Thermal Behavior Of Nanofluids Research Articles

Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making

The rheological and thermal behavior of nanofluids in real-world scenarios is significantly affected by their thermophysical properties (TPPs). Therefore, optimizing TPPs can remarkably improve the performance of nanofluids. In this regard, in the present study, a hybrid strategy is proposed that combines machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) to select optimal parameters for water-based multi-walled carbon nanotubes (MWCNTs)-oxide hybrid nanofluids. In the first step, four critical TPPs, including density ratio (DR), viscosity ratio (VR), specific heat capacity ratio (SHCR), and thermal conductivity ratio (TCR), are modeled using two efficient ML techniques, the group method of data handling neural network (GMDH-NN) and combinatorial (COMBI) algorithm. In the next step, the superior models are subjected to a four-objective optimization by the well-known non-dominated sorting genetic algorithm II (NSGA-II), which aims to minimize DR/VR and maximize SHCR/TCR. This study considers volume fraction (VF), oxide nanoparticle (NP) type, and system temperature as optimization variables. In the final step, two prominent MCDM techniques, TOPSIS and VIKOR, were used to identify the desirable optimal points from the Pareto fronts generated by the MOO algorithm. ML results reveal the COMBI algorithm's superior reliability in accurately modeling various TPPs. The pattern of Pareto fronts for all oxide-NPs indicated that over one-third of the optimal points have a VF > 1.5 %. On the other hand, the distribution of optimal points across different temperature ranges varied significantly depending on the type of oxide-NPs. For Al2O3-based nanofluid, around 90 % of the optimal points were within 40–50 °C. Conversely, for nanofluids containing CeO2 NPs, only approximately 24 % of the optimal points were found within the same temperature range. Considering diverse scenarios for weighting TPPs in the MCDM process implied that combining CeO2/ZnO oxide-NPs with MWCNTs in water-based nanofluids is highly effective across various real-world applications.

Non-trivial Thermal and Rheological Behaviour of Complex Nanofluids in Bifurcated Micro-confinements

The transport properties of nanofluids widely differ contingent on several factors, starting from the base fluid properties to the nanoparticle inclusion-induced rheological behavioural changes of the nanofluids. Inclusion of nanoparticle alter the fluid and flow behaviour, often to an extent of altering the constitutive properties of the fluid including rheology. We unveil the impact non-intuitive rheological properties on the thermal behaviour of nanofluids. We demonstrate that the non-Newtonian nanofluid behaviour can offer superior heat transfer performance compared to base fluid in a bifurcated micro-confinement by suitably tuning the fluid properties. The non-Newtonian behaviour of a nanofluid with a range of concentrations of nanoparticles may portray non-trivial flow behaviour, resulting in a wide variety of thermal outcomes. Our study highlights the significant impact of fluid rheology on improving the heat transfer, for confined flows where pumping cost can be significant at low Reynolds number. We further demonstrate that an increase in the shear-thinning behaviour of fluids can lead to improved heat transfer performance up to a certain threshold. Beyond this threshold, further increases in shear-thinning behaviour may cause a decrease in heat transfer performance involving non-trivial flow asymmetry in a bifurcated channel system. Overall, this research for the first time provides insights into the unique heat transfer properties of non-Newtonian rheology involving nanofluids and highlights their potential as an efficient coolant for bifurcated micro-confinements. The findings could contribute to the advancement effective cooling systems in various industrial applications with the interplay of the effects of nanoparticle inclusion and rheological properties.

Experimental and artificial neural network based study on the heat transfer and flow performance of ZnO-EG/water nanofluid in a mini-channel with serrated fins

The adoptions of mini-channel and nanofluid are potential technologies to improve the heat transfer capability. The present study experimentally investigated the flow and thermal performance of EG/water based ZnO nanofluid inside a mini-channel with serrated fins with an equivalent diameter of 1.59 mm. EG/water mixture with a mass fraction of 40%/60% served as the base fluid. Nanofluids with a volumetric concentration of 0.75 vol% and 1.5 vol% were prepared. The thermophysical properties of viscosity, density and thermal conductivity were measured. It reveals that although the adding of nanoparticles is able to improve the thermal conductivity by 4.2% on average, the viscosity also enlarges remarkably by up to 18.9%. The measured density and thermal conductivity can be predicted accurately by existing formulations. As all correlations underestimate the viscosity, a new one is proposed for the fabricated nanofluid with a small Mean Absolute Relative Deviation (MARD) of 0.1%. The Nusselt number can be averagely improved by 10.6% and 13.2% for the 0.75 vol% and 1.5 vol% nanofluid with a corresponding sacrifice of friction factor augmentation of 31.2% and 47.3% respectively due to the significant augmentation of viscosity, which results in a relatively small heat transfer coefficient enhancement ratio of 0.92–1.09 under the same pumping power. In addition, the heat transfer enhancement shows a nonlinear increment with the concentration and a higher relative enhancement is found at a lower concentration. This indirectly indicates that the heat transfer does not always increase with the concentration. Even though some published correlations present acceptable predictions for the tested flow and thermal performance of EG/water based ZnO nanofluid, the newly developed Backpropagation Artificial Neural Networks (BP-ANNs) show even better prediction accuracies for Nusselt number and friction factor with the MARDs of 0.39% and 0.35% respectively. Our study is expected to give valuable data sources and inspiration for future investigation on hydrodynamic and thermal behavior of nanofluids.

