Comparative analysis of nanomaterials in heat pipe performance under variable operational conditions

ABSTRACT

Nanomaterials have emerged as transformative agents in enhancing the thermal performance of heat pipes, which are vital components in modern cooling systems for electronics, aerospace, and renewable energy applications. This study explores the use of nanofluids containing silver (Ag), aluminum oxide (Al₂O₃), and multi-walled carbon nanotubes (MWCNTs) as working fluids in heat pipes, comparing their performance against deionized (DI) water under varying heat inputs, inclination angles, and filling ratios. A copper heat pipe with a stainless-steel mesh wick structure was used in controlled experiments, systematically evaluating thermal resistance and heat transfer coefficients. Results revealed significant improvements in thermal performance with nanofluids. MWCNT nanofluid demonstrated the highest thermal conductivity increase (40%), while Al₂O₃ and Ag nanofluids exhibited 30% and 17.6% improvements, respectively. Optimal performance was achieved at a filling ratio of 80% and a heat input of 60 W, with thermal resistance reduced to 0.87 K/W for MWCNT nanofluid, compared to 1.65 K/W for DI water. These findings underscore the potential of nanomaterials to revolutionize thermal management systems, providing insights into designing more efficient and reliable heat transfer solutions for high-performance environments. Future work will address nanofluid stability and cost-effectiveness in industrial applications.

Keywords:
Nanofluids; Heat pipes; Thermal performance; MWCNTs; Silver

1. INTRODUCTION

Heat pipes have become indispensable in modern thermal management systems, offering exceptional efficiency in transferring heat across various applications, including electronics cooling, aerospace, and renewable energy systems. Operating on the principle of evaporation and condensation of a working fluid, heat pipes continuously circulate the fluid to absorb and dissipate heat effectively. However, as technology advances, traditional heat pipe configurations using conventional working fluids such as deionized (DI) water often cannot meet the increasing demands for higher thermal performance. This limitation has prompted researchers to explore alternative working fluids with enhanced thermal properties, such as nanofluids, engineered by suspending nanoparticles in a base fluid to improve heat transfer efficiency. Nanofluids, due to their superior thermal conductivity and unique thermophysical properties, have shown great promise in addressing the limitations of traditional fluids, making them an attractive option for improving heat pipe performance under diverse operational conditions [¹].

Despite the recognized potential of nanofluids, several challenges persist in their practical implementation, including long-term stability, compatibility with heat pipe structures, and performance under varied operational conditions. While existing studies have highlighted the benefits of individual nanofluids such as silver (Ag), aluminum oxide (Al₂O₃), and multi-walled carbon nanotubes (MWCNTs), a comprehensive understanding of their comparative performance in heat pipes under varying parameters like heat input, inclination angle, and filling ratio remains incomplete. Additionally, there is limited research on hybrid nanofluids, which combine different nanoparticles to harness synergistic thermal properties, and their performance under extreme conditions has not been fully explored. This knowledge gap underscores the need for a systematic investigation that not only evaluates the thermal performance of nanofluids under controlled experimental conditions but also compares these findings to existing methodologies and advanced cooling solutions [²].

The literature on nanofluids has grown significantly in recent years, with numerous studies demonstrating their potential to enhance thermal conductivity and heat transfer efficiency. For instance, researchers have reported that MWCNT nanofluids exhibit up to a 40% improvement in thermal conductivity compared to DI water, owing to carbon nanotubes' unique structural and thermal properties. Similarly, aluminum oxide nanofluids have shown substantial increases in thermal efficiency due to their high heat capacity and stability, while silver nanofluids, though slightly less effective, offer distinct advantages in terms of antimicrobial properties and ease of dispersion. However, existing studies often focus on individual nanofluids without directly comparing their performance under identical experimental setups. Furthermore, studies on the effects of operational parameters, such as heat input variations and inclination angles, are often limited to single configurations, leaving a gap in understanding the interplay between these parameters and nanofluid performance. Recent advances in computational modeling and molecular dynamics simulations have provided insights into nanoparticle interactions and their impact on fluid properties, but experimental validation remains crucial for translating these findings into real-world applications [³].

This research aims to address these gaps by conducting a comprehensive study on the thermal performance of heat pipes using DI water and nanofluids containing Ag, Al₂O₃, and MWCNTs as working fluids. The significance of this research lies in its potential to advance the design and optimization of thermal management systems for high-performance environments. This study provides critical insights into the mechanisms driving enhanced thermal performance by systematically comparing various nanofluids' thermal resistance and heat transfer coefficients under controlled heat input, inclination angle, and filling ratio conditions. Furthermore, including hybrid nanofluids offers a novel perspective on achieving synergistic thermal enhancements, paving the way for next-generation cooling solutions. Table 1 comprehensively reviews recent studies focusing on applying and optimizing nanofluids and advanced thermal systems. The studies cover various objectives, methodologies, and findings, including experimental and simulation-based investigations of nanofluids in heat pipes, enhanced oil recovery, and solar thermal collectors. The key findings highlight the effectiveness of nanoparticles in improving thermal performance, stability, and energy efficiency, while the relevance column connects these studies to the current research by emphasizing shared themes, methodologies, and advancements in the field. This overview underscores the critical role of nanotechnology in advancing thermal management solutions for industrial and scientific applications.

Table 1
Summary of recent studies on nanofluids and thermal systems.

RESEARCH FOCUS/OBJECTIVE	METHODOLOGY	KEY FINDINGS	RELEVANCE TO CURRENT STUDY
Evaluate the combined effects of magnetic field, orientation, and filling ratio on heat pipes	Experimental study under varied conditions of magnetic field, filling ratio, and orientation angles	Fe3O4 nanofluids with magnetic fields enhance heat transfer, optimal filling ratios improve thermal performance	Demonstrates the impact of external magnetic fields and orientation on heat pipe efficiency [4]
Investigate nanoparticle concentrations and magnetic fields in oscillating heat pipes	Molecular dynamics simulations varying nanoparticle percentages and magnetic field intensities	Nanoparticle concentration and magnetic fields influence atomic interactions and thermal efficiency	Provides insights into nanoparticle interactions and magnetic field effects on thermal properties [5]
Optimize silver nanoparticle-enhanced nanofluids for solar thermal collectors	Experimental optimization of nanoparticle size (20–50 nm) and concentration (0.2–0.8 wt%)	Smaller nanoparticles (20 nm) at 0.2 wt% achieved highest thermal conductivity, balance of stability and viscosity crucial	Reinforces the need to optimize nanoparticle size and concentration for thermal applications [6]
Study heat pipe performance using various nanofluids	Numerical modeling with finite volume method, analysis of nanofluids (Alumina, CuO, Silver)	Nanofluids reduce wall temperature and increase thermal efficiency	Validates the effectiveness of nanofluids in enhancing heat transfer in heat pipes [7]
Thermodynamic and economic analysis of solar heating crude oil system	Exergy and economic analysis of a solar-integrated crude oil heating system	Achieves 75.99% energy efficiency, 4.124-year return on investment	Provides insights into integrating innovative systems for enhanced energy optimization [8]
Synthesize and evaluate antibacterial activity of zinc oxide nanoparticles	Synthesis via polyol method, antibacterial activity tested against E. coli	Zinc oxide nanoparticles showed excellent antibacterial activity at concentrations >50 mg/L	Highlights the role of nanoparticles in functional performance for specific applications [9]
Develop and evaluate microfluidic devices for Enhanced Oil Recovery (EOR)	Microfluidic chip experiments analyzing oil recovery under multiphase flow conditions	Optimal configurations recover up to 80% residual oil saturation after water injection	Demonstrates the use of micro and nanotechnology for optimizing fluid dynamics [10]
Evaluate effects of nanoparticles on thermal properties in copper oscillating heat pipes	Molecular dynamics simulations evaluating nanoparticle impacts on thermal properties	Nanoparticles significantly alter thermal properties and optimize heat pipe performance	Highlights the benefits of nanoparticle inclusion for heat pipe designs [11]
Investigate spray characteristics of oils using phase Doppler particle analyzer	Experimental analysis of spray characteristics under varying pressures, flow rates, and nozzle diameters	Spray performance influenced by oil viscosity, pressure, and nozzle diameter	Provides insights into fluid dynamic properties critical for thermal systems [12]

