Thermophysical Properties Of Nanofluids Research Articles

Stability, Thermophysical, Optical and Photothermal Properties of ZnO Nanofluids with Added Anionic/Cationic Mixed Surfactants

Metal oxide nanofluid is a new type of heat transfer medium with good thermal performance, which can be used to improve the heat collection and photothermal conversion performance of the collector. The development of new nanofluids with excellent stability, thermophysical and photothermal properties is very important for solar thermal utilization. In this paper, the ZnO nanofluids with SDS/CTAB mixed surfactants were prepared by two-step method. Their stability, thermophysical, optical and photothermal properties were studied based on experimental data. Then, the optimum concentration and preparation conditions of the proposed ZnO nanofluids adding SDS/CTAB with good photothermal performance were obtained. The results showed that the ZnO nanofluids with the addition of SDS/CTAB hybrid surfactant has good dispersion stability and its thermal conductivity can reach 0.772 W/(m·K). The transmittance was as low as 19.0.10% and the extinction coefficient was as high as 7.25 cm−1. In addition, the addition of the SDS/CTAB hybrid surfactant caused the ZnO nanofluids to exhibit better photothermal conversion efficiency up to 87%, which was superior to that of the control group with the addition of other surfactants. Therefore, ZnO nanofluids with the addition of SDS/CTAB have great potential as DASC working fluids.

Maximizing heat transfer potential with graphene nanofluids along with structured copper surfaces for pool boiling

Increasing the demand and depletion of energy sources confront every technological development with energy conversation and its utilization. Boiling heat transfer is the most applicable heat transfer method in many industries, from electronic chip manufacturing to heavy nuclear power plants. Hence, the augmentation of it is of prime importance for future requirements. The present investigation focused on the augmentation of boiling heat transfer by considering a novel V-pattern staggered copper surface for pool boiling in graphene nanofluid media. The study encompasses a detailed exploration of nanofluid concentrations ranging from 0.1% to 0.40 vol% prepared in equal volumes of dimethylformamide and distilled water. The nanofluid's stability is observed to be quite promising, and the results are presented. The wall superheat was drastically reduced to 48.73%, and the maximum heat transfer coefficient enhancement of 83.47% was achieved for 0.04% concentration of graphene nanofluid with a V-pattern surface compared to a smooth copper surface with water. This is due to the micro-level structured convection patterns promoted by the microstructures. The thermophysical properties of nanofluid and peculiar microstructures enhanced the relative heat transfer coefficient by 63.37%. On the other hand, two popular machine learning models, Random Forest Regression (RFR) and Multi-Layer Perceptron (MLP), are used to predict the boiling heat transfer coefficient. Moreover, it is observed that the RFR model fitted the data well and yielded the lowest mean absolute percentage error within the bounds of computational time.

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.

Investigation of the effects of various nanoparticles on improvement of hydrogen production rate in a solar energy driven alkaline electrolyzer

Present paper aims to investigate effects of nanoparticle addition to thermal oil of parabolic trough collectors which are integrated to Kalina cycle for electricity generation to run an alkaline electrolyzer for hydrogen production. Thermodynamic models for Kalina cycle, thermal model for parabolic collectors and electrochemical models for water electrolyzer are developed for overall plant modeling. Three types of nanoparticles including: copper oxide, alumina and titanium oxide are considered and overall plant performance is investigated using these nanoparticles and is compared with basefluid. The nanofluid thermophysical properties are evaluated as a function of nanoparticles’ properties, volume fraction and the base fluid properties. The effects of key variables such as nanoparticle volume fraction, collector inlet temperature, evaporation pressure and ammonia concentration of Kalina system were evaluated on hydrogen production as well as exergetic efficiencies. Results revealed greater positive influence for CuO compared to TiO2 and Al2O3 on performance improvement for both hydrogen generation and its exergetic efficiency. Also, compared to basefluid (without nanoparticles) case, it was found that CuO particles result in a solar-to-H2 exergy efficiency improvement by 4.7%. Using this nanofluid with a volume fraction of 5%, the H2 production rate enhances from 0.633kg/hto0.664kg/h, indicating 4.9 % improvement.

