties, the efficiency of this cooling approach is improvable. In this article, Single Walled Carbon Nano Tube (SWCNT)/water nanofluid is used for cooling of a PV cell by considering variations in different factors such as volume fraction of solid phase, solar radiation, ambient temperature and mass flow rate. According to the findings, use of the nanofluid can lead to improvement in performance enhancement; however, this is not significant compared with water. In cases using water and the nanofluid at 0.5% and 1% concentrations, the maximum improvement in the efficiency of the cell compared with the cell without cooing were 49.2%, 49.3 and 49.4%, respectively. In addition, sensitivity analysis was performed on the performance enhancement of the cell and it was noticed that solar radiation has the highest impact on the performance enhancement by using the applied cooling technique, followed by ambient temperature, mass flow rate of the coolants and concentration of the nanofluid, respectively. Moreover, exergy analysis is implemented on the system and it is noticed that lower ambient temperature and solar radiation are preferred in term of exergy efficiency.https://www.mdpi.com/journal/sustainabilityam2023Mechanical and Aeronautical Engineerin