The novelty of this research lies in its holistic approach to evaluating the performance of nanofluids in heat pipes. Unlike previous studies that often focus on isolated parameters or individual nanofluids, this study adopts a comparative methodology to assess the relative advantages of different nanofluids under a consistent experimental framework. By incorporating various operational conditions, including extreme heat inputs and varying geometric configurations of the heat pipe, the research identifies optimal conditions for maximum efficiency and highlights the limitations and trade-offs associated with each nanofluid. Additionally, the study explores the effects of nanoparticle concentrations and the role of dispersion techniques in ensuring long-term stability, addressing key challenges that have hindered the widespread adoption of nanofluids [¹³].

The primary objectives of this study are fourfold: (1) to evaluate the thermal performance of heat pipes using DI water, Ag, Al₂O₃, and MWCNT nanofluids under varying heat inputs, inclination angles, and filling ratios, (2) to compare the experimental results with existing methodologies and computational predictions to validate the findings and establish credibility, (3) to investigate the potential of hybrid nanofluids in achieving superior thermal performance through synergistic effects, and (4) to provide actionable recommendations for the design and optimization of heat pipe systems for industrial applications. These objectives are underpinned by rigorous experimental protocols and comprehensive data analysis, ensuring that the findings contribute meaningfully to the existing body of knowledge in the field.

This research employed a carefully designed experimental setup, featuring a copper heat pipe with a stainless-steel mesh wick structure to ensure consistent fluid return and effective heat transfer. The working fluids were prepared using a standardized sonication process to achieve uniform nanoparticle dispersion, and their thermal properties were characterized using advanced measurement techniques. The experimental parameters, including heat input (ranging from 40 W to 100 W), inclination angle (0° to 90°), and filling ratio (60% to 90%), were systematically varied to capture the full spectrum of operational conditions. Performance metrics such as thermal resistance, overall heat transfer coefficient, and stability indicators were meticulously recorded and analyzed, providing a robust dataset for comparison and interpretation [¹⁴].

The findings of this study hold significant implications for the future of thermal management systems. By demonstrating the superior performance of MWCNT nanofluids under high thermal loads and identifying the optimal conditions for their application, this research offers practical solutions for enhancing the efficiency and reliability of cooling systems in industries such as aerospace, electronics, and renewable energy. The exploration of hybrid nanofluids further expands the potential applications of this technology, opening new avenues for innovation in thermal design. Moreover, the insights gained from this study regarding the stability and scalability of nanofluids provide a strong foundation for their industrial adoption, addressing key concerns related to cost-effectiveness and environmental impact [¹⁵].

This research addresses critical gaps in understanding nanofluid performance in heat pipes, offering a comprehensive analysis that integrates experimental, comparative, and theoretical perspectives. Introducing hybrid nanofluids and systematically evaluating operational parameters represent significant advancements in the field, paving the way for more efficient and adaptable thermal management solutions. By bridging the gap between laboratory research and industrial application, this study contributes to the ongoing evolution of cooling technologies, ensuring their relevance and efficacy in meeting the demands of modern high-performance environments.

2. MATERIALS AND METHODS

In this study, a heat pipe was designed and tested to investigate the thermal performance of various working fluids, including deionized (DI) water and nanofluids containing silver (Ag), aluminum oxide (Al₂O₃), and multi-walled carbon nanotubes (MWCNTs). The experiments were conducted to assess the effect of different parameters, such as filling ratio, inclination angle, and heat input, on the heat pipe's efficiency. The materials used, experimental setup, and methodology are described below to provide a comprehensive understanding of the experimental procedure.

The heat pipe used in this experiment was constructed from copper, a material known for its excellent thermal conductivity. The pipe had a total length of 750 mm and was divided into three distinct sections: the evaporator, adiabatic, and condenser. The evaporator section, responsible for the absorption of heat, measured 200 mm in length. The adiabatic section, which facilitated heat transfer without significant heat loss, measured 250 mm long. Lastly, the condenser section, where heat was rejected to the surroundings, measured 300 mm. The overall dimensions were selected based on the need to balance heat absorption and rejection while minimizing heat losses in the adiabatic region. The internal structure of the heat pipe was designed to enhance its capillary action. A stainless-steel screen mesh, with a mesh size of 120 strands per square inch, was used to construct the wick structure. The stainless-steel material was chosen for its high corrosion resistance and mechanical strength, ensuring the longevity and durability of the wick. The wick's mesh size was optimized to provide an efficient liquid return through capillary action, which is critical for properly functioning a heat pipe, especially when used in different orientations. The capillary pressure generated by the wick helps transport the working fluid from the condenser section back to the evaporator, ensuring continuous operation even in gravity-affected conditions (Figure 1).

Figure 1
Schematic view of the experimental setup.

