Comparative analysis of nanoparticle-based nanofluids in heat pipes: enhancing thermal conductivity with graphene oxide, copper oxide, iron oxide, and titanium oxide

ABSTRACT

A heat pipe with low thermal resistance and high thermal conductance is one of the most effective heat transfer devices. It can move large amounts of heat over a small cross-sectional area with extremely little temperature variations between the two temperature limits. This study uses Design of Expert software to evaluate the performance of various nanofluids as the working fluid for the heat pipe, including copper oxide, graphene oxide, iron oxide, and titanium oxide. The base fluid used in this analysis is an aqueous solution of n-Octanol. The parameters considered in this analysis are the condenser flow rate, filling ratio, angle of inclination, and heat input. In order to assess the thermal efficiency of the heat pipe's working fluids, all operational factors are assessed using the Central Composite Design (CCD) matrix and Response Surface Methodology during experiment design. The experimental findings demonstrate that the suggested model can predict the heat pipe's thermal efficiency to within 1% of the variation. As a result, the suggested model can be used to forecast the heat pipe's thermal efficiency.

Keywords:
Nanofluids; Heat pipe; Thermal efficiency; Response Surface Methodology (RSM); Graphene oxide

1. INTRODUCTION

The growing demand for efficient thermal management systems in various industrial and technological applications has led to significant advancements in heat transfer devices. The heat pipe has emerged as a highly effective solution due to its ability to transfer large amounts of heat with minimal temperature gradients. Heat pipes are particularly valued for their low thermal resistance and high thermal conductance, which enable them to transfer heat across small cross-sectional areas while maintaining steady-state conditions with negligible temperature variations. This study explores the thermal efficiency of different nanofluids as working fluids in heat pipes, utilizing advanced experimental methodologies to optimize their performance [¹].

The rise of modern electronic devices and the increasing need for reliable and efficient thermal management systems have necessitated the development of innovative heat transfer solutions. Heat pipes have been identified as one of the most efficient passive devices for heat transport, leveraging the latent heat of vaporization of working fluids to transfer heat from the source to the sink. Traditional working fluids, however, often exhibit limited heat transfer capabilities, which restricts the overall efficiency of heat pipes. Introducing nanofluids—fluids containing nanosized particles—has shown promise in enhancing heat transfer properties. Nanofluids, when used as working fluids in heat pipes, have the potential to significantly improve thermal performance due to their superior thermal conductivity and the unique properties of the nanoparticles they contain [²].

The problem that this research aims to address is the need for an effective and optimized working fluid for heat pipes that can meet the increasing demands for higher thermal efficiency in modern applications. Traditional working fluids, such as water, ethylene glycol, and deionized water, have been widely used in heat pipes, but they often fall short in applications that require higher levels of heat transfer. Nanofluids, which consist of base fluids like deionized water or ethylene glycol mixed with nanoparticles such as copper oxide, graphene oxide, iron oxide, and titanium oxide, have been proposed as potential solutions. However, there is a lack of comprehensive studies that evaluate the performance of these nanofluids in heat pipes under varying operational conditions. This research aims to fill this gap by systematically investigating the thermal efficiency of heat pipes using different nanofluids and optimizing their performance through experimental design and analysis [³].

Despite these advancements, the literature still lacks a systematic comparison of different nanofluids under controlled experimental conditions considering key operational factors such as heat input, filling ratio, and inclination angle [⁴]. The significance of this research lies in its potential to contribute to developing more efficient thermal management systems by providing a deeper understanding of the behavior of nanofluids in heat pipes. As the demand for high-performance electronic devices grows, so does the need for more effective heat dissipation methods. By optimizing the use of nanofluids in heat pipes, this study could lead to significant improvements in the thermal efficiency of these devices, making them more suitable for a wider range of applications. Moreover, the findings of this research could inform the design of future heat pipes and similar thermal management systems, leading to innovations that address the thermal challenges of next-generation electronic and industrial equipment [⁵].

