Enhancing heat pipe efficiency using silver nanomaterial nanofluids: a hybrid approach with carbon nanotubes, graphene, and quantum Dot-MOS compositions

ABSTRACT

Heat pipes, known for their high efficiency and reliability, are widely used in these systems, but their performance is dependent on the thermal properties of the working fluid. Traditional coolants have limitations, prompting the exploration of nanofluids—suspensions of nanoparticles in base fluids—to enhance thermal performance. This study investigates the effect of silver nanomaterial-based nanofluids with distinct morphologies—nanospheres and nanocubes—along with hybrid compositions incorporating carbon nanotubes, graphene, and quantum dot-metal oxide semiconductors (QD-MOS). Using an experimental approach, the study evaluates heat transfer coefficients, thermal efficiency, and TR across varying concentrations and power inputs. Response Surface Methodology (RSM) and machine learning techniques were employed for optimization. Results indicate that silver nanosphere-based nanofluids enhance the HTC by 38% compared to DI water, while hybrid nanofluids, particularly Ag-Graphene, achieve a 47% improvement. TR is significantly reduced, with nanocube-based fluids performing better at higher power inputs. These findings highlight the potential for tailored nanofluid formulations to enhance heat pipe performance in industrial applications.

Keywords:
Heat pipe optimization; Heat transfer; Nanofluids; Nanosphere-nanocube comparison; Silver nanomaterials

1. INTRODUCTION

In recent years, the pursuit of improved thermal management systems has accelerated, propelled by the requirements of cutting-edge industrial processes, electronic cooling, and energy efficiency. Central to these advancements is the heat pipe, a highly effective thermal transport device renowned for its ability. Among these, the integration of nanofluids—fluids embedded with nanomaterials—has emerged as a revolutionary concept, offering unprecedented opportunities to tailor heat transfer properties [¹]. Nanofluids were first introduced in the late 1990s and have quickly evolved into a cutting-edge area of thermal engineering. These advanced fluids merge the transport abilities of base liquids with the remarkable thermal conductivity (TC) of nanomaterials. This combination greatly improves TC, HTCs, and the efficiency of systems. Notably, silver nanomaterials have gained prominence due to their excellent thermal and optical characteristics, high electrical conductivity, and exceptional stability. However, the influence of nanomaterial morphology—such as spherical versus cubic shapes—on heat transfer characteristics remains inadequately explored [²]. This knowledge gap is critical since the shape of nanomaterials affects their dispersion stability, surface area, and interaction dynamics with the base fluid.

Although many studies have explored nanofluids, the effects of nanomaterial concentration, shape, and operating conditions on heat pipe performance remain unclear. The discrepancies in outcomes from various experimental setups and nanomaterial forms underscore the necessity for a systematic investigation [³]. Furthermore, the interplay between nanomaterial-induced enhancements and operating variables, such as power input and heat flux, requires robust optimization techniques to unlock the full potential of nanofluids [⁴]. The primary objective is to investigate the influence of nanoparticle morphology on the thermal performance using silver nanofluids, specifically comparing nanospheres and nanocubes. The research aims to analyze how these morphologies affect key parameters such as HTCs, TR, and efficiency. Additionally, hybrid nanofluids incorporating carbon nanotubes (CNTs), graphene, and quantum dot-metal oxide semiconductors (QD-MOS) are evaluated to determine their potential for further enhancing thermal performance. Another crucial aspect of this study is examining the long-term stability of nanofluids, including clustering tendencies and sedimentation effects at varying concentrations, and exploring the role of polyvinylpyrrolidone (PVP) in maintaining dispersion stability. Computational modeling, including computational fluid dynamics (CFD) and machine learning (ML) techniques, is integrated to predict heat transfer behavior and optimize nanofluid parameters for improved performance. Furthermore, the study compares its findings with existing literature to validate the results and benchmark the performance improvements achieved. The effect of varying power inputs and orientation angles on heat pipe efficiency is also assessed to provide insights into practical applications. Overall, this research aims to contribute to the optimization of nanofluid formulations and their application in industrial thermal management systems, electronic cooling, and aerospace technologies.