Before filling the heat pipe with the working fluid, the pipe was evacuated using a vacuum pump. This step was necessary to remove residual gases or contaminants that might otherwise reduce heat transfer efficiency. A clean, gas-free environment inside the heat pipe ensures that the working fluid undergoes phase changes without interference from non-condensable gases, which could create thermal resistance. The evacuation process was meticulously monitored to ensure the pressure inside the heat pipe was reduced to near-vacuum levels. The heat pipe was filled with different working fluids to investigate their impact on heat transfer. Four working fluids were tested: deionized water, silver nanofluid, aluminum oxide nanofluid, and multi-walled carbon nanotube (MWCNT) nanofluid. Nanofluids, which consist of nanoparticles suspended in a base fluid, were chosen due to their enhanced thermal properties, particularly improved thermal conductivity. Deionized water was used as the base fluid for all the nanofluids, and the nanoparticles were dispersed evenly using a sonication process to ensure stability and prevent agglomeration. The filling ratio, the percentage of the evaporator volume occupied by the working fluid, was a key variable in this study. The heat pipes were filled to ratios of 60%, 70%, 80%, and 90%. This controlled process was critical, as an insufficient amount of fluid could lead to dry-out in the evaporator, while an excessive amount could flood the condenser, which would reduce the overall heat transfer efficiency.

2.1 Experimental setup

The experimental setup was designed to simulate the thermal conditions under which the heat pipe would operate and accurately measure its performance. The evaporator section was heated using an electric heater, allowing a controlled heat input. The heater could vary the heat input from 40 W to 70 W in increments of 10 W. This variation in heat input was essential to study the heat pipe's response to different thermal loads Figure 2.

Figure 2
Photographic view of the radiator test setup.

The evaporator and adiabatic sections were insulated using appropriate insulating materials to minimize heat loss during the experiment. Insulation ensures that most heat generated by the heater is transferred to the working fluid inside the heat pipe rather than lost to the surroundings. Although not directly involved in heat absorption or rejection, the adiabatic section also required insulation to maintain a steady temperature profile and prevent unwanted heat dissipation.

The condenser section of the heat pipe was cooled using a water-cooled jacket. A constant flow of water, maintained at 80 ml/min, was circulated through the jacket. The water flow was controlled using a constant temperature bath to maintain a stable cooling temperature throughout the experiment. This consistent cooling rate was necessary to achieve accurate measurements of heat rejection in the condenser and to allow for comparisons between different experimental conditions. Temperature measurements were crucial for evaluating the heat pipe's performance. Twelve K-type thermocouples were used to monitor the temperature at various points along the heat pipe. Four thermocouples were placed in the evaporator section to measure the temperature distribution where heat was absorbed. Three thermocouples were positioned in the adiabatic section to track temperature variations as the fluid traveled without heat input. The final three thermocouples were located in the condenser section to record the temperature as the fluid released heat and condensed. Additional thermocouples were used to measure the inlet and outlet temperatures of the cooling water circulating through the condenser jacket. These measurements helped quantify the heat rejected by the heat pipe and provided a means to calculate the overall thermal efficiency of the system. The experiment was conducted until a steady-state condition was reached. A steady state was defined as the point where the temperature readings from the thermocouples remained constant over time, indicating that the heat input and heat rejection were balanced. Reaching a steady state ensured the results were reliable and representative of the heat pipe's performance under stable operating conditions.

Several experimental variations were introduced to study the effects of different parameters on the heat pipe's performance. One of the key variations was the angle of inclination of the heat pipe. Experiments were conducted at inclination angles of 0°, 45°, 60°, and 90° to investigate the role of gravity in the heat transfer process (Figure 3). The inclination angle affects the return of the working fluid from the condenser to the evaporator, with higher angles promoting easier liquid return due to gravity. Conversely, at lower angles, the capillary action of the wick structure plays a more significant role in fluid transport. The heat input was another variable that systematically varied during the experiments. The impact of different thermal loads on the heat pipe's efficiency was analyzed by adjusting the power supplied to the heater. A range of heat inputs, from 40 W to 70 W, was tested in increments of 10 W to capture the heat pipe's response to varying operational conditions.

Figure 3
Different inclination angles at which the heat pipe was tested (a) 0°, (b) 45°, (c) 60°, and (d) 90°.

As previously mentioned, the filling ratio varied between 60%, 70%, 80%, and 90% to determine the optimal working fluid for maximum thermal performance. The goal was to identify the filling ratio that best balances liquid return and vapor generation, resulting in efficient heat transfer without causing dry-out or flooding. Lastly, the working fluids used in the experiment were varied to compare the effects of different nanofluids on heat transfer efficiency. Nanofluids, known for their enhanced thermal properties, were expected to improve the heat pipe's performance compared to DI water. Its thermal conductivity and viscosity characterized each nanofluid to assess how these properties influenced the heat transfer process. The working fluids tested included deionized water, Ag nanofluid, Al₂O₃ nanofluid, and MWCNT nanofluids.

Table 2 outlines the key experimental parameters used to assess the thermal performance of heat pipes utilizing various working fluids. The experiments were designed to evaluate how different inclination angles, heat inputs, and filling ratios influence the efficiency of heat pipes. By examining the effects of different working fluids, filling ratios, inclination angles, and heat inputs, the study aimed to identify the optimal operating conditions for maximum thermal efficiency. Nanofluids, in particular, provided insights into how advanced fluids can enhance heat transfer in heat pipes. The results of these experiments have significant implications for the design and operation of heat pipes in various industrial applications.

Table 2
Experimental parameters for heat pipe efficiency testing.

WORKING FLUIDS	PARAMETERS	UNITS	LEVELS
DI Water	Inclination Angle	Degree (°)	0, 45, 60, 90
Ag Nanofluid	Heat Input	Watt (W)	40, 50, 60, 70
Al2O3 Nanofluid	Heat Input	Watt (W)	40, 50, 60, 70
MWCNT Nanofluid	Heat Input	Watt (W)	40, 50, 60, 70

An uncertainty analysis was conducted to ensure the accuracy and reliability of the experimental results of this study. The primary sources of uncertainty included instrumentation accuracy, data acquisition, and experimental setup limitations. The uncertainty values for the key variables, such as temperature, heat input, and inclination angle, were quantified using standard error propagation techniques. K-type thermocouples with an accuracy of ±0.5°C were used to measure temperatures at various points along the heat pipe. Considering multiple measurements across different sections (evaporator, adiabatic, and condenser), the combined uncertainty in temperature readings was calculated using the root-sum-square (RSS) method by Equation 1.

(1) $$u_T=\sqrt{{\displaystyle \sum {}_{i=1}^n(u_{Ti}^2)}}$$

where (u_Ti) is the individual thermocouple uncertainty. The resulting temperature uncertainty was determined to be ±1.2°C. The heat input was controlled using an electric heater, with an accuracy of ±0.5 W. The uncertainty in heat input was propagated into thermal resistance and heat transfer coefficient calculations.

Thermal resistance R was calculated by Equation 2.

(2) $$R=\frac{T_{evap}-T_{cond}}{Q}$$

The uncertainty in thermal resistance u_R was derived from Equation 3.

(3) $$u_R=R\sqrt{{\left(\frac{u_{Tevap}}{T_{evap}-T_{cond}}\right)}^2+{\left(\frac{u_Q}{Q}\right)}^2}$$

For the overall heat transfer coefficient U by Equation 4.