Numerical study of the volume fraction and thermophysical properties of nanofluids in a porous medium

PurposeThe purpose of the paper is to conduct a numerical and experimental investigation into the properties of nanofluids containing spherical nanoparticles of random sizes flowing through a porous medium. The study aims to understand how the thermophysical properties of the nanofluid are affected by factors such as nanoparticle volume fraction, permeability of the porous medium, and pore size. The paper provides insights into the behavior of nanofluids in complex environments and explores the impact of varying conditions on key properties such as thermal conductivity, density, viscosity, and specific heat. Ultimately, the research contributes to the broader understanding of nanofluid dynamics and has potential implications for engineering and industrial applications in porous media.Design/methodology/approachThis paper investigates nanofluids with spherical nanoparticles in a porous medium, exploring thermal conductivity, density, specific heat, and dynamic viscosity. Studying three compositions, the analysis employs the classical Maxwell model and Koo and Kleinstreuer’s approach for thermal conductivity, considering particle shape and temperature effects. Density and specific heat are defined based on mass and volume ratios. Dynamic viscosity models, including Brinkman’s and Gherasim et al.'s, are discussed. Numerical simulations, implemented in Python using the Langevin model, yield results processed in Origin Pro. This research enhances understanding of nanofluid behavior, contributing valuable insights to porous media applications.FindingsThis study involves a numerical examination of nanofluid properties, featuring spherical nanoparticles of varying sizes suspended in a base fluid with known density, flowing through a porous medium. Experimental findings reveal a notable increase in thermal conductivity, density, and viscosity as the volume fraction of particles rises. Conversely, specific heat experiences a decrease with higher particle volume concentration.xD; xA; The influence of permeability and pore size on particle volume fraction variation is a key focus. Interestingly, while the permeability of the medium has a significant effect, it is observed that it increases with permeability. This underscores the role of the medium’s nature in altering the thermophysical properties of nanofluids.Originality/valueThis paper presents a novel numerical study on nanofluids with randomly sized spherical nanoparticles flowing in a porous medium. It explores the impact of porous medium properties on nanofluid thermophysical characteristics, emphasizing the significance of permeability and pore size. The inclusion of random nanoparticle sizes adds practical relevance. Contrasting trends are observed, where thermal conductivity, density, and viscosity increase with particle volume fraction, while specific heat decreases. These findings offer valuable insights for engineering applications, providing a deeper understanding of nanofluid behavior in porous environments and guiding the design of efficient systems in various industrial contexts.

Variational Derivation of a Nonlinear Meso-Scale Model: Validating Thermophysical Properties of Nanofluids with Experimental Data

Variational Derivation of a Nonlinear Meso-Scale Model: Validating Thermophysical Properties of Nanofluids with Experimental Data

Thermophysical Properties of SWCNT Nanofluid

AbstractThe current paper has the objective of exploring the thermophysical properties (TPPs) of single walled carbon nanotube (SWCNT) nanofluids (NFs) for heat transfer applications. The effect of dispersing the nanotubes in conventional heat transfer fluids (HTFs) has been investigated. Carbon nanotubes (CNTs) have exceptionally high thermal conductivities. Using the effective medium theory, density, specific heat, thermal conductivity, and viscosity has been evaluated as functions of volume fractions. The base fluids considered in this work are water, ethylene glycol, engine oil, and kerosene oil as these are the commonly used HTFs for industrial applications. Theoretical predictions from this study show an increase in density, thermal conductivity, and viscosity with volume fraction while there is a decrease in specific heat with volume fraction. The effect of the aspect ratio (AR) of CNTs on the thermal conductivity of NF is also investigated. For low volume fractions of SWCNT, the thermal conductivity has been found to increase with AR. Further, this study has been extended for a real‐world application of heat transfer using SWCNT. In this paper, a numerical investigation of heat transfer in a square enclosure with differentially heated vertical walls, filled with SWCNT‐water NF under natural convection is carried out. Due to low concentrations of SWCNT in water, single phase flow is assumed. Velocity and temperature distributions are obtained as solutions to the energy and Navier–Stoke's equations. A comparison of the temperature and velocity profile has been represented by simulating the system over a range of Rayleigh numbers.