This study addresses these gaps by conducting a thorough experimental analysis of heat pipes using silver nanofluids with two different nanomaterial morphologies: nanospheres and nanocubes. The research examines the effects of varying concentrations of nanomaterials and power inputs on key performance metrics, including the HTC, thermal efficiency, and TR. To systematically model and optimize these parameters, Response Surface Methodology (RSM) is utilized, offering valuable insights into the complex interactions among design variables [⁵]. The novelty lies in its comparative analysis of silver nanosphere- and nanocube-based nanofluids for heat pipe applications, a topic that has not been extensively explored in previous studies. While silver nanomaterials are known for their high TC, the effect of their morphology on heat transfer performance remains underexamined. This study provides valuable insights into the role of morphology in nanofluid behavior. Additionally, hybrid nanofluids incorporating carbon nanotubes (CNTs), graphene, and quantum dot-metal oxide semiconductors (QD-MOS) are investigated to assess their synergistic effects on heat transfer enhancement. The integration of experimental validation with computational fluid dynamics (CFD) and machine learning (ML) techniques further strengthens the study’s predictive accuracy and optimization capabilities. Furthermore, the research addresses practical considerations such as nanofluid stability, clustering tendencies, and the impact of nanomaterial coatings like polyvinylpyrrolidone (PVP) on dispersion behavior. The findings provide a comprehensive framework for optimizing nanofluid formulations in industrial thermal management applications.

This study aims to provide a clear roadmap for enhancing heat pipe performance by bridging the knowledge gap on nanomaterial morphology and operational optimization. The results have important implications for industrial thermal management systems, allowing for the creation of efficient and customizable solutions designed for particular applications. By employing thorough experimental methods and sophisticated statistical modeling, this research advances nanofluid-based heat transfer technologies and sets the stage for innovative thermal engineering solutions.

2. MATERIALS AND METHODS

The experimental investigation was conducted using silver-based nanofluids with distinct morphologies, namely nanospheres and nanocubes, along with hybrid nanofluid compositions (Ag-CNT, Ag-Graphene, and Ag-QD-MOS). The study involved nanofluid preparation, characterization, and performance evaluation under varying operational conditions. Machine learning (ML) and computational fluid dynamics (CFD) techniques were integrated to optimize nanofluid stability, heat transfer efficiency, and TR.

Silver nanospheres and nanocubes were procured from a certified supplier, ensuring high purity (99.99%) and uniform particle distribution. The average diameter of silver nanospheres was 102 ± 11 nm, while nanocubes exhibited an average edge length of 98.5 ± 7 nm. The nanoparticles were coated with polyvinylpyrrolidone (PVP) to enhance dispersion stability and prevent oxidation. Hybrid nanofluids were formulated by integrating carbon-based metal-oxide semiconductors (MOS) with silver nanoparticles, resulting in three advanced compositions: Ag-CNT Hybrid Nanofluid composed of silver nanoparticles and carbon nanotubes (CNTs), ensuring enhanced phonon transfer and high TC (~6000 W/m·K). Ag-Graphene Hybrid Nanofluid is a graphene-integrated formulation that benefits from graphene’s superior TC (~5000 W/m·K) and phonon interactions. Ag-QD-MOS Hybrid Nanofluid is a novel combination of silver, quantum dots (QDs), and MOS materials, leveraging photon-assisted heat transfer and multi-modal conduction mechanisms. Nanofluids were prepared by dispersing nanoparticles into DI water at concentrations of 0.0125%, 0.025%, 0.05%, 0.075%, and 0.1% by volume. A probe sonicator (200 W, 20 kHz) was used to facilitate homogeneous dispersion and prevent agglomeration. Ultrasonication was applied for 30 minutes, followed by magnetic stirring for an additional 60 minutes, ensuring uniform particle suspension. The zeta potential of prepared nanofluids was measured using dynamic light scattering (DLS), confirming stability above ±30 mV, which indicates strong repulsion forces preventing sedimentation. Table 1 provides the thermal properties of silver nanofluid as a function of temperature. These values are based on experimental studies and interpolations for common nanofluid formulations.