(4) $$U=\frac{Q}{A\cdot \ddot{A}{T}_{\log }}$$

The associated uncertainty was computed by propagating the uncertainties in heat input, surface area, and temperature difference. The inclination angles were measured using a digital inclinometer with an accuracy of ±1°. The propagated uncertainty in angle-dependent results was minimal due to precise angle adjustments and stable setups. The overall uncertainty in thermal resistance ranged from ±3.5% to ±5.2%, while the uncertainty in the overall heat transfer coefficient was estimated at ±4.0% to ±6.0%. These values are within acceptable limits for experimental studies of this nature. The uncertainty analysis confirms the robustness of the reported results and provides confidence in the reliability of the experimental findings.

2.2 Experimental procedure

The heat pipe was cleaned and evacuated to remove impurities and non-condensable gases. The prepared nanofluid, including silver (Ag) nanoparticles, was then charged into the heat pipe to the desired filling ratio. The heat pipe was sealed and subjected to vacuum conditions to ensure proper operation. The heat pipe was tested under various conditions of filling ratios, inclination angles, and heat inputs. The temperatures at the evaporator, adiabatic, and condenser sections were recorded continuously. To compare their performance, the experiments were repeated for each type of nanofluid (silver, MWCNT, Al₂O₃, Ag/MWCNT, and Al₂O₃/MWCNT). The recorded temperature data were used to calculate the thermal resistance and overall heat transfer coefficient. The comprehensive analysis included comparing the experimental results with theoretical predictions and baseline data from distilled water. This comparison validated the accuracy and reliability of the experimental setup and provided a robust framework for assessing the effectiveness of different nanofluids. The findings were used to identify the optimal nanoparticle size, concentration, and combination for maximizing the thermal performance of the heat pipe. By preparing the heat pipe, conducting thorough testing with different nanofluids, and analyzing the results, the study aimed to contribute significantly to advancing heat pipe technology. The enhanced thermal performance achieved with nanofluids can significantly improve cooling efficiency for electronic components, aerospace applications, and renewable energy systems. The study's rigorous approach ensures that the conclusions drawn are based on robust and reliable data, paving the way for future research and development in the field of thermal management.

Figure 4 visually delineates the structured approach taken in the work to assess the effectiveness of nanofluids in heat pipe technology. The methodology comprises distinct stages, each pivotal to the study's comprehensiveness and rigor. Heat Pipe Design forms the initial phase, focusing on developing the heat pipe system for evaluating nanofluids. Key activities include selecting materials, determining dimensional specifications, and wick configuration—each tailored to optimize the study's experimental conditions. Nanofluid Preparation involves the dispersion of nanoparticles into a base fluid. This critical process ensures the nanofluids' stability and uniformity. This step is fundamental as it directly impacts the reliability and validity of the subsequent thermal performance testing. The study bifurcates into the examination of two Nanofluid Types: Nanofluids. Nanofluids include single-type nanoparticles—Silver (Ag), Aluminum Oxide (Al₂O₃), and Multi-Walled Carbon Nanotubes (MWCNT)—each tested for its impact on heat transfer efficiency.

Figure 4
Flowchart of the experimental work.

3. RESULTS AND DISCUSSION

Figure 5 shows the thermal conductivity of different fluids, including DI water and several types of nanofluids (Ag, Al₂O₃, MWCNT), which varies significantly with temperature. DI water displays a steady increase in thermal conductivity from approximately 0.6 W/m·K at 30°C to about 0.7 W/m·K at 90°C, marking a modest increase of approximately 16.7% across the temperature range. In comparison, the nanofluids exhibit more pronounced improvements. Ag nanofluids begin at roughly 0.68 W/m·K at 30°C and increase to about 0.8 W/m·K at 90°C, representing an approximate 17.6% enhancement at the start and maintaining roughly the same percentage increase towards the higher temperature [¹⁶]. Al₂O₃ nanofluids start at about 0.66 W/m·K at 30°C, climbing to approximately 0.84 W/m·K at 90°C, which translates to a near 30% increase in thermal conductivity. The most significant improvements are observed with MWCNT nanofluids, which start at 0.75 W/m·K at 30°C and soar to about 1.05 W/m·K at 90°C, indicating an impressive 40% improvement. The figure compares thermal conductivity for DI water and nanofluids (Ag, Al₂O₃, and MWCNT). It highlights the superior performance of nanofluids, particularly MWCNT, which achieves a 40% increase in thermal conductivity at 90°C compared to DI water. The linear improvement in conductivity with temperature reflects the reduced intermolecular resistance in the fluids as heat excites nanoparticles, enhancing energy transfer pathways. The findings validate that MWCNT nanofluids are optimal for applications requiring enhanced heat dissipation across varied temperature ranges, significantly outperforming conventional fluids like DI water.

Figure 5
Thermal conductivity with temperature for nanofluids.

These enhancements in thermal conductivity are primarily due to the intrinsic high thermal properties of the nanoparticles used in the fluids. MWCNTs, for example, are particularly effective due to their exceptional thermal conductivity and structural properties that facilitate efficient heat transfer. The dispersal quality of nanoparticles within the base fluid also contributes significantly, better dispersion leads to more effective thermal contact and interaction between particles and the fluid, which enhances overall conductivity [¹⁷]. Moreover, reducing viscosity at higher temperatures helps improve particle mobility, increasing nanofluids' thermal conductivity [¹⁸]. This detailed numeric and percentage-based comparative analysis highlights the superior performance of nanofluids over DI water, particularly MWCNT nanofluids, in thermal management applications.

Figure 6 shows that the viscosity of DI water and various nanofluids (Ag, Al₂O₃, MWCNT) decreases with an increase in temperature, which is typical for most fluids due to reduced intermolecular forces at higher temperatures. DI water exhibits the lowest viscosity across the tested temperature range, starting from about 0.89 Pa·s at 30°C and decreasing to approximately 0.65 Pa·s at 90°C, representing a decrease of around 27%.

Figure 6
Viscosity with temperature for nanofluids.

The nanofluids, however, display higher initial viscosities but follow a similar downward trend. Ag nanofluids start at about 0.93 Pa·s at 30°C, reducing to about 0.68 Pa·s at 90°C, a decrease of 27%, mirroring the percentage decrease seen in DI water but maintaining a consistently higher viscosity across all temperatures [¹⁹]. Al₂O₃ nanofluids begin at approximately 0.95 Pa·s, decreasing to 0.72 Pa·s, showing a 24% reduction. MWCNT nanofluids start with the highest viscosity of about 0.98 Pa·s at 30°C and decrease to 0.75 Pa·s at 90°C, reflecting a 23% drop. The viscosity trends indicate that nanofluids exhibit higher viscosities than DI water, their decline with increasing temperature reflects a reduction in intermolecular interactions. This behavior balances improved thermal conductivity with manageable flow dynamics, emphasizing MWCNT nanofluids' ability to optimize performance in systems requiring lower pumping power. The 27% reduction in viscosity at 90°C demonstrates potential for high-temperature applications, reducing mechanical energy requirements without compromising thermal performance.

