Nanofluids For Heat Transfer Applications Research Articles

Investigation of Thermodynamic Properties and Stability of Metal Oxide (CuO and Al2O3)/Deionized Water Nanofluids for Enhanced Heat Transfer Applications

The potential of nanofluids in improving heat transfer efficiency is well recognized, although there is a lack of clarity regarding the methods for assessing their stability and the influence on thermophysical properties. Moreover, the prevention of nanoparticle aggregation, which is essential for stability, requires further investigation. This study aims to address these issues by investigating the thermodynamic properties and stability of Al2O3/deionized water and CuO/deionized water nanofluids, which were prepared using magnetic stirring and ultrasonication. The stability assessment, conducted through standard deviation analysis, revealed that CuO (80 nm)/deionized water nanofluids exhibited greater stability compared to Al2O3 (80 nm)/deionized water nanofluids. The research explored the impact of temperature, volume concentration, and nanoparticle type under both static and dynamic conditions. Static tests focused on measuring thermal conductivity, viscosity, and specific heat, while dynamic tests involved a heat exchanger setup to determine heat transfer rates. The findings indicated that CuO nanofluids displayed the highest thermal conductivity and the most significant reduction in specific heat and heat transfer rate. Viscosity was found to increase with nanoparticle concentration and decrease with temperature. This study provides valuable insights into the thermophysical characteristics and stability of nanofluids, emphasizing the advantages of CuO-based nanofluids for heat transfer applications.

Review on Nanofluids: Preparation, Properties, Stability, and Thermal Performance Augmentation in Heat Transfer Applications.

Nanoparticles play a crucial role in enhancing the thermal and rheological properties of nanofluids, making them a valuable option for increasing the efficiency of heat exchangers. This research explores how nanoparticle characteristics, such as concentration, size, and shape, impact the properties of nanofluids. Nanofluids' thermophysical properties and flow characteristics are essential in determining heat transfer efficiency and pressure loss. Nanoparticles with high thermal conductivity, such as metallic oxides like MgO, TiO2, and ZnO, can significantly improve the heat transfer efficiency by around 30% compared to the base fluid. The stability of nanofluids plays a crucial role in their usability. Various methods, such as adding surfactants, using ultrasonic mixing, and controlling pH, have been employed to enhance the stability of nanofluids. The desired thermophysical properties can be achieved by utilizing nanofluids to enhance the system's heat transfer efficiency. Modifying the size and shape of nanoparticles also considerably improves thermal conductivity, affecting nanofluid viscosity and density. Equations for determining heat transfer rate and pressure drop in a double-pipe heat exchanger are discussed in this review, emphasizing the significance of nanofluid thermal conductivity in influencing heat transfer efficiency and nanofluid viscosity in impacting pressure loss. This Review identifies a trend indicating that increasing nanoparticle volume concentration can enhance heat transfer efficiency to a certain extent. However, surpassing the optimal concentration can reduce Brownian motions due to higher viscosity and density. This Review offers a viable solution for enhancing the thermal performance of heat transfer equipment and serves as a fundamental resource for applying nanofluids in heat transfer applications.

Thermal and surface properties of AlZnO and surfactant-stabilized AlZnO nanofluids: Influence of sonication duration for heat transfer applications