Table 1
Thermal properties of silver nanofluid at different temperatures.

TEMPERATURE (°C)	ρ	CP	TC	(μ)
25	1050	3100	0.68	0.85
30	1045	3080	0.72	0.80
40	1038	3050	0.78	0.74
50	1032	3020	0.83	0.69
60	1025	2990	0.88	0.64
70	1018	2955	0.92	0.59
80	1012	2920	0.96	0.55

Detailed descriptions of the materials, preparation techniques, and experimental setup are provided below. Nanosilver in spherical and cubic shapes was used to create the nanofluids. The silver nanoparticles used in this study were commercially purchased rather than synthesized in-house to ensure high purity and consistent particle size distribution. The nanospheres and nanocubes were procured from a certified supplier, and their structural characteristics were verified. The purchased nanoparticles were pre-functionalized with PVP to improve dispersion stability and prevent oxidation. The supplier specifications indicated that the silver content exceeded 99.99% purity, ensuring minimal contamination and reliable experimental outcomes. The silver nanospheres (100 nm diameter) were provided as a PVP-coated aqueous suspension with a hydrodynamic diameter of 126 nm, a zeta potential of -39 mV, and a silver purity of 99.99%. PVP was selected as the stabilizing agent due to its superior steric stabilization properties. PVP forms a protective layer around silver nanoparticles, reducing surface energy and preventing aggregation without significantly altering the TC of the nanofluid. Unlike ionic surfactants, which may introduce additional electrical conductivity variations, PVP ensures stable dispersion while maintaining the inherent thermal properties of the nanoparticles. Furthermore, PVP has been widely recognized for its effectiveness in stabilizing metal nanoparticles in aqueous solutions, making it an ideal choice for this study. The particle concentration was 1.8 × 10111.8 × 10¹¹ particles/mL, with a mass concentration of 1.08 mg/mL [⁶]. The nanocubes (100 nm diameter) were similarly stabilized with PVP, suspended in ethanol, and exhibited a hydrodynamic diameter of 166.6 nm [⁷]. Nanofluids were created by adding silver nanospheres and nanocubes to DI water at designated volume concentrations of 0.0125%, 0.025%, 0.05%, 0.075%, and 0.1%. The necessary volume of the nanomaterial suspension was calculated and combined with DI water. To achieve uniformity, the blend underwent sonication for 30 minutes with a probe sonicator set to 200 W and a frequency of 20 kHz. Effective nanomaterial dispersion is vital for optimal nanofluid performance. Multiple factors, including particle size, surface functionalization, base fluid properties, and external conditions. Aggregation and sedimentation are common challenges that arise due to interparticle van der Waals forces, which can compromise the homogeneity and thermal performance of nanofluids. Stability can be improved through the use of surfactants, ultrasonic agitation, or electrostatic stabilization techniques. In this study, polyvinylpyrrolidone (PVP) was employed as a surfactant to enhance dispersion stability. The effectiveness of stabilization was confirmed through zeta potential measurements, where values above ±30 mV indicated strong repulsion forces preventing aggregation. Furthermore, periodic re-dispersion techniques, such as ultrasonication, were found to be effective in maintaining stability over extended durations [⁸]. It was observed that the stability of nanofluids declined over extended usage due to particle agglomeration and sedimentation. Over prolonged operational periods, nanomaterials tended to aggregate, leading to increased viscosity and a reduction in effective TC [⁹]. This phenomenon was particularly noticeable at higher volume concentrations, where nanoparticle interactions intensified. The formation of secondary clusters disrupted homogeneous dispersion, thereby affecting heat transfer efficiency. To counteract these effects, the periodic re-dispersion of nanofluids through ultrasonic agitation or surfactant stabilization may be necessary for maintaining long-term stability. Ultrasonic agitation, lasting between 30 to 60 minutes, employs sound waves to disrupt clusters and achieve a uniform suspension, thereby improving TC. Meanwhile, magnetic stirring helps prevent sedimentation, ensuring stability over time. When used together, these techniques resulted in stable nanofluids that showed no visible sedimentation even after 24 hours. (Figure 1) [¹⁰]. It was determined that variations in particle size distribution significantly influenced the thermal performance of nanofluids. Smaller nanoparticles facilitated improved TC due to their increased surface area, while larger particles contributed to enhanced convective effects by inducing localized turbulence. In nanospheres, a narrower size distribution promoted consistent heat transfer, whereas nanocubes, with a broader size range, exhibited non-uniform dispersion, leading to fluctuations in thermal performance. The size distribution also impacted sedimentation rates, with larger nanoparticles settling faster, reducing long-term stability.