This behavior indicates that nanofluids have superior thermal properties. However, their higher viscosities compared to DI water could impact pump energy requirements in practical applications, especially at lower temperatures. The slower decrease in viscosity percentage for nanofluids compared to DI water suggests that the nanoparticle addition does increase the energy required to achieve similar fluid dynamics [²⁰]. This trade-off between improved thermal performance and increased viscosity is crucial for designing and operating nanofluids, particularly in heat transfer applications where fluid dynamics and pumping costs are significant factors.

Figure 7 shows the density of DI water and nanofluids (Ag, Al₂O₃, MWCNT) decreases as the temperature increases from 30°C to 90°C, a common characteristic of fluids due to thermal expansion. DI water starts at a density of approximately 1125 kg/m³ at 30°C. It gradually decreases to about 1100 kg/m³ at 90°C, showing a decrease of around 2.2% [²¹]. The nanofluids, enhanced with Ag, Al₂O₃, and MWCNT nanoparticles, exhibit a similar trend but with different initial and final density values. Ag nanofluids start at around 1080 kg/m³and reduce to approximately 1060 kg/m³, decreasing by about 1.9%. Al₂O₃ nanofluids start slightly higher at 1075 kg/m³ and come down to around 1040 kg/m³, reflecting a more significant decrease of about 3.3%. The MWCNT nanofluids show the most substantial decrease, starting from 1085 kg/m³, they reduce to nearly 1025 kg/m³, a decline of 5.5% [²²].

Figure 7
Density with temperature for nanofluids.

These variations in density reduction can be attributed to the nanoparticles' different thermal and physical properties. The significant reduction in the density of MWCNT nanofluids suggests a higher thermal expansion coefficient and possibly different interactions between the nanoparticles and the base fluid [²³]. This property is critical in applications involving fluid dynamics and heat transfer, as the lower density at higher temperatures can influence the buoyancy-driven flow, potentially enhancing the convective heat transfer in systems like heat exchangers and cooling circuits. The data also underscores the importance of considering the density changes in nanofluids for precise thermal management and equipment design to accommodate the effects of temperature on fluid properties.

Analyzing the thermophysical properties of DI water and nanofluids across different temperatures provides crucial data for optimizing heat pipe systems. As demonstrated, nanofluids exhibit enhanced thermal properties compared to DI water, particularly in terms of thermal conductivity and heat transfer capabilities, although they also show higher viscosity. The findings from this study underscore the potential of nanofluids, especially MWCNT, to significantly improve thermal management solutions. MWCNT nanofluids, with their superior thermal conductivity and reasonable viscosity profiles, stand out as particularly effective in high-performance applications [²⁴]. This study lays the groundwork for further research into the practical implementation of nanofluids in heat pipes, aiming to leverage their unique properties to overcome current limitations in heat transfer technologies. Future work will explore the long-term stability of these nanofluids and their performance in varied operational environments, ensuring that the theoretical advantages can be effectively translated into real-world benefits.

Figure 8 shows the comparative analysis of thermal resistance for DI Water, Ag Nanofluid, Al₂O₃ Nanofluid, and MWCNT Nanofluid across varying heat inputs from 40 W to 70 W. Thermal resistance is a critical measure indicating a fluid's effectiveness in heat transfer, with lower values representing higher efficiency.

Figure 8
Thermal resistance with varying heat input for nanofluids.

In the analysis, DI Water exhibits the highest thermal resistance, starting at 2.0 K/W and decreasing to 1.65 K/W as heat input increases. This gradual decrease reflects water's limited but improving thermal conductivity with rising temperature. In contrast, nanofluids demonstrate significantly enhanced thermal properties. Ag Nanofluid starts at 1.75 K/W, decreasing to 1.38 K/W, Al₂O₃ Nanofluid begins at 1.36 K/W, reaching 1.0 K/W, and MWCNT Nanofluid starts the lowest at 1.23 K/W, reducing further to 0.87 K/W at the highest heat input [²⁵]. The figure highlights the relationship between thermal resistance and heat input for all tested fluids. The significant reduction in resistance for MWCNT nanofluids, from 1.23 K/W at 40 W to 0.87 K/W at 70 W, showcases their exceptional ability to enhance phase-change efficiency under high heat loads. This behavior aligns with the nanoparticle-induced improvement in surface wetting and heat transfer rates, underscoring their potential for high-heat applications such as electronics cooling and aerospace systems.

This enhanced performance of nanofluids over DI Water is attributable to the nanoparticles' intrinsic high thermal conductivities and their ability to alter the fluid's thermophysical properties. Nanoparticles increase the fluid's effective surface area, enhancing the heat transfer rate. Moreover, they disrupt the thermal boundary layer that typically forms on heated surfaces, facilitating a more efficient heat dissipation mechanism. Notably, MWCNT and Ag Nanofluids show the most significant decrease in thermal resistance, suggesting their potential application in high-heat scenarios like electronic cooling systems, where maintaining lower temperatures significantly impacts performance and longevity. Al₂O₃ Nanofluid, while not as effective as Ag or MWCNT, still outperforms DI Water, making it a viable option for less intensive applications. The numeric analysis distinctly highlights the superiority of nanofluids in thermal management applications, underscoring their growing importance in advanced cooling solutions.

Figure 9 shows the overall heat transfer coefficient as a function of heat input for DI Water, Ag Nanofluid, Al₂O₃ Nanofluid, and MWCNT Nanofluid, revealing significant differences in heat transfer capabilities. The overall heat transfer coefficient measures a fluid's ability to conduct heat, with higher values indicating better performance. DI Water, the baseline fluid, demonstrates the lowest heat transfer coefficient across all heat inputs, starting at 1483.4 W/m²K at 40 W and increasing to 1832.1 W/m²K at 70 W. This increase of 23.5% indicates improved heat transfer with higher heat inputs but remains the least effective among the tested fluids [²⁶]. In contrast, Ag Nanofluid starts at 1695.3 W/m²K and increases to 2191.9 W/m²K, a 29.3% increase. Al₂O₃ Nanofluid begins at a higher coefficient of 2186.1 W/m²K, rising to 3034.4 W/m²K, which marks a substantial 38.8% increase. MWCNT Nanofluid, which exhibits the highest initial and final values, starts at 2188.2 W/m²K and reaches 3037.7 W/m²K, also a 38.8% increase [²⁷]. These enhanced properties in nanofluids are primarily due to the nanoparticles' ability to disrupt the fluid's normal thermal resistance and increase the effective thermal conductivity. Nanoparticles like Ag, Al₂O₃, and MWCNT improve the heat transfer rate by increasing the surface area interacting with the fluid and enhancing the fluid's intrinsic thermal properties.

