Specific Heat Capacity Of Nanofluids Research Articles

Molecular dynamics study on thermophysical properties of K2CO3 molten salt-based SiO2 nanofluids using Buckingham potential framework

In concentrated solar power (CSP) systems, molten salt plays a vital role as a medium for both storing and transferring heat. This research conducts molecular dynamics (MD) simulations to investigate the thermophysical properties of K2CO3 molten salt-based SiO2 nanofluids within the Buckingham potentialframework. MD results show that the Buckingham potential along with the derived parameters can well reproduce the thermophysical properties of K2CO3 molten salt. At 1.0 wt% of nano-SiO2, the nanofluids achieve a 11.80 % enhancement in specific heat capacity compared with the pure molten salt. The thermal conductivity of molten salt nanofluids is increased as the doping ratio of nano SiO2 is increased. Specifically, when the doping ratio reaches 3.0 wt%, the thermal conductivity is increased by 14.31 % compared with the undoped base salt. It has been found that the enhanced specific heat capacity of nanofluids is primarily resulted from the change in the Coulomb energy due to the addition of the nano-SiO2 and a compression layer surrounding the nanoparticles. Heat flow decomposition of the nanofluid has also shown that its enhanced thermal conductivity is mainly caused by the strengthened nonbonded interactions by adding the nanoparticles.

Advancing nanofluid analysis: A GBR-GSO predictive model for accurate prediction of specific heat capacity in nanofluids

Nanofluids, characterized by the dispersion of nanoparticles in a base liquid, have attracted significant attention in recent years due to their exceptional thermal properties. Specifically, the specific heat capacity of nanofluids plays a crucial role in the design and optimization of heat transfer systems. Traditional experimental methods for determining the specific heat capacity of nanofluids are often limited in terms of cost, time, and operating condition ranges. To address these limitations, this research focuses on the development of a novel predictive model for estimating the specific heat capacity of nanofluids. This study aims to develop a machine learning regression model called Gradient Boost Regression (GBR) with Grid Search optimization (GSO) for accurately predicting the specific heat capacity of aluminum nitride (Al2N3) nanoparticles that are suspended in both water and an ethylene glycol (EG) solution. The GBR-GSO model capitalizes on the strengths of GBR, which can effectively capture complex relationships, and GSO, a metaheuristic optimization technique inspired by the law of gravity. By integrating these two approaches, we aim to create a robust and accurate predictive model for specific heat capacity in nanofluids. To develop and validate the GBR-GSO model, a diverse dataset based on experimental-specific heat capacity collected from the literature has been designed. The performance of the model has been evaluated by comparing its predictions with experimental data. The GBR-GSO model achieved 99.99% accuracy with the experimental data of specific heat capacity. This research contributes to the advancement of nanofluid-based heat transfer systems by providing an effective tool for predicting the specific heat capacity of nanofluids. The developed model can facilitate the design and optimization of various engineering applications, leading to the development of energy-efficient and sustainable technologies.

Heat Transfer Enhancement of Energy Pile with Nanofluids as Heat Carrier

As one of the core components of the transport medium in the energy pile system, the circulating working fluid is crucial for the enhancement of its heat transfer efficiency. In this study, based on the heat transfer enhancement theory of nanofluids, deionized water was used as the base solution to prepare the corresponding 0, 0.5, 1, and 1.5 vol% nanofluids by introducing nanoparticles SiO2, Al2O3, CuO, and Cu. The laws of thermal conductivity and specific heat capacity with the volume fraction of different nanofluids are studied by the comparison. The results of thermal conductivity research illustrate that the specific heat capacity of nanofluids decreases linearly with the increase of the volume concentration of nanoparticles, but the thermal conductivity increases linearly. The fluid with the highest thermal conductivity and specific heat capacity is Cu–water nanofluids. In addition, it is proposed to directly apply all kinds of nanofluids with 1 vol% as circulating fluid to self-made miniature model box of energy pile to simulate the working conditions in summer. The influence laws of the nanofluids on the flow rate and heat transfer efficiency in the energy pile were calculated through the temperature difference between the inlet and outlet of the heat exchange tube. The results reveal that the nanofluids used in the experiment exert a optimization influence on the heat transfer of energy pile but the mass flow rate of the nanofluids during the circulation is decreased compared with that of the deionized water circulation liquid. Among them, the temperature difference between inlet and outlet of Cu–water nanofluid is the largest, which has the best effect on improving the heat transfer efficiency of the energy pile but its mass flow loss is also the largest. The research results can provide a reliable reference for the improvement of heat transfer efficiency of energy pile engineering in the future.