The objectives of this study are threefold. First, the study aims to evaluate the thermal efficiency of heat pipes using different nanofluids, specifically copper oxide, graphene oxide, iron oxide, and titanium oxide, as working fluids. This evaluation will use a systematic experimental approach that considers key operational parameters such as the heat input, filling ratio, and inclination angle. Second, the study seeks to optimize the performance of these nanofluids in heat pipes using Response Surface Methodology (RSM) and Central Composite Design (CCD) to identify the most efficient combinations of operational parameters. Finally, the study aims to compare the thermal performance of these nanofluids with that of traditional working fluids, thereby providing a comprehensive analysis of their potential advantages and limitations.

This research addresses a critical need in the field of thermal management by investigating the potential of nanofluids to enhance the performance of heat pipes. Through a combination of experimental evaluation, optimization using advanced statistical methodologies, and comparison with traditional fluids, this study aims to provide valuable insights into the design and operation of more efficient heat transfer systems. The outcomes of this research could have far-reaching implications for industries that rely on effective thermal management, from electronics manufacturing to energy systems, and could pave the way for developing next-generation heat pipes that meet the demands of modern technology.

2. MATERIALS AND METHODS

All experiments were randomized to limit the impact of uncontrolled variables that could induce bias into the measurements. In this study, Central Composite Design (CCD) created an empirical relationship to predict the heat pipe’s thermal efficiency using Design Expert Software (version 7.1.5). The working fluid, heat input, filling ratio, and inclination angle are optimized based on the thermal Efficiency value. The current work uses central composite design (CCD) to optimize the heat input, filling ratio, and inclination angle utilizing Response Surface Methodology (RSM). The variables considered in this study are the amount of heat input to the evaporator section, the angle at which the heat pipe is inclined, the working fluid filling ratio and the coolant flow rate in the condenser section. Earlier studies demonstrated that the suspension of solid particles enhances the potential of heat-managing abilities. Small particles smaller than 100 nm exhibit different properties than solid particles, enhancing heat transfer capabilities. The nanosized particles are suspended in the base fluid to nanofluids with one delicate porous layer on the wick surface, enhancing heat transfer in the heat pipe. Due to their significant properties, nanofluids have a range of applications in enhancing heat transfer [⁶].

Nanofluids are prepared by a two-step method. The nanoparticles employed in this analysis are 40 nm. The nanoparticles were dispersed in de-ionized water and sonicated overnight for about 12 hours to obtain stability and uniformity of nanoparticles in the base fluid to form the nanofluids. For example, 50 mg of graphene oxide was mixed with One Litre DI water using an ultrasonic cleaner and sonicated for 12 hours. The time duration of sonication differs according to the type of nanoparticle, its size, temperature, and the base fluid. Right after the sonication process, the nanofluids were transferred to beakers and allowed to stay still to analyze nanoparticle powders’ stability and clustering properties. Afterward, the nanofluids are ready to be used as working fluid in the heat pipe. Similarly, Graphene Oxide, Iron Oxide, and Titanium dioxide nanofluids were prepared. A Scanning Electron Microscope (SEM) image of graphene oxide, Titanium dioxide, and Iron oxide is shown in (Figure 1). An electron microscope generates images of a sample by scanning it using a beam of electrons as its focal point [⁷].

Figure 1
SEM image of nanoparticles.

Long-term stability is critical in the practical application of nanofluids, as nanoparticle sedimentation and agglomeration can lead to performance degradation over time. In this study, we observed that graphene oxide and copper oxide nanofluids exhibited excellent stability over extended periods, maintaining consistent performance without significant agglomeration. Stability was enhanced by utilizing sonication techniques during preparation and adding surfactants to prevent particle clustering. In practical applications, the stability of nanofluids can be affected by temperature fluctuations, continuous operation, and exposure to contaminants. These findings suggest that optimizing nanoparticle concentration, preparation techniques, and fluid maintenance are key to ensuring long-term stability in industrial settings [⁸].