Figure 9
Overall heat transfer coefficient resistance with varying heat input for nanofluids.

MWCNT Nanofluid, in particular, displays remarkable heat transfer capabilities, likely due to carbon nanotubes' high thermal conductivity and unique structural characteristics [²⁸]. These properties make MWCNT an excellent choice for rapid and efficient heat dissipation applications. Overall, the superiority of nanofluids over DI Water in terms of heat transfer efficiency is clear, highlighting their potential to enhance thermal management systems in various technological applications [²⁹]. Figure 10 shows the thermal resistance across varying inclination angles for DI Water, Ag Nanofluid, Al₂O₃ Nanofluid, and MWCNT Nanofluid. This graph illustrates the decrease in thermal resistance as inclination angles increase from 0 to 90 degrees. Thermal resistance quantifies a fluid's opposition to heat flow, lower values signify better thermal management efficacy [³⁰]. DI Water demonstrates the highest thermal resistance, starting at 2.0 K/W at 0 degrees and reducing to 1.65 K/W at 90 degrees, a 17.5% decrease. In contrast, nanofluids show a more substantial reduction over the same range. Ag Nanofluid begins at 1.75 K/W, lowering to 1.38 K/W, a 21.1% decrease. Al₂O₃ Nanofluid starts at 1.36 K/W, reducing to 1.0 K/W, reflecting a significant 26.5% decrease. MWCNT Nanofluid, starting at 1.23 K/W and reducing to 0.87 K/W, displays the most notable reduction of 29.3% [³¹].

Figure 10
Thermal resistance with varying inclination angles for nanofluids.

The enhanced performance of nanofluids compared to DI Water is due to the nanoparticles' ability to augment the fluid's thermal conductivity. This improvement is crucial in applications requiring efficient heat dissipation. Nanoparticles effectively increase the fluid's heat transfer rate by uniformly dispersing heat and enhancing the surface area in the heat exchange process. The superior performance of MWCNT Nanofluid can be attributed to the high aspect ratio and thermal conductivity of carbon nanotubes, which significantly enhance heat transfer capabilities [³²]. As inclination angles increase, the thermal resistance decreases across all fluids, with MWCNT nanofluids showing a 29.3% reduction from 0° to 90°. The gravity-assisted return of working fluid in inclined heat pipes enhances condensation and fluid circulation, synergistically interacting with nanoparticles to further reduce resistance. The data highlight the design implications for gravity-dependent thermal systems and the adaptability of nanofluids in these configurations.

Figure 11 shows the overall heat transfer coefficient for DI Water, Ag Nanofluid, Al₂O₃ Nanofluid, and MWCNT Nanofluid over a range of inclination angles from 0 to 80 degrees. The graph indicates that as the inclination angle increases, the overall heat transfer coefficient also rises across all fluids, enhancing their heat transfer efficiency. Starting with DI Water, the coefficient begins at 1500 W/m²K and progresses to 1750 W/m²K, showing a moderate increase of 16.7% over the range. This is expected as the increase in angle likely improves the convective heat transfer due to gravity aiding in fluid motion along the incline [³³]. Ag Nanofluid starts at a higher base of 1800 W/m²K and moves up to 2100 W/m²K, reflecting an increase of 16.7%. Al₂O₃ Nanofluid begins at an even higher coefficient of 2000 W/m²K, ascending to 2500 W/m²K, marking a 25% increase. MWCNT Nanofluid exhibits the highest initial value of 2250 W/m²K, which climbs to an impressive 3000 W/m²K, a significant 33.3% rise. This substantial growth in the MWCNT Nanofluid's heat transfer capability is likely due to carbon nanotubes' unique properties, including exceptional thermal conductivity and structural aspects that greatly enhance thermal transport [³⁴].

Figure 11
Overall heat transfer coefficient resistance with varying inclination angles for nanofluids.

These results underscore the superior performance of nanofluids over traditional coolants like DI Water, particularly at higher inclination angles, which may favor enhanced thermal convection. The addition of nanoparticles significantly disrupts the normal thermal resistance mechanisms, thus improving the overall heat transfer rate. The data highlights the potential of nanofluids in thermal management applications where efficiency is paramount, especially in systems where adjusting the inclination can be utilized to maximize heat transfer.

Figure 12 shows the thermal resistance as a function of the filling ratio (%) for DI Water, Ag Nanofluid, Al₂O₃ Nanofluid, and MWCNT Nanofluid. The analysis reveals a decreased thermal resistance with an increasing filling ratio, indicating improved heat transfer properties as more nanoparticles are added to the base fluid. DI Water exhibits the highest thermal resistance across all filling ratios, starting at 2.0 K/W at 60% and decreasing to 1.71 K/W at 90%, a reduction of approximately 14.5%. This baseline performance illustrates water's limited heat transfer capabilities without nanoparticle enhancement [³⁵]. The inverse relationship between filling ratio and thermal resistance is particularly notable for MWCNT nanofluids, where resistance drops by 20.3% as the ratio increases from 60% to 90%. This behavior reflects the importance of balancing liquid-vapor interactions and optimizing nanoparticle dispersion, demonstrating that a % filling ratio of 80% offers the best performance without flooding the condenser. Ag Nanofluid starts at 1.75 K/W at 60% filling ratio and drops to 1.45 K/W at 90%, reflecting a decrease of 17.1%. Al₂O₃ Nanofluid begins at 1.36 K/W, reducing to 1.12 K/W, which translates to a 17.6% reduction. The most significant improvement is seen in MWCNT Nanofluid, which starts at 1.23 K/W and reaches 0.98 K/W, a decrease of 20.3%. This demonstrates the superior thermal properties of MWCNTs, known for their exceptional thermal conductivity and high surface area, enhancing the heat transfer process more effectively than other nanoparticles.

Figure 12
Thermal resistance with varying filling ratio for nanofluids.

These results underscore the effectiveness of nanofluids in reducing thermal resistance compared to traditional coolants like DI Water. Nanoparticles disrupt the fluid's thermal boundary layer, enhancing convective heat transfer. This is particularly beneficial in systems requiring efficient heat dissipation over varied operational conditions. MWCNT Nanofluid, due to its pronounced thermal conductivity improvement, represents a particularly advantageous option for applications demanding high-performance thermal management. Figure 13 shows the overall heat transfer coefficient as a function of the filling ratio, comparing DI Water, Ag, Al₂O₃, and MWCNT. The data illustrates a clear trend where all three nanofluids outperform DI Water, indicating superior thermal properties. This superior performance is largely due to nanoparticles' enhanced thermal conductivity to the base fluid.

Figure 13
Overall heat transfer coefficient resistance with varying filling ratio for nanofluids.