Experimental study on glycerol/aldehyde or ketone binary nanofluids for thermal management

Glycerol is a bio-based low-cost chemical, which has been widely studied in the field of heat conduction because of its wide working temperature range and superior thermopysical. However, high viscosity of glycerol is a prominent problem that limit the application of glycerol. In this work, low viscosity and high thermal conductivity of glycerol nanofluids are prepared under the dual role of nanoparticles, in which nanoparticles work as the nanoadditives enable an enhancement of thermal conductivity and specific heat capacity of nanofluids and the catalyst make a decline of viscosity of nanofluids. The thermal and viscosity properties of the nanofluid were investigated, and the addition of 15% acetylacetone and 2% TiO2 increased the thermal conductivity of the nanofluid to 0.3194 W/m·K, which was 8.12% higher than that of the glycerol. Meanwhile, the viscosity was reduced to 357.4 mPa (30 °C) with 15% acetylacetone and 0.5% TiO2 nanoparticles, which was 47% lower than that of glycerol at the same temperature. Additionally, results of fourier transform infrared spectrometer and proton nuclear magnetic resonance demonstrate that acetal reaction plays an important role in reducing the viscosity of nanofluids, meanwhile, the tests of X-ray diffraction show that nanoparticles can be utilized circularly.

Experimental study on sodium acetate trihydrate/glycerol deep eutectic solvent nanofluids for thermal energy storage

With the development of electronic machinery, the traditional heat exchange working fluid can no longer be used in some extreme working environments. As a new type of fluid, deep eutectic solvent has the advantages of promising thermal stability, low cost and non-toxicity through the hydrogen bond association of hydrogen bond acceptor and hydrogen bond donor, and is also coined to be an efficient heat transfer working fluid. In this work, three kinds of nanofluids filled with TiO2, Al2O3 and Fe2O3 nanoparticles were prepared with glycerol/sodium acetate trihydrate deep eutectic solvent as the base solution, and the mechanism of thermal conductivity change of nanofluids were discussed from atomic and microscopic levels. The experimental results showed that the prepared glycerol/ sodium acetate trihydrate 2:1 deep eutectic solvent system reduced the viscosity by 58.03% compared with glycerol, and increased the thermal conductivity by 17.2% compared with glycerol at 25 ℃. The effects of nanoparticles on thermal conductivity and specific heat capacity were also investigated. Compared with deep eutectic solvent, the thermal conductivity was improved after adding Fe2O3 nanoparticles; it is worth noting that the specific heat capacity of nanofluids was more than doubled after adding nanoparticles.

Impact of data processing and robust machine learning process on accurate estimation of specific heat capacity property in energy storage applications