Entropy analysis of MHD hybrid nanoparticles with OHAM considering viscous dissipation and thermal radiation

This research explores the 3-D flow characteristics, entropy generation and heat transmission behavior of nanofluids consisting of copper and titanium in water as they flow across a bidirectional apparent, while considering the influence of magneto-hydrodynamics. The thermophysical properties of nanofluids are taken advantage of utilizing the Tiwari and Das demonstrate. The concept of the boundary layer has facilitated the comprehension of the physical ideas derived from it. By applying requisite transformations, the connected intricate sets of partial differential equation have been converted into ordinary differential equation. The modified model is calculated employing the widely recognized technique known as OHAM by using Mathematica program BVPh2.0 Software. For different dimensionless parameters computational and graphical investigations have been performed. It is notice that as fluid parameters change, they exhibit distinct responses in comparison to the temperature, velocity profiles and entropy generation. The results show that velocity profile rise with greater estimates of the magnetic parameter and the rate of entropy formation. Furthermore, thermal profiles become less significant as Eckert and Prandtl numbers increase.

Thermophysical and Electrical Properties of Ethylene Glycol-Based Nanofluids Containing CaCO3

The thermophysical properties of various types of nanofluids are often studied to find more effective working fluids for heat transfer applications. In this paper, the mass density, isobaric heat capacity, thermal conductivity, dynamic viscosity surface tension, and electrical properties of calcium carbonate-ethylene glycol (CaCO3-EG) nanofluids were investigated. The samples with mass fractions of 0.01, 0.02, and 0.03 were prepared with a two-step method and studied as well as pure base fluid (ethylene glycol). The measurements were conducted at temperatures between 283.15 and 313.15 K and the obtained results show the impact of CaCO3 nanoparticles on the thermophysical and electrical properties of ethylene glycol.

A comparative study of thermo‐physical properties of different nanofluids for effective heat transfer leading to Li‐ion battery pack cooling

AbstractThe rapid advancement in Lithium‐ion battery technology has significantly boosted the electric vehicle market worldwide. These batteries are highly sought after by automobile manufacturers and researchers due to their exceptional energy storage and power capabilities. However, efficient working temperatures ranges from 15°C to 35°C, making it essential to implement battery cooling and heating methods to maintain optimal performance. This study primarily focuses on battery cooling. The automotive manufactures have focused on ethylene glycol, water and synthetic oils as commonly used coolants for keeping the lithium‐ion battery pack within working temperature limits. However, these fluids have lower heat conduction, leading to reduced heat transfer rates and diminished cooling performance. To address this issue, nano‐enhanced fluids, which are engineered fluids containing suspended nanospheres, have been explored. The heat transfer properties of conventional coolants are increased using nano‐enhanced fluids, thereby improving heat conduction and promoting efficient heat transfer within the battery. The study compares the temperature‐related characteristic of different nano‐enhanced fluids, including oxides of copper (CuO), titanium (TiO2), aluminum (Al2O3), silicon (SiO2), and zinc (ZnO), when added to base fluids such as synthetic oils, water and ethylene glycol. The importance to capacity of specific heat, conductivity of fluid thermally, fluid resistance to flow and fluid volumetric mass which are affected by changes in temperature limits are stressed upon. Depending on the specific application and test conditions, the effectiveness of one nano‐enhanced fluid may outperform the others. In conclusion, the thermo physical properties of nanofluids play a critical role in maintaining the battery pack temperature within operating temperature limits. The selections of nanofluids for particular battery thermal management system depends upon its arrangement along the battery pack and cell geometry. The cooling performance using nanofluids as coolant is 10% to 15% higher than the conventional used fluids. This nano‐enhanced fluid show great promise in enhancing the cooling capabilities of battery cooling systems, contributing to improved Lithium‐ion battery performance in electric vehicles.

Improving Thermo-Hydraulic Performance with Varying Concentrations of Alumina Nanofluids: A Numerical Investigation