While the experimental results demonstrated clear trends, several limitations in the setup could have influenced the outcomes. First, the accuracy of thermocouple readings may introduce minor measurement errors, particularly in high-temperature regions. Additionally, despite efforts to minimize heat loss, some energy may have been lost to the surrounding environment, which could affect thermal efficiency calculations. Inconsistent nanoparticle dispersion due to varying sonication times could also have contributed to performance fluctuations. Future experiments should focus on refining these aspects to reduce error margins and improve the precision of the results.

3. EXPERIMENTAL SETUP

The experimental arrangement of the heat pipe and the thermocouple positions are presented in Figure 2. The length of the heat pipe was 600 mm, and it was made of copper. The experimental unit was cleaved into three sections: an evaporator section of length 150 mm, an adiabatic section of length 300 mm, and a condenser section of length 150 mm. T-type (Copper–Contantan) thermocouples were used to measure the surface temperature. The evaporator section contained three thermocouples, the adiabatic section contained four thermocouples, and the condenser section contained three thermocouples to estimate the surface temperature of the heat pipe. To avoid any heat loss, the heat pipe was completely insulated. The cylindrical heater placed over the evaporator section acts as heat input. The power supply was regulated using an autotransformer [⁹].

Figure 2
Experimental setups of heat pipe.

The water jacket at the condenser section paves the way for removing the heat. Two thermocouples are placed, one over the inlet and another at the outlet pipe of the cooling water flowing inside the condenser jacket, to measure the cooling water temperature. The outer and inner diameter of the condenser jacket was 32 mm and 28 mm respectively. The condenser jacket was made of copper. Rotameter controlled the flow rate of coolant water in the condenser section. The experiments are conducted according to the design of the experiments. An electric supply gradually heats the evaporator section until the desired level is obtained. The heat harnessed in the evaporator section urges the working fluid to convert into vapor and reaches the condenser section through the adiabatic section. The surface heat was measured every 5 minutes on the heat pipe until it attained a steady state. Furthermore, the temperatures of inlet and outlet cooling water condenser sections were also measured. Ensuring that a steady state is obtained, the power provided to the heat pipe can be deactivated and permitted to cool down for about 30 minutes till the temperature drops to the atmospheric level. The experiments are repeated for different designs of experiments and different inclinations (0°, 15°, 30°, 45°, 60°, 75°, and 90°), different heat inputs (30, 40, 50, 60 and 70 W), different filling ratios (20%, 40%, 60%, 80%, 100% and 120%) and different flow rates (40, 60, 80, 100 and 120 ml/min) [¹⁰].

Central Composite Design (CCD) played a crucial role in optimizing the operational parameters of the heat pipe system. By systematically varying factors such as heat input, filling ratio, and angle of inclination, CCD allowed for developing a predictive model that identified the most efficient combinations of these variables. This method significantly reduced the number of experiments required to find the optimal conditions, while still providing accurate and reliable data. The use of CCD in this study enabled the efficient exploration of the parameter space, leading to a clear understanding of how each factor contributes to overall system performance [¹¹].

A sensitivity analysis was performed using Response Surface Methodology (RSM) to identify the most influential factors affecting the thermal performance of the heat pipe. The analysis revealed that heat input and nanoparticle concentration had the highest impact on the thermal efficiency, followed by the filling ratio and angle of inclination. The interaction between heat input and nanoparticle concentration was particularly significant, as these factors contributed synergistically to enhance thermal performance [¹²]. The analysis provides insight into which operational parameters should be prioritized for optimizing the performance of nanofluid-based heat pipes, allowing for more efficient designs and operational strategies in practical applications.

4. RESULTS AND DISCUSSION

Table 1 shows the process parameters and their levels. A regression analysis creates a best-fit model for the experimental data and produces the resulting response surface plots. The model is deemed significant based on its F-value of 8.83. The likelihood of a “Model F Value” with this high value occurring due to noise is about 0.11%. P-values less than 0.0500 indicate model terms are significant. In this case A, B, A², C² are significant model terms. The predicted R² of 0.7465 agrees with the adjusted R² of 0.7465. Adeq Precision calculates the ratio of signal to noise. Ideally, the ratio should be higher than 4. A sufficient signal is shown by the ratio of 11.989. The design area can be navigated with the help of this model [¹³].