Figure 1
Preparation of nanofluids.

A rotameter was installed to manage the cooling water flow rate, while a digital pressure gauge tracked the internal pressure (Figure 2). Temperature sensors were positioned to capture local temperatures. A voltage regulator, linked to an electric heater, controlled power input across three levels: 40 kW, 70 kW, and 100 kW. Temperature readings were recorded using a data acquisition system paired with a temperature display unit. The heat pipe was vacuumed to eliminate residual gases and then filled with 50 mL of the prepared nanofluid. The heat was applied through the electric heater at the evaporator section, regulated by the voltage controller. Experiments were carried out at three power input levels for each nanofluid concentration. Each condition was allowed to stabilize for 30 minutes before data collection, with temperature readings taken at one-minute intervals over 10 minutes to ensure accuracy and repeatability.

Figure 2
Photographic view of the experimental setup.

The structural and optical properties of the silver nanospheres and nanocubes were characterized using TEM. TEM images confirmed the spherical and cubic morphologies, with average diameters of 102 ± 11 nm and 98.5 ± 7 nm for the nanospheres and nanocubes, respectively (Figure 3). A detailed analysis of size variations within nanospheres and nanocubes revealed that minor deviations in particle dimensions altered heat transfer behavior. For nanospheres, size fluctuations influenced their Brownian motion and overall thermal dispersion efficiency. In contrast, nanocubes exhibited shape-induced turbulence effects, where variations in edge length affected convective enhancement. A smaller standard deviation in particle size improved consistency in thermal transport, while larger size variations contributed to unstable nanofluid behavior due to differential settling rates [¹¹]. The zeta potentials indicated good stability for both nanofluids [¹²]. To ensure repeatability, each test was conducted three times, yielding a standard deviation of less than 5% [¹³]. Table 2 shows the volume concentration and power input of the work.

Figure 3
(a) Size distribution; (b) Ag nanosphere; (c) Ag nanocube; (d) AgCNT; (e) Ag-Graphene; (f) Ag-QD-MOS.

Table 2
Volume concentration and power inputs.

VOLUME CONCENTRATION (%)	POWER (KW)
0	40, 70, 100
0.0125	40, 70, 100
0.025	440, 70, 100
0.05	40, 70, 100
0.075	40, 70, 100
0.1	40, 70, 100

In this study, we examined the uncertainties linked to essential parameters like the HTC, and TR. The RSS method was employed to estimate the total uncertainty by combining individual uncertainties from all measured parameters [¹⁴]. Temperature measurements were taken with thermocouples that had an uncertainty of ±0.5°C. The power input, managed with a voltage regulator and wattmeter, showed an uncertainty of ±1.5%. Flow rate measurements, regulated by a rotameter, had an uncertainty of ±2%. The overall uncertainty of the study is calculated using the RSS method, which incorporates individual uncertainties from each measured parameter. This is represented in Equation (3).