In the current study, the investigation of heat transfer and fluid flow Characteristics of Pure water when pass through a double tube heat exchanger (DTHX). This investigation has been conducted across various Reynolds Number to gain insights into their performance also conducted a computational fluid dynamics (CFD) simulation using the ANSYS-FLUENT 22 R1 software. The study employed mathematical models and thermophysical properties of nanofluids and water, which were sourced from existing literature. The analysis focused on comparing pure water, 1% Al2O3/H2O nanofluids. The investigation considered various operating variable as Reynolds Number and temperature across the inner, and outer tubes. Specifically, the Reynolds Number of a range of 2500 to 5500 at 80°C, and 2500 at 15°C for the respective tubes. Key findings are that friction factor for pure water, 1% alumina nf, 2% alumina nf, and 3% alumina nf is increased by 4.61%,11.42%,15.06% and 16.21% as compared to Gnielinski correlation in existing literature at a Reynolds Number of 2500 and this increase in friction factor is 5.66%, 13.79%, 18.03% and 19.61% respectively at Reynolds number of 5500. Nusselt number (Nu) for pure water, 1% alumina nf, 2% alumina nf, and 3% alumina nf is increased by 24.92%, 50.04%, 59.90% and 64.31% as compared to Gnielinski correlation in existing literature at a Reynolds Number of 2500 and this increase is 10.84%, 28.68%, 35.31% and 41.55% respectively at Reynolds number of 5500. The heat transfer coefficients (hi) for pure water, 1% alumina nf, 2% alumina nf, and 3% alumina nf is increased by 3.17%, 7.29%, 8.49% and 8.94% as compared to Gnielinski correlation in existing literature at a Reynolds Number of 2500 and this increase is 8.04%, 18.49%, 21.54% and 22.64% respectively at Reynolds number of 5500.

APPLICABILITY OF MACHINE LEARNING TECHNIQUES IN PREDICTING SPECIFIC HEAT CAPACITY OF COMPLEX NANOFLUIDS

The complexity of the interaction between base fluids and nano-sized particles makes the prediction of nanofluid thermophysical properties difficult. However, machine learning techniques can be utilized as an alternative approach due to their ability to identify complex nonlinear patterns in data and make accurate forecasts. This paper presents intuitive predictions of specific heat of various types of nanofluids using machine learning models based on experimental data obtained from 47 different studies, comprising 5009 data points. Three machine learning algorithms, namely, artificial neural network (ANN), support vector regression (SVR), and extreme gradient boosting (XGBoost), were tested to develop a universal predictor for nanofluid specific heat. To enhance the performance of the machine learning models, the best set of input variables was selected, and hyperparameter optimization was conducted to maximize the prediction accuracy. The accuracy of three selected machine learning models [i.e., MLP (a type of ANN), SVR, and XGBoost] and their unseen data prediction capability were compared with existing complicated empirical models, and the results showed that the machine learning-based predictions were more accurate. The machine learning models demonstrated excellent agreement with experimental nanofluid specific heat data. Particularly, the extreme gradient boosting method (i.e., XGBoost) showed the best nanofluid specific heat forecast results with minimal prediction error and presented broad range of applicability.

Numerical simulation on nanofluid enhancement of downward facing surface’s critical heat flux

In this work, an improved wall boiling model for nanofluids was proposed, taking into account the effect of bubble slip on the wall heat flux density, as well as the effects of nanofluid thermophysical properties and nanoparticle deposition on the density of nucleation sites and bubble departure diameter. The nucleation site density and bubble departure diameter were calculated and compared with the experimental data, the deviations were not more than 7.80% and 9.81%, respectively. The downward-facing surface’s critical heat flux (CHF) was numerically simulated using Al2O3-H2O nanofluids. The simulation results of CHF were compared with the experimental data, and the maximum deviation did not exceed ±16.9%. Compared to pure water, the average CHF enhancement of 0.001–0.01vol% Al2O3-H2O nanofluid was 65.4%. The impacts of thermophysical properties, contact angle, and surface roughness were analyzed separately using the control variable method. The results showed that the wall temperature and void fraction were mostly not affected by thermophysical properties, and the variation in contact angle and surface roughness had a substantial impact. For the CHF improvement of 0.001vol% Al2O3–H2O nanofluids, the proportions of contact angle, surface roughness, and thermophysical properties on the CHF enhancement are 57%, 8%, and 1%. CHF enhancement with nanofluid was strongly related to the changes in contact angle and surface roughness.