Research into doped metal oxide nanofluids (NFs) has intensified due to recent breakthroughs in heat transfer applications. This study explores the thermophysical properties of Aluminum Zinc Oxide (AlZnO) at a 0.10 vol% concentration and surfactant-enhanced Aluminum Zinc Oxide (f@AlZnO) at concentrations of 0.05, 0.10, and 0.20 vol%. Experiments were conducted across a temperature range from 20 to 80°C. The study utilized various analytical techniques such as FT-IR, UV–visible spectroscopy, Powder XRD, SEM, and EDX to examine the effects of volume concentration and sonication duration on stability, particle size, thermophysical properties, contact angle, wettability, and surface tension. It was found that the thermal conductivity of AlZnO and f@AlZnO NFs increases with both volumetric concentration and temperature. Notably, surfactant inclusion significantly enhanced the stability of AlZnO NFs, achieving a high zeta potential after 160 minutes of sonication. The 0.10 vol% f@AlZnO NF displayed optimal stability at this sonication duration, while the 0.10 vol% AlZnO NF showed the smallest particle size distribution after just 20 minutes. Thermal conductivity enhancements of 5 %, 2.5 %, 2 %, and 1.9 % were observed for 0.10 vol% AlZnO NF and 0.05, 0.10, and 0.20 vol% f@AlZnO NFs, respectively. Additionally, at 200 minutes of sonication, the 0.05 vol% f@AlZnO NF demonstrated the lowest increase in specific heat capacity. The study also revealed that both surfactant addition and increasing volumetric concentration of AlZnO NFs reduced surface stress and contact angle values. In contrast, dynamic viscosity and density increased with higher nanoparticle concentration and decreased with rising temperature. The Mouromtseff number calculations underscore the potential of AlZnO NFs in thermal applications, meriting further investigation. This comprehensive examination provides critical insights into the effective utilization of hybrid metal oxide NFs in heat transfer applications.

Characterization, Preparation and Thermophysical Properties Investigations of Aqueous AgNO3–Graphene Hybrid Nanofluids for Heat Transfer Applications

Characterization, Preparation and Thermophysical Properties Investigations of Aqueous AgNO3–Graphene Hybrid Nanofluids for Heat Transfer Applications

Experimental study on preparation and heat transfer efficiency of ethylene glycol/water-based hybrid nanofluids enhanced by graphene oxide/MXene nanoparticles

In this study, the preparation and heat transfer efficiencies of ethylene glycol (EG)/water-based (50:50) hybrid nanofluids were enhanced using graphene oxide (GO)/MXene (1:1) nanoparticles. The stability of the nanofluids was analyzed, and the thermal conductivity and viscosity were tested at 20–60 °C. A correlation equation for the thermal conductivity ratio of the nanofluids was proposed. The heat transfer efficiency of the nanofluids was evaluated using the performance enhancement ratio (PER) and Mouromtseff number (Mo). The prepared EG/water-based GO/MXene nanofluids exhibited good chemical and dispersion stability. The thermal conductivity and viscosity of the nanofluids gradually increased with the GO/MXene dosage. The nanofluid containing 0.3 wt% GO/MXene nanoparticles exhibited the largest thermal conductivity increment of 33.49 % at 60 °C compared with EG/water and maintained the low viscosity. The proposed prediction equation based on the temperature and mass dosage was highly accurate. The PER and Mo demonstrated the suitability of the GO/MXene nanofluids for heat transfer applications.

A Review of Artificial Intelligence Methods in Predicting Thermophysical Properties of Nanofluids for Heat Transfer Applications

This present review explores the application of artificial intelligence (AI) methods in analysing the prediction of thermophysical properties of nanofluids. Nanofluids, colloidal solutions comprising nanoparticles dispersed in various base fluids, have received significant attention for their enhanced thermal properties and broad application in industries ranging from electronics cooling to renewable energy systems. In particular, nanofluids’ complexity and non-linear behaviour necessitate advanced predictive models in heat transfer applications. The AI techniques, which include genetic algorithms (GAs) and machine learning (ML) methods, have emerged as powerful tools to address these challenges and offer novel alternatives to traditional mathematical and physical models. Artificial Neural Networks (ANNs) and other AI algorithms are highlighted for their capacity to process large datasets and identify intricate patterns, thereby proving effective in predicting nanofluid thermophysical properties (e.g., thermal conductivity and specific heat capacity). This review paper presents a comprehensive overview of various published studies devoted to the thermal behaviour of nanofluids, where AI methods (like ANNs, support vector regression (SVR), and genetic algorithms) are employed to enhance the accuracy of predictions of their thermophysical properties. The reviewed works conclusively demonstrate the superiority of AI models over the classical approaches, emphasizing the role of AI in advancing research for nanofluids used in heat transfer applications.