The specific heat capacity propery is a critical thermal property because it is directly related to heat storage and transfer. The need for better and novel thermal energy storage materials have led to the discovery of high temperature nanofluids with molten salts (nanosalts). The utilization of novel machine learning tools in accurately estimating the specific heat capacity of nanofluids, would further pave the way for more innovative studies on nanosalt, hence the prediction of specific heat capacity of hybrid nanofluids constitute a significant research area. Despite the few researches conducted in the area of prediction of these fluids, priority with regards to the robust analysis of input variables for different machine learning algorithms have not been given due consideration. Therefore, this present study aims to develop and validate a robust package of machine learning algorithms (twenty machine learning algorithms) in estimating the specific heat capacity of hybrid nanofluids, across different feature spaces. The different algorithm hyperparameters were adjusted to give optimum prediction. A total of 600 data points were retrieved and split into training, testing, and validation data set, for which the validation dataset was not used in the training and testing process. The input variables used in the training of the machine learning models are temperature, volume fraction, the specific heat capacity of the base fluid, the specific heat capacity of individual nanoparticles, and the mixture ratio of individual nanoparticles. The Model A, which constituted the seven (7) input variables gave the best prediction accuracy with validation score of 0.999703, while the Model C, which contained the feature variables of temperature, volume fraction and specific heat capacity of the base fluid gave the least prediction accuracy. The optimal machine algorithms retrieved for the Model A, B, C and D was the Ridge, Gradient Boost regressor, Passive Aggressive regressor, and Kernel ridge regressor. The combination of ensemble tree regressors and online learning algorithms can be coded and used in future prediction analysis of SHC, as they have both shown accurate prediction quality.

Exploring the specific heat capacity of water-based hybrid nanofluids for solar energy applications: A comparative evaluation of modern ensemble machine learning techniques

The current study aims to give insight into the usefulness of three underutilized yet exceptionally efficient machine learning approaches in estimating the specific heat capacity (SHC) of nanofluids: Gaussian process regression, extreme gradient boosting, and support vector machine. The study made use of three uncommon metal oxides-multiwall carbon nanotubes-water nanofluids. Several factors influencing the SHC of nanofluids were considered during model development, including volume concentrations (0.25 %–1.5 %), range of temperature (25–50 °C), the base fluid's SHC, the specific heat capacity of the nanofluid, the density of the base fluid, the density of the nanofluid, and the average diameter of the used nanoparticles. All three models predicted the SHC of hybrid nanofluids with high precision, suggesting that they are a viable alternative to the time-consuming, expensive, and complex experimental approaches for measuring the SHC of nanofluids. The extreme gradient boosting (R = 0.9975, mean absolute percentage error = 0.01 %) model outperformed the Gaussian process regression (R = 0.9973, mean absolute percentage error = 0.015 %) and support vector machines (R = 0.9952, mean absolute percentage error = 0.021 %) in terms of prediction performance. The Nash-Sutcliffe efficiency for Gaussian process regression, extreme gradient boosting, and support vector machines models were 0.9956, 0.997, and 0.992, respectively, while Theil's U2 was 0.0752, 0.072, and 0.1015, indicating that the three machine learning algorithms performed well in the precise estimation of SHC of test nanofluids. These prognostic models can help to reduce the time and resources needed for experimental trials.

Experimental studies on thermophysical properties of ethylene glycol/water-based MgO nanofluids

In the present study, the aim is to experimentally measure the isobaric specific heat, viscosity and thermal conductivity of different magnesium oxide (MgO) nanoparticles concentrations dispersed in ethylene glycol/ water mixture with ratio 50:50 vol% as a base fluid. The experiments were performed on 20 nm particle size of MgO nanoparticles suspended in base fluid with different volume fractions from 0.25 to 1% and temperature range from 40 to 120°C. The data results detected that the specific heat capacity of nanofluids reduces as the nanoparticles volume fraction increases but increases as the temperature rises. For all volume concentrations, the dynamic viscosity declines non-linearly with rising temperature. The increase in viscosity related to the base fluid is slightly recognized at low nanoparticles concentrations, while this change is more sensible at higher volume concentrations. Generally, MgO nanofluids showing higher viscosities than the base fluid used. The thermal conductivity of examined nanofluids increases uniformly with increasing the nanofluid sample temperature and nanoparticles volume concentration.

Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their performance. Specific heat capacity of nanofluids, as one of the thermophysical properties, performs principal role in heat transfer of thermal mediums utilizing nanofluids. In this regard, different studies have been carried out to investigate the influential factors on nanofluids specific heat. Moreover, several regression models based on correlations or artificial intelligence have been developed for forecasting this property of nanofluids. In the current review paper, influential parameters on the specific heat capacity of nanofluids are introduced. Afterwards, the proposed models for their forecasting and modeling are proposed. According to the reviewed works, concentration and properties of solid structures in addition to temperature affect specific heat capacity to large extent and must be considered as inputs for the models. Moreover, by using other effective factors, the accuracy and comprehensive of the models can be modified. Finally, some suggestions are offered for the upcoming works in the relevant topics.

Machine learning specific heat capacities of nanofluids containing CuO and Al2O3

AbstractEmpirical work and modeling approaches have shown that fundamental thermophysical parameters of constituents, nanoparticles, and base liquids have complex but synergistic effects on the specific heat capacity of nanofluids. In this work, we develop the Gaussian process regression model to investigate the statistical relationship among temperature, specific heat capacities of nanoparticles and base liquids, nanoparticle volume concentrations, and specific heat capacities of nanofluids. The model is developed with a dataset containing nanofluids with CuO and Al2O3 nanoparticles, and water and ethylene glycol (EG) as base liquids. The model also applies well to nanofluids containing mixtures of water and EG, and Al2O3 nanoparticles with different particle size distributions. The model is highly accurate and stable that contributes to fast and low‐cost estimations of specific heat capacities of nanofluids over a wide range of compositions and temperature.

Experimental investigations of the performance of a flat-plate solar collector using carbon and metal oxides based nanofluids

Covalently functionalized carbon nanoplatelets and non-covalent functionalized metal oxides nanoparticles (surfactant-treated) have been used to synthesize water-based nanofluids in this paper. To prove nanofluid stability, ultraviolet–visible (UV–vis) spectroscopy is used, and the results show that nanofluid is stable for sixty days for carbon and thirty days for metal oxides. The thermophysical properties are evaluated experimentally and validated with theoretical models. Thermal conductivities of f-GNPs, SiO2, and ZnO nanofluids are enhanced by 25.68%, 11.49%, and 15.42%, respectively. Lu-Li and Bruggeman’s thermal conductivity models are correctly matched with the experimental data. Similarly, the viscosity, density, and specific heat capacity of nanofluids are measured and compared with theoretical models. The enhancement in density, specific heat and viscosity of f-GNPs, ZnO, and SiO2 nanofluids are 0.12%, 0.22%, and 0.12%; 1.54%, 0.96%, and 0.73%; 12%, 9.41%, and 24.05% respectively in comparison of distilled water. A flat-plate solar collector is installed, and its thermal performance is evaluated by using carbon and metal oxides based nanofluids, following the ASHRAE standard 93–2003, at different heat flux intensities (597, 775, and 988 W/m2), mass flow rates (0.8, 1.2 and 1.6 kg/min), inlet fluid temperatures (30–50 °C) and the weight concentrations (0.025–0.2%). The thermal efficiency of the flat-plate solar collector is measured for distilled water and compared with the weight concentration (0.025–0.2%) of functionalized carbon and metal oxide-based nanofluids. A comparison of 0.1 wt% water-based nanofluids can be sequenced f-GNPs > ZnO > SiO2 because of a percentage improvement of thermal efficiency of the flat-plate solar collector obtained at a mass flow rate of 1.6 kg/min with values of 17.45% > 13.05% > 12.36%, respectively in comparison to water.