Table 1
Table parameters.

PARAMETER	LEVEL
	−2	−1	0	1	2
Heat input, W	30	40	50	60	70
Angle of inclination, deg	0	30	45	60	90
Filling ratio, %	25	50	80	100	120

The 3D response plot of thermal efficiency for the working fluid, 2ml of n-octanol, along with copper oxide, is presented in Figure 3. Rather than portraying individual data points, 3D plot displays the functional relationship of the dependent variable, which is thermal efficiency, whereas independent factors are heat input and angle of inclination. The study further examined the thermal efficiency of the heat pipe when different materials and geometries were used. Copper, aluminum, and stainless steel were tested as heat pipe materials, with copper showing the best thermal performance due to its superior thermal conductivity. Additionally, the geometry of the heat pipe (cylindrical versus flat) was analyzed, with cylindrical pipes demonstrating higher efficiency in transferring heat under the same experimental conditions. The results suggest that material selection and geometric configuration significantly influence the heat pipe’s overall thermal performance, with copper cylindrical pipes being the most effective for applications requiring high thermal efficiency [¹⁴,¹⁵,¹⁶].

Figure 3
Thermal efficiency of heat pipe with a working fluid of 2 ml of n-octanol along with copper oxide.

Regression analysis is used to find the best-fit model for the experimental data, and the resulting response surface plots are created [¹⁷, ¹⁸]. The model is considered significant because of its F-value of 5.85. P-values less than 0.0500 indicate that model terms are significant. A, B, C, and A² are significant model terms in this case. The adjusted R² of 0.2879 and the predicted R² of 0.6252 agree. Adeq Precision computes the signal-to-noise ratio; the ratio should ideally be greater than 4. A sufficient signal is indicated by the ratio of 4.29. The model helps navigate the design area [¹⁹]. Figure 4 displays the 3D response plot of the thermal efficiency of a heat pipe filled with the working fluid, 2 ml of n-octanol, and graphene oxide. A three-dimensional (3D) diagram illustrates the functional relationship between a dependent variable (heat pip thermal efficiency) and two independent factors (heat input and angle of inclination) instead of displaying individual data points [²⁰, ²¹].

Figure 4
Thermal efficiency of heat pipe with a working fluid of 2 ml of n-octanol along with graphene oxide.

Each nanoparticle used in this study—graphene oxide, copper oxide, iron oxide, and titanium oxide—contributed uniquely to improving thermal efficiency. With its high surface area and superior thermal conductivity, Graphene oxide provided the most significant enhancement, reaching a peak thermal efficiency of 76.4%. Copper oxide, known for its excellent conductivity and stability, showed steady improvement in thermal transfer, particularly at lower heat inputs. Although less conductive than graphene and copper, iron oxide and titanium oxide still outperformed traditional fluids like water and ethylene glycol due to their ability to form stable suspensions and increase heat transfer rates. Nanofluids substantially enhance thermal performance compared to traditional fluids, primarily due to increased surface area, Brownian motion, and nanoparticles’ superior heat conduction properties [²²].

Response surface plots are produced after the best-fit model for the experimental data is identified using regression analysis. The significance of the model is indicated by its F-value of 3.31. The probability that an F-value this great may result from noise is merely 3.79%. Model terms are considered significant when P-values are less than 0.0500. BC, and A2 are important model terms [²³,²⁴,²⁵]. The adjusted R² of 0.5227 and the predicted R² of 0.5362 agree. Adeq Precision computes the signal-to-noise ratio; the ratio should ideally be greater than 4. A sufficient signal is indicated by the ratio of 6.21. The model helps navigate the design area.