A direct approach towards synthesis of copper nanofluid by one step solution phase method

We adopted a simple one step approach to synthesize copper nanofluids by reduction of copper sulphate with fructose. The solution phase synthetic technique led to the formation of copper particles whose size was restricted to the nanodimensions by use of sodium lauryl sulphate. We studied the effect of various parameters on the formation and dispersion of the copper nanoparticles in the base fluid containing a 1:1 mixture of water and ethylene glycol. The resulting Newtonian nanofluid was found to be highly stable with increased thermal conductivity. Thus, the applied technique is found to be simple, economic, and extendable to other class of materials to obtain stable dispersions of nanofluids for heat transfer applications.

Application of Machine Learning Algorithms in Predicting Rheological Behavior of BN-diamond/Thermal Oil Hybrid Nanofluids

The use of nanofluids in heat transfer applications has significantly increased in recent times due to their enhanced thermal properties. It is therefore important to investigate the flow behavior and, thus, the rheology of different nanosuspensions to improve heat transfer performance. In this study, the viscosity of a BN-diamond/thermal oil hybrid nanofluid is predicted using four machine learning (ML) algorithms, i.e., random forest (RF), gradient boosting regression (GBR), Gaussian regression (GR) and artificial neural network (ANN), as a function of temperature (25–65 °C), particle concentration (0.2–0.6 wt.%), and shear rate (1–2000 s−1). Six different error matrices were employed to evaluate the performance of these models by providing a comparative analysis. The data were randomly divided into training and testing data. The algorithms were optimized for better prediction of 700 experimental data points. While all ML algorithms produced R2 values greater than 0.99, the most accurate predictions, with minimum error, were obtained by GBR. This study indicates that ML algorithms are highly accurate and reliable for the rheological predictions of nanofluids.

Production and characterization of CuNiZnFe2O4 dispersed transformer and kerosene oil based magnetic nanofluids for heat transfer applications

This study produced nanofluids via a two-step method by dispersing copper–nickel–zinc (CuNiZnFe2O4) ferrite nanocomposites in transformer and kerosene oils. A sol–gel auto-combustion approach was adopted to synthesize ferrite nanoparticles. The prepared nanoparticles were analyzed through scanning electron microscopy, photoluminescence spectroscopy, and x-ray diffraction. The measured crystallite size varied between 11 and 13 nm. The SEM images show that the structures of the developed CuNiZn nanoparticles are irregular. The photoluminescence results give a bandgap of 1.91 eV and the emission lines of the nanoparticles. Transient hot wire analysis was performed to determine the thermal conductivity of the base fluid and the prepared nanofluids. It is observed that nanoparticles in the nanofluid enhance the heat transfer rate. It has been proven that CuNiZn/kerosene-based nanofluids have greater thermal conductivity than CuNiZn/transformer oil-based nanofluids. The viscosity of transformer oil-based nanofluids at room temperature is 12.53 mm2 s−1, which decreases to 12.49 mm2 s−1 at 40 °C. Similarly, the viscosity of kerosene-based nanofluids is 1.49 mm2 s−1 at room temperature and 1.16 mm2 s−1 at 40 °C. The sedimentation method revealed that CuNiZn/transformer oil-based nanofluids have greater stability than CuNiZn/kerosene-based nanofluids.

Thermo-optical characterization of novel MXene/Carbon-dot hybrid nanofluid for heat transfer applications

Nanofluid has emerged as a promising heat transfer fluid (HTF) due to their significant thermophysical, and optical characteristics enhancement over base fluids. Hybrid nanofluids with multiple nanomaterials have the advantage of synergistic properties in comparison to monocomponent nanofluids. The present study proposes an energy-efficient and cleaner synthesis method for developing carbon quantum dot (C-dot), MXene, and a hybrid MXene/C-dot hybrid nanofluids, for heat transfer application. In-situ microwave pyrolysis technique and two-step method were adopted for nanomaterial and nanofluid synthesis. The morphological, phase structural, chemical, and elemental compositional analysis of the nanomaterials was performed. The material characterization confirms the hybridization of C-dot on MXene nanosheets. The thermal conductivity and volumetric heat capacity of the nanofluids were measured using the transient plane source (TPS) method. Thermal conductivity was observed to increase with nanofluid concentration and temperature. Results indicate that MXene has the highest thermal conductivity enhancement (50 %) over water, followed by hybrid (42.2 %) and C-dot nanofluid (33.2 %). The volumetric heat capacity of nanofluids decreased with concentration and temperature. A semi-empirical correlation, as a function of nanofluid concentration and temperature, was coined for predicting thermal conductivity and volumetric heat capacity. Optical property characterization study shows that C-dot nanofluid exhibited considerable absorption along the UV range, while MXene nanofluid showed absorption in the visible and near-infrared (NIR) region. Hybrid nanofluids demonstrated complementary absorption properties of C-dot and MXene nanofluids.