On the assessment of specific heat capacity of nanofluids for solar energy applications: Application of Gaussian process regression (GPR) approach

To characterize the performance of nanofluids for heat transfer applications in solar systems, an accurate estimation of their specific heat capacity (SHC) is of paramount importance. To this end, having such properties of nanofluids via computational approaches has gained attention as an effective method to eliminate the time-consuming process of experimental investigations. This study focuses on modeling the SHC of different carbon-based and metal oxide-based nanoparticles dispersed in various base fluids. Herein, we propose a novel data-driven dynamic model based on the Gaussian process regression (GPR) technique in comparison with the random forest (RF) approach and generalized regression neural network (GRNN) to predict the SHC of nanofluids. The developed models employ the solid volume fraction (ϕ), temperature (T), mean diameter of nanoparticle (Dp), and SHC of base fluid (CPBase) as the input parameters. The data has been collected from 10 reliable references. The results showed that the GPR model (R=0.99974, RMSE=0.01506 J/K.g) shows superior performance than the results of the RF (R=0.99761, RMSE=0.04598 J/K.g) and GRNN (R=0.99563, RMSE=0.06085 J/K.g). The results proved that the developed model would accurately estimate the SHC of the studied nanofluids. In addition, the sensitivity analysis of the dependence of input variables on the SHC of nanofluids revealed that the mean diameter of nanoparticles and the SHC of base fluid are the major critical factors in the determination of SHC of nanofluids.

Efficiency and effectiveness enhancement of an intercooler of two‐stage air compressor by low‐cost Al2O3/water nanofluids

AbstractThe present study was aimed to utilize low‐cost alumina (Al2O3) nanoparticles for improving the heat transfer behavior in an intercooler of two‐stage air compressor. Experimental investigation was carried out with three different volume concentrations of 0.5%, 0.75%, and 1.0% Al2O3/water nanofluids to assess the performance of the intercooler, that is, counterflow heat exchanger at different loads. Thermal properties such as thermal conductivity and overall heat transfer coefficient of nanofluid increased substantially with increasing concentration of Al2O3 nanoparticles. Specific heat capacity of nanofluids were lower than base water. The intercooler performance parameters such as effectiveness and efficiency improved appreciably with the employment of nanofluid. The efficiency increased by about 6.1% with maximum concentration of nanofluid, that is, 1% at 3‐bar compressor load. It is concluded from the study that high concentration of Al2O3 nanoparticles dispersion in water would offer better heat transfer performance of the intercooler.

Analysis on thermal and flow behavior of triple concentric tube heat exchanger handling MWCNT-water nanofluids

Design of heat exchangers and heat transfer enhancement methods are struggling to meet out the cooling demand of present scenario. Many researchers suggested that the addition of nanosized solids particles into traditional base fluids resulting the higher heat transfer rate than the existing coolants and named the new fluids as nanofluids. In this investigation, the effect of microwave carbon nanotube (MWCNT)-water nanofluids on heat transfer rate, pressure drop and pumping power of a triple concentric tube heat exchanger are experimentally investigated and compared the results of MWCNT-water nanofluids with water. The MWCNT-water nanofluids were prepared by two step method at the volume concentrations of 0.2%, 0.4%, and 0.6%. The range of target fluid mass-flow rate is in the range of 0.026 to 0.039 kg per second and the constant heat flux condition is considered. On experimentation, it is noted that the effectiveness and presure drop of 0.6% MWCNT-water based nanofluids are 27% and 21% greater than water at the maximum mass-flow rate. The reason for improved heat transfer rate of nanofluids is because of higher thermal conductivity, Brownian motion, lower boundary-layer thickness, and lower specific heat capacity of nanofluids. Also found that the pumping power increases with increasing volume concentration and pumping power is 25% higher than water at the 0.6% nanofluids. Therefore, the MWCNT-water nanofluids are good choice for replacing water as coolant in triple concentric tube heat exchanger.

Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression

The specific heat capacity of nanofluids (CPnf) is a fundamental thermophysical property that measures the heat storage capacity of the nanofluids. CPnf is usually determined through experimental measurement. As it is known, experimental procedures are characterised with some complexities, which include, the challenge of preparing stable nanofluids and relatively long periods to conduct experiments. So far, two correlations have been developed to estimate the CPnf. The accuracies of these models are still subject to further improvement for many nanofluid compositions. This study presents a four-input support vector regression (SVR) model hybridized with a Bayesian algorithm to predict the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids. The bayesian algorithm was used to obtain the optimum SVR hyperparameters. 189 experimental data collected from published literature was used for the model development. The proposed model exhibits low average absolute relative deviation (AARD) and a high correlation coefficient (r) of 0.40 and 99.53 %, respectively. In addition, we analysed the accuracies of the existing analytical models on the considered nanofluid compositions. The model based on the thermal equilibrium between the nanoparticles and base fluid (model II) show good agreement with experimental results while the model based on simple mixing rule (model I) overestimated the specific heat capacity of the nanofluids. To further validate the superiority of the proposed technique over the existing analytical models, we compared various statistical errors for the three models. The AARD for the BSVR, model II, and model I are 0.40, 0.82 and 4.97, respectively. This clearly shows that the model developed has much better prediction accuracy than existing models in predicting the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids. We believe the presented model will be important in the design of nanofluid-based applications due to its improved accuracy.

An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study

Nanofluids have proven their ability to improve the thermal conductivity of base fluids and heat transfer performance in automobiles, heat exchangers, solar collectors and air conditioning systems. However, the stability and optimum operating conditions of nanofluids require further study. Therefore, this study compares the effect of three different types of surfactants; Gum Arabic (GA), polyvinyl pyrrolidone (PVP) and sodium dodecyl sulfate (SDS) on the stability and thermo-physical properties of carbon nanotubes (CNTs)/water nanofluids which include: density, viscosity, thermal conductivity and specific heat capacity. Furthermore, the heat transfer characteristics and pressure drop of the optimized nanofluid were investigated using a shell and tube heat exchanger. The stability results demonstrate ratios of (1:0.5) and (1:1) are sufficient to achieve a stable nanofluid for more than 6 months using GA and PVP. Moreover, Mathis TCi thermal conductivity analyzer and differential scanning calorimeter were used to measure thermal conductivity and specific heat capacity of nanofluids. The results demonstrate a significant enhancement of thermal properties, 36% in thermal conductivity and 50% in specific heat capacity using 0.1 wt% of CNT. The experimental results of the heat exchanger demonstrate that the heat transfer rate increases with the concentration of CNT up to 65%, with a maximum corresponding increase in pressure drop of about 15% using 0.5 wt% of CNT. The pumping power calculations indicate that the required power to provide the same amount of heat using nanofluids is one-third of that required for water.

Effect of SiO2 nanoparticles on specific heat capacity of low-melting-point eutectic quaternary nitrate salt

In order to evaluate the effect of nanoparticles on the specific heat capacity of a low-melting-point eutectic quaternary nitrate salt, 1.0 wt% SiO2 nanoparticles with an average size of 20 nm was doped into the mixed salt Ca(NO3)2·4H2O-KNO3-NaNO3-LiNO3. A mechanical dispersion mixer was developed to prepare molten salt nanofluids. The effects of the mixing time (15, 45, 90, 120, and 150 min) and stirring rate (600, 750, and 1000 rpm) on the specific heat capacity of the nanofluids were analyzed using a differential scanning calorimeter. The results showed that the specific heat capacity of the nanofluids varied with the mixing time and stirring rate. It increased by ~ 17.0% at a mixing time of 15 min and stirring rate of 750 rpm. Microstructural characterization of the molten salt nanocomposites was performed using scanning electron microscopy, which showed that special nanostructures, which resemble chain-like nanostructures, were formed on the surface of the solid nanofluids. The special nanostructures with a large specific surface area and high surface energy could increase the specific heat capacity of the molten salt nanofluids.

