Thermophysical Properties Of Nanofluids Research Articles

Enhancing thermal management systems: a machine learning and metaheuristic approach for predicting thermophysical properties of nanofluids

Abstract In thermal engineering, predicting nanofluid thermophysical properties is essential for efficient cooling systems and improved heat transfer. Traditional methods often fall short in handling complex datasets. This study leverages machine learning (ML) and metaheuristic algorithms to predict key nanofluid properties, such as specific heat capacity (SHC), thermal conductivity (TC), and viscosity. By utilizing Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gradient Boosting (GB), and Linear Regression (LR), alongside metaheuristic models like Differential Evolution (DE) and Particle Swarm Optimization (PSO), we achieve superior prediction accuracy compared to traditional models. The integration of these computational techniques with empirical data demonstrates their effectiveness in capturing the complex dynamics of thermofluids. Our results validate the precision of ML and metaheuristic models in predicting nanofluid properties and underscore their potential as robust tools for researchers and practitioners in thermal engineering. This work paves the way for future exploration of ML algorithms in thermal management, marking a significant advancement in optimizing nanofluid applications in industry and research.

Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks

Accurately predicting thermo-physical properties (TPPs) of MXene/graphene-based nanofluids is crucial for photovoltaic/thermal solar systems, driving focused research on developing precise TPP predictive models. This study presents optimized multi-layer perceptron neural network (MLPNN) models, leveraging Bayesian optimization to refine architectural and training hyperparameters, including hidden layers, neurons, activation functions, standardization, and regularization terms. A comparative analysis of Bayesian acquisition functions—the probability of improvement (POI), lower confidence bound (LCB), expected improvement (EI), expected improvement plus (EIP), expected improvement per second plus (EIPSP), and expected improvement per second (EIPS)—demonstrated that the POI-MLPNN achieves the most accurate results, as evidenced by the lowest MAPE of 1.0923 % and exceptional consistency with an R-value of 0.99811. The EI-MLPNN and EIP-MLPNN models recorded the same outputs. The EI/EIP-MLPNN (R = 0.99668) model excels in consistency over LCB-MLPNN (R = 0.99529) and EIPSP-MLPNN (R = 0.99667). The optimized models offer a reliable, cost-efficient alternate for experimental and computational TPP analyses. Leveraging insights from these models enables better control over nanofluid TPPs in solar systems, enhancing energy conversion efficiency.

Numerical Investigation of Nanofluid’s Heat Transfer Performance in Passive Residual Heat Removing System of AP1000 Nuclear Reactor

The Passive Heat Removal system (PHRS) is designed to remove the residual heat from the core in case of a station blackout, failure of emergency core cooling system, or failure of feedwater supply through the Passive Residual Heat Removal Heat Exchanger (PRHR HX). PRHR HX consists of a C-shaped tube bundle as a heat exchanger and the In-Containment Refueling Water Storage Tank (IRWST) as a heat sink. A temperature distribution of this passive heat removal system of an AP1000 Reactor is generated using COMSOL Multiphysics and the heat transfer coefficient is calculated to illustrate the effectiveness of the PHRS. A comparison of the heat transfer coefficient between the IRWST filled with water and nanofluid has been generated using the PRHR HX design. Thermophysical properties of nanofluids have been calculated in the process of calculating the heat transfer coefficient. Numerical results show the difference in temperature reduction of Al2O3, TiO2, and Ag as opposed to water in the IRWST. Time-dependent heat conduction of water and nanofluid results contribute to the effective analysis of passive heat removal systems and provide information for the safe operation of AP1000 reactors. By the end of 2024/2025, two VVER-1200 power stations with a combined capacity of 2400 MW will be operating in Bangladesh. For safety and licensing reasons, heat transfer simulation of VVER-1200 can be performed using COMSOL software.

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.

Nanofluids: Why we love them?

The rising need for fluids with significantly improved heat transfer properties led to the development of nanofluids. The first experiments showed encouraging results reflected in increased thermal conductivity and heat transfer coefficient accompanied with better stability than colloid suspension. Many research laboratories and companies observed the potential of nanofluid technology for specific industrial applications. However, after publication of numerous papers with contradictory results for the same or similar nanofluids, many issues arose. Although in some branches of industry nanofluids have already found practical applications, at some point researchers went back to basics, conducting extended studies and benchmark tests in attempt to explain the nanoparticle influence on thermophysical properties of nanofluids. The final goal of the whole scientific community is to produce nanofluids at low cost, exhibiting long-term stability, and good fluidity as the three most significant preconditions toward practical applications in the heat transport field.

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.