Understanding the abnormal thermal behavior of nanofluids through infrared thermography and thermo-physical characterization

Nanofluids (NFs) are colloidal suspensions of nanoparticles (NPs) within a base fluid. Unlike conventional mixtures, NFs exhibit dramatically enhanced properties, such as an abnormal increase in heat capacity at low concentration of NPs (e.g., Cp values 30% higher than the base material value). Understanding the thermo-physical behavior of NFs is essential for their application as thermal energy storage systems. In this study, we analyze a sodium nitrate ionic system containing 1 wt%, 3 wt% and 7 wt% of SiO2 NPs with different techniques like infrared thermography, infrared spectroscopy and differential scanning calorimetry (DSC) in order to shed light on the mechanism behind the increase of Cp. The themographies reveal the presence of a colder layer on top of the NF with 1 wt% of NPs whereas this layer does not appear at higher concentrations of NPs. The IR spectrum of this foamy top layer evidences the high amount of SiO2 bonds suggesting the clustering of the NPs into this layer linked by the nitrate ions. The linking is enhanced by the presence of hydroxyls in the NPs’ surface (i.e., hydroxilated NPs) that once mixed in the NF suffer ionic exchange between OH− and NO3− species, leading to O2–Si–O–NO2 species at the interface where a thermal boundary resistance or Kapitza resistance appears (RT = 2.2 m2 K kW−1). Moreover, the presence of an exothermic reactive processes in the calorimetry of the mixture with 1 wt% of NPs evidences a reactive process (ionic exchange). These factors contribute to the heat capacity increase and thus, they explain the anomalous behavior of the heat capacity in nanofluids.

Direct effect of nanoparticles on the thermal conductivity of CuO-water nanofluid in a phase transition phenomenon using molecular dynamics simulation

The thermal behavior of CuO-water nanofluid is examined on an aluminum surface. The dimensions of simulation boxes are 70 × 70 × 30 A3, 60 × 60 × 30 A3, and 50 × 50 × 30 A3, with the corresponding number of atoms of 104,351, 123,958, and 145,980. Thermal conductivities of fluid and nanofluid are calculated using the Green–Kubo formula. Molecular dynamics simulation results confirm the direct effect of nanoparticles on the values of thermal conductivities of fluid and nanofluid. The value of 0.59 Wm−1 K−1 is obtained for the thermal conductivity of water. But adding three CuO nanoparticles into the water increases this value to 0.85 Wm−1 K−1. The same effect is observed on the thermal behavior of nanofluid upon increasing the size of CuO nanoparticles that raises the value of the thermal conductivity of nanofluid to 0.99 Wm−1 K−1. The heat flux is increased by adding the nanoparticles into the base fluid; thereby, the phase transition (evaporation) occurs in fewer time steps.

Simulation of entropy optimization and thermal behavior of nanofluid through the porous media

Hydrothermal behavior of hybrid nanomaterial migration from a perforated and wavy container is analyzed by taking into account the impacts of Lorentz force and constant heat flux. The non-Darcy model incorporates the effects due to medium porosity. Suitable relations are used to transform the developed set of PDEs to non-dimensional form. The flow is simulated by employing the standard computational technique of CVFEM and then analyzed through entropy optimization. The impacts produced due to enhancing buoyancy, Lorentz forces and porosity over the hydrothermal behavior of the fluid motion are displayed by various plots. It is observed that the augmenting buoyancy and medium porosity enhance the convective flow and drop the temperature. An enhancing Lorentz forces depreciates the convective flow strength and rises the fluid temperature. The entropy generation due to different contributions also shows dependence on the varying strength of the associated parameters. Analytical relations of average Nusselt and Bejan numbers are derived, which show their dependence on the Rayleigh, Hartmann and Darcy numbers. The accommodation of the achieved results and the previous research ascertains the correctness of applied procedure.