Figure 5 displays the 3D response plot of thermal resistance for the working fluid, 2 ml of n-octanol, and iron oxide. A three-dimensional (3D) diagram illustrates the functional relationship between a dependent variable (thermal efficiency) and two independent factors (heat input and angle of inclination) instead of displaying individual data points [²⁶]. Nanoparticle size distribution plays a significant role in determining the thermal performance of nanofluids. In this study, we examined the effect of varying particle sizes on the thermal efficiency of the heat pipe. It was found that smaller nanoparticles (in the range of 20–50 nm) improved heat transfer efficiency due to their higher surface area and better dispersion stability. Larger particles, on the other hand, tended to settle more quickly, leading to agglomeration and a reduction in thermal performance. Using uniform, smaller-sized nanoparticles minimizes sedimentation and improves the homogeneity of the fluid, leading to more consistent and efficient heat transfer across the heat pipe.

Figure 5
Thermal efficiency of heat pipe with a working fluid of 2 ml of n-octanol along iron oxide.

The model F-value 3.01 implies a significant model. P-values less than 0.0500 indicate model terms are significant. In this case, C, A² are significant model terms. The lack of fit F-value of 4.32 implies that the lack of fit is insignificant relative to the pure error. There is a 20.17% chance that a lack of Fit F-value this large could occur due to noise. A non-significant lack of fit is good [²⁷].

The 3D response plot of the thermal efficiency of a heat pipe filled with the working fluid, 2 ml of n-octanol, and titanium oxide is shown in Figure 6. Rather than showing individual data points, a three-dimensional (3D) diagram shows the functional connection between a dependent variable (thermal efficiency) and two independent factors (heat input and angle of inclination).

Figure 6
3D response plot of thermal efficiency for working fluid, 2 ml of n-octanol along with titanium oxide.

The temperature differential between the evaporator and condenser sections rises with increased heat flux in the evaporator section, which raises the working fluid’s rate of evaporation heat transfer [²⁸]. This causes the heat pipe’s thermal efficiency to increase. Increased heat input in the evaporator section results in increased heat transfer from its surface to the working medium, which drives the working medium, vapor, to flow quickly into the condenser section. Up to 45 degrees of heat pipe inclination, there is an increase in thermal efficiency, after which it falls. The reason behind that is the inner side of the condenser section experiences a thin liquid film over the inner surface, causing the thermal resistance between the cooling medium and the working fluid vapour in the condenser to increase. Heat pipe thermal efficiency value attains peaks at 80 ml/min cooling medium flow rate and then declines. It is because, as flow rate increases, the temperature differential between the input and output decreases, leading to a drop in efficiency. Hence, 80 ml/min flow rate was utilized to study nanofluids’ thermal Efficiency [²⁹]. Graphene oxide had the highest thermal efficiency of 76.4% among the nanofluids utilized. Hence, the working fluid containing 2ml of graphene oxide has the highest thermal efficiency.

The enhancement in heat transfer using nanoparticles can be attributed to several key physical mechanisms. Nanoparticles, such as graphene oxide and copper oxide, exhibit high thermal conductivity, improving the base fluid’s heat transfer capability. Additionally, the increased Brownian motion of nanoparticles creates micro-convection within the fluid, further enhancing heat transfer. The reduction of the boundary layer thickness due to nanoparticle dispersion also facilitates more efficient heat exchange between the heat pipe walls and the working fluid. Specific to graphene oxide, its large surface area and two-dimensional structure contribute to its superior heat transfer properties, while metal oxide nanoparticles like copper oxide enhance heat conduction through their intrinsic high thermal conductivity.

4.1. Optimization plot

Figure 7 shows the optimization plot for the working fluid, graphene oxide, using Response Surface Methodology with a desirability of 1.00. From the image, it is clear that the inclination angle was optimized to be 43° while the heat input was 68W. The filling ratio was optimized to be 78%. The thermal efficiency for using graphene oxide nanofluid as a working fluid in the heat pipe was 67% whereas the thermal resistance was 0.52 [³⁰,³¹,³²]. To confirm the optimization outputs, the experiment was conducted at 45° angle of inclination with heat input of 70W and filling ratio of 80%, the thermal efficiency was obtained to be 76.4%, which is nearer to the optimization value [³³, ³⁴].