Development of semiconducting graphitic carbon nitride nanofluid for heat transfer applications: A mode mismatched thermal lens study

The present work delineates the heat transfer potential of the semiconducting graphitic carbon nitride (g-C3N4) nanofluid. The paper also discusses the eco-friendly green synthesis of g-C3N4 nanoparticles from the natural carbon precursor portobello mushroom by the hydrothermal method. The nanoparticles synthesised are subjected to structural, morphological, thermal, and optical characterizations. The flower-like laminar structure of the sample revealed through field emission scanning electron microscope (FESEM) analysis exhibited a semiconducting nature with an optical bandgap of 2.58 eV. The formation of g-C3N4 is confirmed by X-ray diffraction (XRD), Fourier transform infrared (FTIR), X-ray photoelectron (XPS) and Raman spectroscopic analyses. The thermogravimetric analysis (TGA) reveals good thermal stability up to 500 °C, which suggests possible applications in heat transfer fluids. The concentration-dependent thermal diffusivity variation of the g-C3N4 semiconductor nanofluid, investigated using the sensitive mode mismatched dual beam thermal lens technique, divulges its potential as an organic, metal-free additive in engine coolants for automobile applications.

Thermophysical properties of TiO2/CuO hybrid nanofluids for heat transfer applications

ABSTRACT Hybrid nanofluids are emerging as a new generation fluids because of their enhanced heat transport properties. In this work, thermal transport properties were investigated for TiO2/CuO hybrid nanofluids. Morphological characteristics were studied using SEM (Scanning Electron Microscopy). Elemental composition and average crystallite size were determined using XRD (X-ray Diffraction) and EDX (Energy Dispersive Spectroscopy). Hybrid nanofluids were produced by suspending TiO2/CuO nano powder of 1–50 nm into the water. Three different concentrations (0.01%, 0.03% and 0.05%) of the hybrid nanofluids were prepared. Thermal transport properties like density, viscosity, specific heat and thermal conductivity were assessed experimentally. The results depict that the thermal conductivity was enhanced by 65.8% in contrast to the base fluid at a volume concentration of 0.05%. Density, as well as dynamic viscosity, of TiO2/CuO hybrid nanofluids at 0.05% concentration is increased and specific heat is decreased with the increase in volume concentration.

Thermal lens study of hydrothermally synthesised graphitic carbon nitride nanofluids for heat transfer applications

Thermal lens study of hydrothermally synthesised graphitic carbon nitride nanofluids for heat transfer applications

Characterization of Thermophysical and Electrical Properties of SiC and BN Nanofluids

Experimental data associated with the thermophysical properties (TPPs) of various nanofluids (NFs) are essential for their diverse applications in energy storage and conversion, as well as thermal management. This study experimentally investigated important TPPs such as thermal conductivity (TC), thermal diffusivity, density and viscosity, as well as the electrical conductivity of two new types of NFs, namely silica (SiC) and boron nitride (BN) nanofluids. The NFs are prepared at five low concentrations of nanoparticles from 0.01 to 0.05 vol.% dispersed into a mixture of ethylene glycol (EG) and distilled water (DW). The TPPs are measured, and their enhancements are evaluated in comparison with their base fluids. The results show a good increase in TC and thermal diffusivity for both types of nanofluids with increasing concentrations until reaching the maximum enhancement of about 4.4% for the SiC nanofluid and about 7.0% for the BN nanofluid at the same concentration (0.05 vol.%). On other hand, a Newtonian rheological behaviour is observed, and viscosity and density are also found to increase for both types of NFs, where the maximum increase in viscosity and density at 0.05 vol.% are found to be 5.2% and 0.3%, respectively. The electrical conductivity also increases by up to 3.2 times for SiC nanofluids and 2.8 times for BN nanofluids at the maximum concentration of these nanoparticles (0.05 vol.%) compared with the base fluid (EG/DW). The overall evaluation of the obtained results demonstrates the great potential of these nanofluids in heat transfer applications.