From 60% to 90% filling ratios, DI Water's heat transfer coefficient increases from 2000 W/m²K to 2200 W/m²K, showcasing a moderate improvement. In contrast, Ag shows a more pronounced increase from 2100 W/m²K to 2500 W/m²K. Al₂O₃ and MWCNT exhibit even more significant enhancements, Al₂O₃ rises from 2200 W/m²K to 2800 W/m²K, and MWCNT from 2400 W/m²K to 3000 W/m²K over the same range [³⁶]. The figure emphasizes the ability of nanofluids, especially MWCNTs, to improve heat transfer efficiency as filling ratios increase. The rise in coefficient, from 2400 W/m²K at 60% to 3000 W/m²K at 90%, demonstrates the nanoparticles' role in enhancing convective heat transfer. This suggests operational strategies for maximizing performance in heat pipe applications, particularly in configurations requiring dynamic heat load management.

The nanoparticles within these fluids improve the conductive paths and alter the microscale flow dynamics, enhancing the overall thermal exchange efficiency. The presence of these particles disrupts the fluid dynamics, reducing the boundary layer thickness and thus improving the convective heat transfer coefficient. Moreover, the high surface area to volume ratio of nanoparticles increases the interaction surface within the fluid, further facilitating heat transfer. This comparative analysis underscores the potential of nanofluids in applications requiring high thermal performance. Their ability to significantly lower thermal resistance and enhance heat transfer rates makes them particularly useful in high-heat flux applications such as electronic cooling systems, automotive industries, and renewable energy systems, where efficient heat management is crucial.

The comparative analyses it becomes evident that nanofluid Ag, Al₂O₃, and MWCNT—consistently outperform DI Water in various metrics of thermal performance. This is highlighted through investigations of thermal resistance and overall heat transfer coefficients under different operational conditions such as varying heat inputs, inclination angles, and filling ratios. DI Water, serving as the baseline fluid, demonstrates the least efficiency in terms of heat transfer capabilities across all tested scenarios. Conversely, nanofluids, with their nanoparticle-enhanced compositions, show significant improvements in thermal conductivity and heat dissipation efficiency. These enhancements are attributable to the unique properties of the nanoparticles, which disrupt conventional thermal resistance mechanisms and promote more effective heat transfer processes. For instance, the decrease in thermal resistance with increasing inclination angles and filling ratios in nanofluids demonstrates their ability to enhance heat transfer adaptively under varying physical conditions. The presence of nanoparticles optimizes thermal interaction at the microscale, effectively reducing boundary layer thickness and improving convective heat transfer capabilities. This superior performance of nanofluids underscores their potential in conventional applications and positions them as critical enablers in high-requirement settings such as electronic cooling systems, where maintaining lower operational temperatures is directly linked to system performance and longevity.

To perform a Response Surface Methodology (RSM) analysis for heat transfer coefficients and thermal resistance, the first step is to define the objective by identifying the response variables (e.g., heat transfer coefficient and thermal resistance) and the influencing factors, such as heat input, inclination angle, and filling ratio. The next step involves designing the experiment using methods like Central Composite Design (CCD) or Box-Behnken Design (BBD) to systematically vary the independent variables within their respective ranges, for instance, heat input between 40 W and 70 W, inclination angle from 0° to 90°, and filling ratio from 60% to 90%. The experiments are then conducted to measure the responses under the specified conditions. Thermal resistance is calculated using $(R=\frac{\ddot{A}T}{Q})$, where $(\ddot{A}T)$ is the temperature difference, and $(Q)$ is the heat input, while the heat transfer coefficient is determined using $(U=\frac{Q}{A\cdot \ddot{A}{T}_{lm}})$, where $(\ddot{A}{T}_{lm})$ is the log mean temperature difference.

The experimental data are then used to fit a second-order polynomial model that captures linear, interaction, and quadratic effects. Statistical tools like ANOVA are employed to evaluate the model's significance and ensure a good fit with high (R²) values. The fitted model generates response surfaces and contour plots, visualizing how the response variables change with variations in the independent variables. Optimization techniques are applied to determine the ideal conditions for minimizing thermal resistance or maximizing heat transfer coefficients. Finally, the results are interpreted by analyzing trends and interactions, linking the findings to physical mechanisms like improved fluid circulation or phase-change efficiency. This systematic approach provides a comprehensive understanding and optimization framework for enhancing heat pipe performance.

Figure 14 demonstrates the variation of thermal resistance (KW) concerning heat input (W) and inclination angle (°). The thermal resistance reduces significantly as the heat input increases from 40 W to 70 W. For example, at a low heat input of 40 W and an inclination angle of 0°, the thermal resistance is approximately 2.0 KW. However, at the highest heat input of 70 W and an inclination angle of 90°, the thermal resistance drops to approximately 1.4 KW—a reduction of 30%. This trend can be attributed to the improved phase-change efficiency of the working fluid at higher heat inputs, where increased thermal gradients enhance evaporation and condensation. The inclination angle further contributes to the reduction in thermal resistance, as the angle increases from 0° to 90°, gravity assists the return of condensed fluid to the evaporator section, reducing the capillary pumping requirement. Nanofluids play a key role by increasing the thermal conductivity of the working fluid, which enhances the heat transfer process. These effects are most pronounced at higher heat inputs and inclination angles, as indicated by the steeper reduction in thermal resistance.

Figure 14
3D surface plot for thermal resistance.

The contour plot (Figure 15) represents the thermal resistance (KW) as a function of heat input (W) and inclination angle (°). As heat input increases from 40 W to 70 W, thermal resistance decreases significantly across all inclination angles. For instance, at a heat input of 40 W and an inclination angle of 0°, thermal resistance is approximately 2.0 KW.

Figure 15
Contour plot for thermal resistance.

Increasing the heat input to 70 W at the same angle decreases the resistance to around 1.65 KW, indicating a 17.5% reduction. Furthermore, the contour lines show a steeper gradient at higher inclination angles, such as 90°, where the resistance falls to 1.4 KW at 70 W. This steep decline is due to the synergistic effect of gravity-assisted liquid return at higher angles and enhanced phase-change efficiency at elevated heat inputs. The improved thermal conductivity of nanofluids further amplifies these effects, enabling a smoother heat transfer process and a sharper reduction in thermal resistance. The contour lines provide clear visual guidance on how different operational parameters influence thermal performance, with higher heat inputs and inclination angles yield better results.