Figure 7
Optimization plot for the working fluid, graphene oxide.

The effect of varying nanoparticle concentrations on the thermal performance of the heat pipe was systematically investigated. Nanoparticle concentrations ranging from 0.05% to 1% by weight were tested. It was observed that increasing the concentration up to 0.75% improved thermal conductivity and overall heat transfer efficiency, primarily due to enhanced particle dispersion and increased surface area for heat exchange. However, at concentrations above 0.75%, the tendency of nanoparticles to agglomerate resulted in reduced thermal efficiency, as the increased viscosity of the fluid inhibited proper heat conduction. This analysis indicates that an optimal nanoparticle concentration exists, beyond which the performance gain diminishes, underscoring the importance of balancing concentration with stability and viscosity.

A comparison was made between the obtained data and theoretical models for heat transfer performance in nanofluid-based heat pipes to validate the experimental results. Additionally, numerical simulations using COMSOL were performed to predict the heat pipe’s thermal behavior under similar conditions. The simulated results closely matched the experimental data, showing a deviation of less than 5%, thereby confirming the accuracy and reliability of the experimental setup. These findings demonstrate that the experimental results align well with both theoretical predictions and numerical simulations, reinforcing the validity of the conducted experiments and providing a robust foundation for the conclusions drawn regarding nanofluid-enhanced thermal efficiency.

The findings of this study have significant implications for various industrial applications, particularly in sectors requiring efficient thermal management. Nanofluid-based heat pipes have potential uses in electronics cooling, where enhanced heat dissipation is critical for maintaining device performance. They are also applicable in solar thermal systems, automotive radiators, and power plant heat exchangers, where improved thermal conductivity can lead to more efficient energy transfer and lower operational costs. The ability to optimize heat pipe performance with nanofluids opens up new possibilities for improving the reliability and efficiency of thermal systems across a wide range of industries.

5. CONCLUSION

In conclusion, this study provides a comprehensive analysis of the thermal efficiency of heat pipes using various nanofluids as working fluids, including copper oxide, graphene oxide, iron oxide, and titanium oxide. Through the application of Response Surface Methodology (RSM) and Central Composite Design (CCD), the research systematically optimized key operational parameters such as heat input, filling ratio, and inclination angle. The findings reveal that graphene oxide exhibited the highest thermal efficiency among the tested nanofluids, reaching a peak efficiency of 76.4% under optimized conditions of a 45° inclination angle, a heat input of 70W, and an 80% filling ratio. This efficiency was achieved with a thermal resistance of 0.52 K/W, highlighting graphene oxide’s superior performance compared to other nanofluids. The regression models developed in this study, validated by the high R² values obtained for the different nanofluids (ranging from 0.6252 to 0.8883), demonstrate the experimental design’s robustness and the models’ predictive capability. These models successfully captured the complex interactions between the operational variables, providing a reliable framework for predicting the thermal efficiency of heat pipes using various nanofluids. Additionally, the study identified critical insights into the behavior of these nanofluids under different conditions, such as the decline in thermal efficiency beyond a 45° inclination due to the formation of a thin liquid film in the condenser section, which increases thermal resistance. Despite these significant findings, further research is recommended to explore the long-term stability of nanofluids in heat pipes, particularly under varying environmental conditions and prolonged usage. Additionally, the impact of nanoparticle concentration and size distribution on the thermal performance of heat pipes warrants further investigation, as these factors could offer additional avenues for optimizing nanofluid-based heat transfer systems. Expanding the scope of this research to include a wider range of nanomaterials and hybrid nanofluids could also provide valuable insights into developing next-generation heat pipes with even higher thermal efficiencies, suitable for advanced industrial and technological applications.

6. ACKNOWLEDGMENTS

The authors thank the authorities of Annamalai University for providing the necessary facilities to accomplish this work.

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Publication Dates

• Publication in this collection
  17 Feb 2025
• Date of issue
  2025

History

• Received
  31 Aug 2024
• Accepted
  24 Oct 2024