Inclusion of nanoparticles in PCM for heat release unit

The main motivation of the present work is to scrutinize the solidification of CuO-water within a heat release unit that has innovative fins. This compact structure along with limitation at the surface of the fins is simulated using Finite Element Method (FEM). Mixture of copper oxide and H2O is introduced as a working fluid. Moreover, the nanoparticle-enhanced phase change materials (NEPCM) shows a significant change in thermal behavior of nanofluid and pure PCM properties by considering the Brownian motion. The outcomes are depicted in the form of temperature, solid fraction and phase change front contours. The results are obtained to determine the variation of phase change by adding the nanoparticles in PCM for various time domains. Similarly, significant effects of nanoparticles volume fraction on the total energy and solid fraction are presented. Furthermore, it is found that the nanoparticles and radiation parameter with size of dp = 40 nm has the greatest heat transfer rate.

Carbon nanotube nanofluid for the efficiency improvement in a CHP system: simulation and experimental investigation

Due to the importance of combined heat and power (CHP) systems in ensuring the energy efficiency of thermal units as well as the ability of nanofluids to enhance the thermal efficiency of thermal equipment, a new nanofluid was synthesized and compared with some other thermal fluids. Multi-walled carbon nanotubes (CNTs) were employed as a multifunctional nanoplatform in a base fluid. Additionally, some physical and chemical surface modifications were conducted in order to improve not only dispersion but also stability of this nanofluid. The acid treatment and polymer wrapping (PEGylation) of CNTs were applied as chemical and physical modifications, respectively. These techniques assisted us to prepare a more stable thermal nanofluid in a CHP system. Furthermore, higher heat removal was achieved, compared to previously tested nanofluids. Because of the extensive and various methods in surface modification of the CNT together with the effect of this modification on the thermal behavior of nanofluid, it seems that the PEGylated and oxidized CNT nanofluid can play an important role in improving CHP heat removal and ensure higher heat recovery. Furthermore, the possibility of more power generation was investigated with the aid of Aspen HYSYS simulation software. Simulation results showed that there is a potential to produce 52.75 kW more power with implementation of an Organic Rankine Cycle (ORC) within the current system. The ORC’s thermodynamic efficiency was 10.29%.

An experimental study on the proper criterion to judge the thermal performance of the nanofluids

This work is dedicated to finding a suitable measure to judge thermal performance of nanofluids. The importance of this issue arises from misleading claim of excess heat transfer of nanofluids compared to the base fluid, neglecting the hydraulic effects such as increase in pressure drop. To clarify the issue, the experimental setup with capability to create constant Reynolds number and constant pumping power is constructed. Thermal behavior of nanofluids of silicon oxide/water and aluminum oxide/water and distilled water in developing region of laminar flow regime is investigated. In this regard, the convective heat transfer coefficient within the finned tube heat exchanger is evaluated. According to the results, the concentration of nanoparticle in the base fluid will have a significant impact on the amount of deflection of these two criteria, so that by increasing the nanoparticle's concentration the difference between these two measures becomes greater.

Simulation of Flow and Heat Transfer of Nanofluid in an Eccentric Annulus with Multicomponent Lattice Boltzmann Method

In the present study, Lattice Boltzmann method is employed to investigate a two dimensional mixed convection heat transfer of Al2O3-water nanofluid in a horizontal annulus between a cold outer cylinder and the hot, rotating inner cylinder. To do so, the double lattice Boltzmann equation is utilized for the base fluid and the nanoparticles to describe the dynamic as well as the thermal behavior of nanofluid. Moreover, different forces such as Brownian, drag and gravity acting on the nanoparticles are taken into consideration. Calculations have been performed for Rayleigh number ranging from 103 to 2×104, Reynolds number from 5 to 120, vertical and horizontal eccentricity from -0.75 to 0.75 with volume fraction of nanoparticles from 0 to 0.1. The current computational results reveal that by adding nanoparticles, the mean Nusselt number for Ra 104, it decreases. Also, with Re>80, the mean Nusselt number increases with increasing Reynolds number; although for low Reynolds number this rising trend is not observed. Besides, when the inner cylinder moves vertically upward from the center, the addition of nanoparticles increases Nusselt number relative to the base fluid.

Particle migration in a flow of nanoparticle suspensions

This paper is concerned with particle migration in pressure-driven laminar pipe flows of relatively dilute suspensions of nanoparticles (nanofluids), one of the most frequently used configuration in industries. The motivation behind the work is associated with the thermal behaviour of nanofluids, which can greatly exceed the values predicted by currently available macroscopic theories. A theoretical model is formulated to predict particle concentration, and velocity field of nanofluids in the transverse plane of the pipe. The model takes into account the effects of the shear-induced and viscosity gradient-induced particle migrations, as well as self-diffusion due to the Brownian motion. It is shown that particle concentration in the wall region can be much lower than that in the central core region. This indicates a highly non-uniform thermal conductivity profile across the transverse plane of the pipe, and thus has a significant implication to heat transfer intensification using nanofluids. The results also suggest the existence of an optimal particle size whereby the thermal conductivity is enhanced with little penalty due to the effect of pressure drop.