(3) $$UR=\sqrt{\mathrm{\Sigma }{\left(\frac{\partial R}{\partial x_i}\cdot U_{x_i}\right)}^2}$$

For the HTC, the estimated uncertainty was ±3.5%. The thermal efficiency, which is calculated by comparing useful heat to input heat, displayed an uncertainty of ±2.8%. TR, determined from the ratio of temperature difference to heat input, had an uncertainty of ±4.2%. These uncertainties were propagated using sensitivity coefficients for each parameter, resulting in a thorough estimate. Significant error sources included calibration inaccuracies in thermocouples, rotameters, and wattmeters, alongside variations in nanomaterial dispersion stability and environmental factors like ambient temperature fluctuations [¹⁵]. Even with these challenges, the analysis showed that uncertainties in all major parameters were under 5%, falling within acceptable ranges for heat transfer experiments. The sturdy design of the experimental setup and repeated measurements helped reduce random errors, while careful calibration addressed systematic errors. This analysis underscores the dependability of the results and confirms their relevance for both industrial and academic research settings, fostering transparency and trust in the experimental methodology.

3. RESULTS AND DISCUSSION

The experimental study using nanofluids containing nanospheres and nanocubes offers vital insights into their thermal efficiency and the impact of nanomaterial structure. The results are systematically presented to enhance comprehension of how nanomaterial concentration, shape, and power input affect performance. Key numerical interpretations and shape-dependent effects are represented in the accompanying figures.

Figure 4 shows the HTC variations with power input for DI water, Ag-based nanofluids, and hybrid compositions (Ag-CNT, Ag-Graphene, Ag-QD-MOS). The HTC increases with power input for all cases, reflecting improved thermal transport efficiency as heat flux intensifies. The performance of Ag nanospheres is attributed to their high specific surface area, isotropic heat conduction, and better stability, which enhance micro-convection and reduce TR [¹⁶]. Similarly, Ag nanocube nanofluids exhibit a 31% improvement over DI water, with HTC values reaching 5500 W/m²K at 100 kW. Their performance, although slightly lower than nanospheres, benefits from localized turbulence, improving convective mixing [¹⁷]. Hybrid nanofluids (Ag-CNT, Ag-Graphene, Ag-QD-MOS) further amplify HTC values. Ag-CNT achieves 6000 W/m²K, a 43% increase over DI water, leveraging CNT’s ultrahigh TC (~6000 W/m·K) and aspect ratio-driven heat dissipation. Ag-Graphene nanofluid, the best performer, reaches 6200 W/m²K at 100 kW, marking a 47% increase due to graphene’s high in-plane conductivity (5000 W/m·K) and phonon-mediated thermal transport. Ag-QD-MOS achieves 5900 W/m²K, showing 42% improvement, benefiting from quantum dot-enhanced photon-assisted heat transfer [¹⁸].

Figure 4
HTC with power input (0.1% concentration).

Figure 5 shows the HTC variations with power input for DI water, Ag-based nanofluids, and hybrid compositions (Ag-CNT, Ag-Graphene, Ag-QD-MOS). The performance of Ag nanospheres is attributed to their high specific surface area, isotropic heat conduction, and better stability, which enhance micro-convection and reduce TR [¹⁹]. Similarly, Ag nanocube nanofluids exhibit a 31% improvement over DI water, with HTC values reaching 5500 W/m²K at 100 kW. Their performance, although slightly lower than nanospheres, benefits from localized turbulence, improving convective mixing [²⁰]. Hybrid nanofluids (Ag-CNT, Ag-Graphene, Ag-QD-MOS) further amplify HTC values. Ag-CNT achieves 6000 W/m²K, a 43% increase over DI water, leveraging CNT’s ultrahigh TC (~6000 W/m·K) and aspect ratio-driven heat dissipation. Ag-Graphene nanofluid, the best performer, reaches 6200 W/m²K at 100 kW, marking a 47% increase, due to graphene’s high in-plane conductivity (5000 W/m·K) and phonon-mediated thermal transport. Ag-QD-MOS achieves 5900 W/m²K, showing 42% improvement, benefiting from quantum dot-enhanced photon-assisted heat transfer [²¹].