The effect of pH on stability and thermal performance of graphene oxide and copper oxide hybrid nanofluids for heat transfer applications: Application of novel machine learning technique

This paper investigates the effects of pH on stability and thermal properties of copper oxide (CuO), graphene oxide (GO), and their hybrid nanofluid (HNF) at different mixing ratios. Initially, sol-gel and Hummer’s method was employed for the synthesis of CuO and GOnanoparticles (NPs), and they are characterized with various techniques. The effects of two different surfactants were analyzed on nanofluid’s (NF’s) stability at different pH values. The properties like thermal conductivity (TC) and viscosity (VST) of NFs were measured at different volume concentration (0.1 vol% to 1.0 vol%) and temperature range of 30–60 °C, respectively. The TC and VST of GO/CuO (50:50) HNF are higher than that of GO/CuO (20:80). The figure of merit (FOM) is determined for the studied HNFs. The correlations were presented to calculate the TC as well as VST of HNFs. Two modern novel machine learning-based ensemble approaches were employed for predictive model development for TC and VST of considered HNFs. The comparison of prognostic models with Taylor’s diagram revealed that Bayesian optimized support vector machine (BoA-SVM) was superior to Bayesian optimized boosted regression trees (BoA-BRT) for both TC and VST models.

Measurement of Thermal Conductivity and Viscosity of a ZrO2-TiO2 Hybrid Nanofluid for Heat Transfer Applications

Measurement of Thermal Conductivity and Viscosity of a ZrO2-TiO2 Hybrid Nanofluid for Heat Transfer Applications

Effect of surfactant on stability, thermal conductivity, and viscosity of aluminium oxide–methanol nanofluids for heat transfer applications

• synthesis of nanofluids by suspending various surfactants–Al 2 O 3 in pure methanol. • surfactant effect on stability and thermophysical properties of MBNF have studied. • more than 180 days stability was found for Al 2 O 3 –methanol–(CTAB) nanofluids. • significant enhancement of thermal conductivity was found. Nanofluids, suspension of nanoparticles, perform better in heat transfer applications due to their excellent thermal properties over conventional heat-transfer fluids. In this study, the stability of Aluminium Oxide (Al 2 O 3 ) methanol nanofluids and their thermophysical properties (thermal conductivity (TC), viscosity, and density) are experimentally investigated. Various surfactants and nanoparticles were suspended into methanol and sonicated to stabilise the suspension at different weight fractions (0.05–0.15 wt%) of the nanoparticles and surfactants. The stability was analysed for the same ratios of different wt.% of nanoparticles and surfactants as a function of time. All the experiments were carried out at a temperature of 298 °K. Nanofluids characterisation, zeta potential and physical observations revealed 0.10 wt% of Al 2 O 3 –methanol with cationic surfactant cetyltrimethylammonium bromide (CTAB) surfactant nanofluids were stable for more than 180 days (6 months). The experimental results found that the TC, viscosity, and density of Al 2 O 3 –methanol nanofluids were all an increasing function of wt.% of nanoparticles. The addition of surfactant into the Al 2 O 3 –methanol nanofluids slightly affects the TC, viscosity, and density. The maximum enhancement of TC with surfactant was found to be 13.7% which was 1.4% lower than without surfactant of Al 2 O 3 –methanol nanofluids. However, the Al 2 O 3 –methanol nanofluids were more stable and had enhanced thermal properties such as (TC, viscosity, and density) over the base fluid. Therefore, the results encourage using these nanofluids in heat transfer applications.