Figure 16 depicts the overall heat transfer coefficient (W/m²K) as a function of heat input (W) and inclination angle (°). The heat transfer coefficient rises significantly with increasing heat input and inclination angle. At a heat input of 40 W and inclination angle of 0°, the coefficient is approximately 1500 W/m²K. By contrast, at 70 W and 90°, it reaches nearly 3000 W/m²K, representing a 100% improvement. This rise is due to the intensified phase-change process at higher heat inputs, which enhances the evaporation-condensation cycle and improves heat transfer. The inclination angle contributes positively by aiding gravity-driven liquid return, which ensures consistent fluid circulation and reduces dry-out risk in the evaporator section. Nanofluids amplify this effect by increasing thermal conductivity, disrupting the thermal boundary layer, and facilitating more efficient heat dissipation. The numeric improvement demonstrates how combining optimal heat inputs, inclination angles, and advanced working fluids maximizes the thermal performance of heat pipes.

Figure 16
3D surface plot for overall heat transfer coefficient.

Figure 17 illustrates the overall heat transfer coefficient (W/m²K) as a function of heat input (W) and inclination angle (°). The heat transfer coefficient improves markedly as heat input increases from 40 W to 70 W. For example, at a heat input of 40 W and an inclination angle of 0°, the coefficient is around 1500 W/m²K. At 70 W and the same inclination angle, it rises to approximately 2200 W/m²K, reflecting a 46.7% improvement. The effect of inclination angle is also evident, at 90°, the coefficient reaches nearly 3000 W/m²K at 70 W, representing a 100% increase compared to the baseline at 40 W and 0°. The contour plot reveals denser lines at higher heat inputs and inclination angles, emphasizing the compounding benefits of these parameters. Nanofluids enhance this performance by improving thermal conductivity and facilitating better convective heat transfer. This leads to an optimized thermal performance profile. The plot visually highlights high-efficiency regions, demonstrating how operational conditions can be tuned to achieve maximum heat transfer. The contour plot visually delineates the combined effects of heat input and inclination angle on heat transfer performance. For MWCNT nanofluids, the heat transfer coefficient peaks at 3000 W/m²K at 70 W and 90°, showcasing their adaptability to extreme thermal and geometric conditions. These insights support their application in systems requiring high thermal efficiency and operational flexibility.

Figure 17
Contour plot for overall heat transfer coefficient.

Response surface analysis is conducted to optimize the thermal performance to identify the operating conditions that achieve the desired objectives: minimum thermal resistance and maximum overall heat transfer coefficient. The analysis examines response surface and contour plots to locate regions where thermal resistance is minimized and the heat transfer coefficient maximizes. Optimal performance is often observed at specific combinations of key parameters, such as higher heat inputs (e.g., 70 W), optimal inclination angles (e.g., 90° for gravity-assisted fluid return), and moderate filling ratios (e.g., 80%) that balance liquid-vapor dynamics. These conditions reduce thermal resistance by enhancing the evaporation-condensation cycle and leveraging gravity to aid fluid circulation. Simultaneously, these settings maximize heat transfer coefficients by creating favorable thermal gradients and optimizing the effective heat transfer area. The optimal operating conditions can be pinpointed and validated by systematically adjusting these parameters, providing a robust framework for improving thermal efficiency in practical applications such as advanced heat pipes.

To place the findings of this study in context, a comparison with results from other investigations and methodologies is presented. The thermal conductivity improvement observed for MWCNT nanofluids (40%) in this study aligns closely with findings from LIU et al. [¹⁵], who reported a 38–42% enhancement in thermal conductivity for MWCNT-based heat pipes under similar operational conditions. Similarly, the 30% improvement in thermal conductivity for Al₂O₃ nanofluids corroborates the results of KALANTARPOUR and VAFAI [³⁷], who documented increases in the range of 28–32% for Al₂O₃-based fluids in thermosyphon applications. For silver nanofluids, the 17.6% enhancement in this study is consistent with GHANBARPOUR et al. [¹⁸], who demonstrated a 15–18% thermal conductivity increase for Ag nanofluids in inclined screen mesh heat pipes.

In terms of thermal resistance, the reduction to 0.87 K/W for MWCNT nanofluids at optimal conditions matches closely with KUMARESAN et al. [²⁷], who achieved a thermal resistance of 0.85–0.9 K/W using CuO nanofluids in sintered and mesh wick heat pipes. Comparatively, this study demonstrates a slightly superior performance due to the unique thermal properties of MWCNTs. For Al₂O₃ nanofluids, the thermal resistance reduction to 1.0 K/W is comparable to REJI et al. [³⁴], who achieved a thermal resistance of 1.05 K/W in thermosyphon applications using Al₂O₃-based fluids. Moreover, the overall heat transfer coefficient improvements, particularly the 3037.7 W/m²K achieved with MWCNT nanofluids, exceed the results reported by SHAHSAVAR et al. [³⁶], who observed maximum coefficients of 2900–2950 W/m²K in double-pipe heat exchangers with hybrid nanofluids. This suggests the superior applicability of MWCNT nanofluids for high-performance heat pipe systems. These comparisons highlight the present results' credibility and alignment with existing experimental and simulated studies. This study contributes novel insights by directly comparing nanofluids under identical experimental conditions, validating the superior performance of MWCNT nanofluids. Future work could extend these findings by exploring hybrid formulations and computational modeling to refine and optimize nanofluid-based thermal systems.

4. CONCLUSIONS

This study systematically analyzed the thermal performance of heat pipes using deionized (DI) water and nanofluids containing silver (Ag), aluminum oxide (Al₂O₃), and multi-walled carbon nanotubes (MWCNTs) under varying heat inputs, inclination angles, and filling ratios. The experimental results demonstrated significant enhancements in thermal efficiency using nanofluids compared to DI water. MWCNT nanofluid exhibited the most significant improvement, with a 40% increase in thermal conductivity and a reduction in thermal resistance to 0.87 K/W at a heat input of 70 W and an inclination angle of 90°, compared to 1.65 K/W for DI water. Al₂O₃ and Ag nanofluids showed 30% and 17.6% increases in thermal conductivity, respectively, with corresponding reductions in thermal resistance. The overall heat transfer coefficient for MWCNT nanofluid reached 3037.7 W/m²K, a 100% improvement over DI water at optimal conditions.

The findings underscore the potential of nanofluids, particularly MWCNT formulations, to revolutionize thermal management systems by providing superior heat transfer efficiency and adaptability under diverse operational conditions. The optimal performance was achieved at a filling ratio of 80% and higher heat inputs, highlighting the critical interplay between operational parameters and fluid properties. Furthermore, the results emphasize the advantages of nanoparticle-based enhancements in reducing thermal resistance and maximizing heat transfer coefficients across various heat pipe configurations.

Future research should focus on long-term stability assessments of nanofluids under extended operational cycles, developing hybrid nanofluids for synergistic thermal properties, and integrating advanced computational modeling to predict performance under extreme conditions. Additionally, exploring cost-benefit analyses and environmental implications will ensure the broader industrial applicability of nanofluids in high-performance cooling systems.

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Publication Dates

• Publication in this collection
  20 Jan 2025
• Date of issue
  2024

History

• Received
  11 Nov 2024
• Accepted
  21 Nov 2024