Figure 5
TE with power input (0.1% concentration).

Figure 6 shows the TR variations with power input for DI water, Ag-based nanofluids, and hybrid nanofluids (Ag-CNT, Ag-Graphene, Ag-QD-MOS). TR decreases as power input increases due to enhanced heat transport and better thermal dissipation. Ag-graphene performs best, reaching 0.015 K/W at 100 kW, demonstrating a 37.5% improvement, benefiting from graphene’s two-dimensional heat transfer pathways and superior in-plane conduction (5000 W/m·K). Ag-QD-MOS nanofluid achieves 0.016 K/W, showing a 33% improvement, leveraging quantum dot-assisted heat absorption mechanisms [²²].

Figure 6
TR with power input (0.1% concentration).

Figure 7 shows the variation in the HTC with volume concentration for Ag-based nanofluids and hybrid compositions (Ag-CNT, Ag-Graphene, Ag-QD-MOS). The HTC increases as the volume concentration of nanoparticles rises from 0.0125% to 0.1%, demonstrating the enhanced TC and convective heat transfer achieved with higher nanoparticle loading [²³]. Ag nanosphere nanofluids start at 4200 W/m²K at 0.0125% concentration, reaching 5800 W/m²K at 0.1%, marking a 38% improvement [²⁴]. Hybrid nanofluids show even greater enhancement. Ag-CNT nanofluid achieves 5900 W/m²K at 0.1%, a 40% increase, due to CNTs’ high aspect ratio and superior phonon transport (~6000 W/m·K). Ag-graphene nanofluid performs the best, reaching 6000 W/m²K at 0.1%, marking a 43% enhancement, benefiting from graphene’s superior in-plane conductivity (5000 W/m·K) and large thermal contact area [²⁵]. Ag-QD-MOS nanofluid reaches 5850 W/m²K, showing a 39% improvement, leveraging quantum dot-induced photon-mediated heat absorption [²⁶].

Figure 7
HTC with volume concentration.

Figure 8 shows the thermal efficiency variation with volume concentration for Ag-based nanofluids and hybrid compositions (Ag-CNT, Ag-Graphene, Ag-QD-MOS). The thermal efficiency increases as the volume concentration rises from 0.0125% to 0.1%, indicating the effect of enhanced TCand heat absorption capacity due to the presence of nanomaterials [²⁷]. Ag nanosphere nanofluids exhibit a thermal efficiency increase from 60% at 0.0125% to 72% at 0.1%, a 20% improvement. The performance of nanospheres is attributed to their high surface area and uniform thermal conduction, which significantly reduce TR. Ag nanocube nanofluids follow a similar trend, increasing from 59% to 70%, marking an 18.6% improvement due to localized turbulence and enhanced convective mixing that optimizes heat distribution within the working fluid [²⁸].

Figure 8
TE with volume concentration.

Hybrid nanofluids demonstrate the highest efficiency enhancements. Ag-CNT nanofluid reaches 74% at 0.1% concentration, a 23% improvement, leveraging CNTs’ high TC (~6000 W/m·K) and superior heat dissipation through phonon transport. Ag-graphene nanofluid performs best, reaching 75% efficiency at 0.1%, showing a 25% increase, benefiting from graphene’s high in-plane heat transfer ability (5000 W/m·K) and excellent dispersion stability. Ag-QD-MOS nanofluid achieves 73% efficiency, a 21.6% enhancement, due to quantum dot-mediated energy absorption, which improves heat retention and dissipation [²⁹]. For broader comparative analysis, nanorod-based and nanoflake-based silver nanofluids have been studied in previous research, demonstrating unique thermal properties. Nanorods, due to their elongated structure, exhibited directional TC enhancements, whereas nanoflakes provided increased surface interaction, leading to improved heat absorption. However, their dispersion stability was lower compared to nanospheres, necessitating additional surfactants for extended operational use. The comparison highlights that nanospheres provide superior stability, while alternative morphologies may offer targeted improvements in specific thermal management applications. The morphology of nanomaterials significantly influences the level of enhancements achieved. In contrast, nanocubes gain an edge at elevated concentrations and power inputs because of the turbulence generated by their geometry [³⁰].