Rheological and Thermal Conductivity Study of Two-Dimensional Molybdenum Disulfide-Based Ethylene Glycol Nanofluids for Heat Transfer Applications

The rheological behavior of two-dimensional (2D) MoS2-based ethylene glycol (EG) nanofluids (NFs) was investigated at low volume concentrations (0.005%, 0.0075%, and 0.01%) in a wide temperature range of 0–70 °C and at atmospheric pressure. A conventional two-step method was followed to prepare NFs at desired volume concentrations. Based on the control rotational (0.1–1000 s−1 shear rate) and oscillation (0.01–1000% strain) methods, the viscoelastic flow curves and thixotropic (3ITT (three interval thixotropic) and hysteresis loop) characteristics of NFs were examined. Shear flow behavior revealed a remarkable reduction (1.3~14.7%) in apparent dynamic viscosity, which showed concentration and temperature dependency. Such remarkable viscosity results were assigned to the change in activation energy of the ethylene glycol with the addition of MoS2. However, the nanofluids exhibited Newtonian behavior at all temperatures for concentrations below 0.01% between 10 and 1000 s−1. On the other hand, strain sweep (@1Hz) indicated the viscoelastic nature of NFs with yielding, which varied with concentration and temperature. Besides, 3ITT and hysteresis loop analysis was evident of non-thixotropic behavior of NFs. Among all tested concentrations, 0.005% outperformed at almost all targeted temperatures. At the same time, ~11% improvement in thermal conductivity can be considered advantageous on top of the improved rheological properties. In addition, viscosity enhancement and reduction mechanisms were also discussed.

Experimental investigation of dispersion stability and thermophysical properties of ZnO/DIW nanofluids for heat transfer applications

Metal-oxide based nanofluids have grabbed many researchers' attention because of their better dispersion stability and enhanced thermophysical properties for potential heat transfer applications. This work presents an experimental investigation of well-dispersed ZnO nanoparticles' dispersion stability and thermophysical properties in deionized water. Aqueous ZnO nanofluids with different mass concentrations (0.012, 0.024, 0.036, and 0.048%) of nanoparticles were synthesized using a typical two-step method with and without using surfactants. Sodium hexametaphosphate and acetylacetone were used as stabilizing agents in this study. Under optimal operating conditions and surfactants' concentration, acetylacetone-based nanofluids were found stable for more than 60 days. Viscosity and thermal conductivity of nanofluids have been investigated in the 20–60 °C temperature range. Both viscosity and thermal conductivity of nanofluids increased with increasing nanoparticle loading, while with an increase in temperature, viscosity decreased, in contrast to an increase in nanofluids' thermal conductivity. At a fixed temperature and concentration of nanoparticles, maximum enhancement in viscosity and thermal conductivity was recorded as 16.75% and 23.70%, respectively, for sodium hexametaphosphate stabilized nanofluids. The existing well-known theoretical models failed to predict the nanofluids' viscosity and thermal conductivity; however, the proposed new correlations well projected the experimental findings.

Investigation on thermophysical properties of zinc oxide nanofluid for heat transfer applications

In this present work, nanosize zinc oxide particles were prepared via modest coprecipitation synthesis route. Powder X-ray diffraction pattern was applied for calculating the size of crystallites in the prepared sample. Fourier Transform Infra-Red spectrum established the Zn-O bands at 539 and 433 cm−1 in nano ZnO sample. The structural analysis was done using Scanning Electron Microscopy (SEM) studies for ZnO nanoparticles. Ultrasonicated two step procedure was followed to disperse the produced nanoparticles in the combination of water (60%) and ethylene glycol (40%) to produce ZnO nanofluids with five different particle volume fractions from 0.0125% to 0.1%. To overcome the stability problem and uniform distribution of nanoparticles within the fluid, Cetyl Trimethyl Ammonium Bromide (CTAB) dispersant was added within the suspension while preparation. The investigation results of viscosity, density as well as thermal conductivity at various temperatures showed the enhanced results than the base fluid combination. Huge improvements in thermophysical properties leads the ZnO nanofluid as an alternate working liquid in heat transferring systems.