Enhanced heat transfer performance of an automobile radiator with graphene based suspensions

We report the convective heat transfer coefficient and pressure drop of graphene nanoplatelets seeded in water-ethylene glycol mixture flowing through an automobile radiator. The volume concentrations of graphene nanoplatelets were varied from 0.1% to 0.5%. Thermophysical properties such as thermal conductivity, viscosity, density and specific heat capacity of nanofluids were measured experimentally. Mass flow rate of nanofluids were varied from 10g/s to 100g/s. Nanofluid inlet temperature was considered as 35°C and 45°C while the ambient air velocity was fixed as 3m/s for the convective heat transfer experiments. The convective heat transfer coefficient of nanofluids increases with increasing loading of graphene nanoplatelets, nanofluid inlet temperature and mass flow rate. The enhancement of convective heat transfer coefficient for the highest concentration (0.5vol%) and highest mass flow rate (100g/s) was found to be 20% and 51% when the nanofluid inlet temperature was 35°C and 45°C respectively. The pressure drop of nanofluid increases with respect to graphene nanoplatelets loading and mass flow rate. As the loading of nanoplatelets increases from 0 to 0.5vol% the pressure drop increases from 3.07 to 4.88kPa at 35°C while it increases from 3.02 to 4.04kPa at 45°C for 100g/s. The present nanofluid has a potential to replace the conventional heat transfer fluids leading to compact thermal systems.

A critical investigation of the anomalous behavior of molten salt-based nanofluids

A critical investigation is presented in this work to study the effect of nanoparticle addition, temperature, and nanoparticle size-dependence on the specific heat capacity of both conventional and molten salt-based nanofluids. The effects of temperature and nanoparticle volume fraction on the specific heat capacity of conventional nanofluids are in agreement in all studies cited in this review. Different correlations based on the available data were developed as a function of temperature and volume concentration only. However, the effect of nanoparticle size-dependence was ignored in these correlations. A general correlation for Al2O3–water nanofluids, one of the most commonly studied nanofluids, that takes into account the effect of temperature, volume fraction, and nanoparticle size-dependence was developed and verified in this review. Disagreement was reported for the results of the specific heat capacity of molten salt-based nanofluids. A number of studies showed an enhancement in the specific heat capacity of nanofluids using 1% concentration of nanoparticles by weight only. However, other studies have shown deterioration in the specific heat capacity of nanofluids compared with the base mixture using various volume concentrations of nanoparticles. Moreover, very few studies have demonstrated the effect of nanoparticle size-dependence on the specific heat capacity of molten salt nanofluids and disagreement in the results was reported in these studies. Few models based on the conventional specific heat model were developed to determine the specific heat capacity of molten salt nanofluids. These models suffer from the lack of knowledge of many terms in these equations which make them impractical. Different mechanisms were assumed in the literature to explain the abnormal behavior of molten salt nanofluids. Additional theoretical and experimental research studies are required to clarify the mechanisms responsible for specific heat capacity enhancement or deterioration in nanofluids.

A comparative review on the specific heat of nanofluids for energy perspective

Abstract Nanofluid is one of the novel inventions of science. Nanofluid can be used for energy savings by increasing the heat transfer performance of the heat recovery systems, which are generally struggling to overcome the present challenging issues such as global warming, greenhouse effect, climate change, and fuel crisis. Specific heat capacity is necessary to analyze energy and exergy performances. This paper extant different characteristic of specific heat capacity of nanofluids containing preparation and measuring methods, effects of volume fraction, temperature, types and sizes of nanoparticles and base fluids. Additionally a compilation has been done on available theoretical correlation related to specific heat of nanofluid. Based on existing experimental and theoretical results, nanofluid specific heat falls with the enhancement of volume concentration of nanoparticle though there are some inconsistencies among outcomes. Moreover, specific heat of the nanofluids are generally increased after adding dispersant in the mixtures. However, many contradictory results about the effects of temperatures on specific heat of nanofluids found in the literatures. Therefore, this review will help the researchers and related peoples to get enough information to select a nanofluid based on specific heat for their practical applications.