Investigation of the Thermophysical Properties of Nanofluids Based on Metal Oxides: Application in Concentrated Solar Power Plants

Solar energy is a renewable source of energy that does not emit greenhouse gases. The sun is free, inexhaustible and available all over the world. The sun's rays can be used to produce energy in two ways. The first technique converts the sun's rays into electricity using photovoltaic panels, while the second converts the sun's rays into heat using concentrated solar power plants (CSP). Several studies have been carried out with the aim of improving the performance of these solar power plants in order to achieve high efficiency, which is the case in our study. In this work a numerical study was carried out on the effect of nanoparticles on the thermophysical properties of nanofluids with the aim of determining the most optimal nanofluid for use as a heat transfer fluid in concentrated solar power plants. The nanoparticles examined were metal oxides (SIO2, MgO and Fe3O4), which were dispersed in Therminol VP-1 and Syltherm 800. The thermophysical properties examined were density, thermal conductivity and heat capacity. To carry out this study, we set the temperature from 200 to 400°C at the same operating temperature as the concentrating solar power plant.After evaluating the effect of nanoparticles on the thermophysical properties, we studied the behaviour of the CSP plant based on nanofluids using SAM (System Advisor Model) software. The results obtained are very encouraging and show that the addition of nanoparticles to a base fluid improves its thermophysical properties compared with the pure base fluid, and the rate of improvement in thermal conductivity exceeds 9%. We also found that the nanofluid (Fe3O4 /Therminol Vp1) is the best selected for use as a heat transfer fluid in concentrated solar power plants with an efficiency and thermal energy produced equal to 40.87% and 588164 MWht, respectively.

Comparison of the use of different nanofluids for heat transfer from the outer surface of spiral tubes: Energy, exergy, and exergoeconomic (3E) analysis

Heat exchangers play a crucial role in industrial processes by facilitating the efficient transfer of thermal energy. The thermophysical properties of nanofluids, which consist of nanoparticles dispersed in conventional heat transfer fluids, are enhanced. Their remarkable thermal conductivity and convective heat transfer characteristics have the potential to significantly improve heat transfer processes. In the present investigation, a numerical study has been carried out to investigate the effect of different nanofluids on heat transfer from outer surface of a spiral coil based on the first and second laws of thermodynamics. The numerical solutions are checked against previous experimental and numerical research in the field. Nusselt number, Euler number, exergy efficiency, irreversibility, and equipment cost are studied with respect to concentration, mass flow ratio (Rm), and shell and tube inlet temperature for a variety of nanofluids, including Al2O3/H2O, CuO/H2O, Fe2O3/H2O, and TiO2/H2O. Compared to water, the exergy efficiency is lower while using nanofluids, as shown by the results, especially in higher nanofluid concentrations, and among the different nanofluids, Al2O3–H2O nanofluid was found to have the highest thermodynamics second law efficiency. The exergy destruction rate experiences linear growth by increasing the nanofluid concentration. A higher pressure drop based on the Euler number is expected for nanofluids. In addition, the consumption of nanofluids on the shell side results in a considerable reduction of the purchased equipment cost (PEC) value compared to water. As opposed to the tube-side input temperature, variations in the shell-side inlet temperature found to have a greater impact on the heat exchanger's exergetic performance.

Modelling the thermal conductivity of nanofluids using a novel model of models approach

In this study, a unique method for modelling the thermal conductivity of nanofluids is proposed using a "model of models" approach. Three distinct data streams are utilised to achieve this. The first stream uses experimental data to predict thermal conductivity, an input for the primary machine learning model. The other stream involves modelling correlations from previous studies and integrating them as an additional input. Lastly, theoretical data streams are modelled and included as a last stream. By training a model on these combined data streams, the study aims to overcome various challenges in modelling nanofluids' thermophysical properties. The research holds great significance as it can potentially reconcile and understand errors that come with various modelling methods. This could result in improved model performance that closely resembles experimental data. The presented model in the model of models’ approach achieves a remarkable coefficient of determination (R-squared) value of 0.999 on the test data set, showcasing its exceptional accuracy and effectiveness in handling complex data, particularly about the thermophysical properties of nanofluids. Furthermore, this implicit general model comprises of data models incorporating material properties and physical phenomena, offering broad applicability. It is recommended that this approach be extended to viscosity, enhancing the understanding and prediction of nanofluid properties.

Enhancing Thermal Performance of Evacuated Tube Solar Collector using Novel Graphene Oxide Nanofluid

The research article investigates the thermal performance of heat pipe-based evacuated tube solar collector (ETSC) experimentally using graphene oxide (GO) and deionised (DI) water as working fluid with a mass flow rate of 0.5 lit/min, 0.75 lit/min, and 1 lit/min. The different volumetric concentrations of 0.001%, 0.002%, and 0.003% graphene oxide nanofluid samples were prepared in the deionised water. The X-ray diffraction (XRD) was used to determine the structural properties of graphene oxide and the morphology of graphene oxide was studied by scanning electron microscope (SEM). To evaluate the stability of nanofluid samples, the Zeta potential analysis was carried out which showed that prepared nanofluid samples remain stable for up to 30 days. The effect of different nanofluid concentrations on various thermo physical properties of nanofluid was studied and discussed. The thermal performance of ETSC was investigated by considering the effect of volumetric concentrations of nanofluid and mass flow rates. According to the findings, there is a significant increment in temperature difference and energy gain by using nanofluid samples, and the maximum thermal efficiency of ETSC was found to be 37.1% for a volumetric concentration of 0.003% at 1 lit/min mass flow rate as compared to water.

Effects of Al2O3 Concentration in Ethylene Glycol on Convection Heat Transfer Coefficient

The energy demand is more in the world due to increase in the populations. The sustainable and clean renewable energy is required to meet the demand. The solar energy with nanofluid used as heat transfer medium is the best alternative source to enhance the rate of heat transfer. The nanofluids are the suspended nano sized particles in the water, ethylene glycol or oil. The stability analysis of Al2O3 ethylene glycol carried out using zeta potential method. The 20nm sized Al2O3 nanoparticles with volume concentration from 0.01% to 1% in ethylene glycol is used as nanofluid to study the effects of concentration on convective heat transfer coefficient (HTC) and wall function HTC at temperature 298K and mass flow rate 0.033kg/s. The investigation also carried out to study the effects of concentration on its thermophysical properties of nanofluid. ANSYS fluent software used to carry out the numerical analysis with suitable thermal boundary conditions.

Nanofluids Minimal Quantity Lubrication Machining: From Mechanisms to Application

Minimizing the negative effects of the manufacturing process on the environment, employees, and costs while maintaining machining accuracy has long been a pursuit of the manufacturing industry. Currently, the nanofluid minimum quantity lubrication (NMQL) used in cutting and grinding has been studied as a useful technique for enhancing machinability and empowering sustainability. Previous reviews have concluded the beneficial effects of NMQL on the machining process and the factors affecting them, including nanofluid volume fraction and nanoparticle species. Nevertheless, the summary of the machining mechanism and performance evaluation of NMQL in processing different materials is deficient, which limits preparation of process specifications and popularity in factories. To fill this gap, this paper concentrates on the comprehensive assessment of processability based on tribological, thermal, and machined surface quality aspects for nanofluids. The present work attempts to reveal the mechanism of nanofluids in processing different materials from the viewpoint of nanofluids’ physicochemical properties and atomization performance. Firstly, the present study contrasts the distinctions in structure and functional mechanisms between different types of base fluids and nanoparticle molecules, providing a comprehensive and quantitative comparative assessment for the preparation of nanofluids. Secondly, this paper reviews the factors and theoretical models that affect the stability and various thermophysical properties of nanofluids, revealing that nanoparticles endow nanofluids with unique lubrication and heat transfer mechanisms. Finally, the mapping relationship between the parameters of nanofluids and material cutting performance has been analyzed, providing theoretical guidance and technical support for the industrial application and scientific research of nanofluids.

Experimental determination of NiFe2O4-water nanofluid thermophysical properties and evaluation of its potential as a coolant in polymer electrolyte membrane fuel cells

Heat is generated as a byproduct of the electrochemical reactions of a polymer electrolyte membrane fuel cell. This heat raises the temperature of the system, and if it is not properly removed from the cell, it can cause membrane damage and overheating. Thermal management is thus critical for PEM fuel cell stability, efficiency, and safety. This paper investigates the potential use of NiFe2O4-water nanofluid as a novel coolant for PEM fuel cells. To determine the nanofluid thermophysical properties, an experimental study for different mass% concentrations of NiFe2O4-water nanofluid is first performed. Then, a three-dimensional CFD model is built to demonstrate the effect of nanofluid use on the thermal performance of a cooling plate. Simulation results show that replacing water with 0.5% NiFe2O4 nanofluid improves temperature uniformity by 11.97%. Furthermore, nanofluid cooling reduces maximum surface temperature by up to 0.75 °C while increasing pressure drop by 5.6%, implying higher pumping power.