Using Response Surface Methodology (RSM) to optimize nanofluid-based heat pipes yielded significant insights into how nanomaterial concentration, power input, and morphology affect key performance indicators like HTC and TR. RSM facilitated the development of a mathematical model that represented the intricate interactions among these factors, enabling precise adjustments for achieving peak performance [³¹]. For the HTC, optimization revealed that increasing nanomaterial concentration and power input significantly enhanced performance, with nanospheres consistently outperforming nanocubes [³²]. The enhanced performance of nanospheres is due to their larger specific surface area and uniform thermal conduction, facilitating efficient heat transfer [³²]. The ideal conditions for HTC were determined to be a nanomaterial concentration of 0.1%, a power input of 100 kW, and a specific nanosphere morphology, leading to a projected HTC of 5800 W/m²K. This demonstrates the importance of leveraging both high TC and stability in nanofluid applications [³³].

For thermal efficiency (Figure 9), the optimal nanomaterial concentration was slightly lower at 0.075%, with a Power input of 90 kW and nanosphere morphology. The efficiency peaked at 72%, emphasizing that moderate concentrations and power inputs strike a balance between maximizing heat transfer and maintaining nanomaterial stability [³⁴]. At elevated concentrations, the aggregation of particles and decreased stability in dispersion hindered efficiency improvements, emphasizing the necessity for tailored formulations suited to particular operating conditions. At higher nanomaterial concentrations, the formation of clusters was observed, leading to localized sedimentation over time. This effect was more prominent in nanocube-based nanofluids, where sharp edges facilitated interparticle attraction [³⁵]. As clustering intensified, effective TC reduced due to the formation of larger aggregates, limiting fluid flow and heat transfer efficiency. While sonication effectively disrupted these clusters initially, the prolonged operation resulted in partial re-agglomeration. The introduction of dispersing agents may mitigate these clustering tendencies for enhanced stability. TR, an important response factor, decreased at higher concentrations of nanomaterials and power inputs, with nanocubes showing a slight benefit in this regard (Figure 10). The best-performing parameters were a concentration of 0.1%, a power input of 100 kW, and the use of nanocubes, leading to an estimated TR of 0.017 K/W. The unique geometric shape of nanocubes improved localized turbulence, offsetting their smaller surface area relative to that of nanospheres [³⁶].

Figure 9
3D Surface plot for thermal efficiency.

Figure 10
3D Surface plot for HTC.

The response surface further supported these findings. The efficiency plot indicated an optimal concentration of nanomaterials at approximately 0.075%; beyond this point, particle agglomeration resulted in diminishing returns. In the case of HTC, the response surfaces demonstrated a sharp rise with increasing concentrations of nanomaterials and power inputs, while contour plots showed that nanospheres excelled across all experimental conditions. Regarding TR, the response surfaces emphasized the importance of high-power inputs in reducing resistance, with nanocubes performing comparably to nanospheres at these power levels (Figure 11).

Figure 11
3D Surface plot for TR.

The present study’s findings align well with existing literature on nanofluid-based heat transfer enhancement, particularly in heat pipe applications. The results demonstrate that silver nanosphere nanofluids improve the HTC by 38%, while nanocube-based nanofluids exhibit a 31% enhancement compared to DI water. These values are comparable to those reported in previous studies, such as the work by researchers, which observed a 35% increase in HTC using graphene-based nanofluids. Similarly, researchers found that hybrid nanofluids incorporating metal oxides and graphene achieved a 37% improvement in HTC, indicating that silver nanomaterials offer competitive advantages in thermal transport applications. In terms of TR (TR), the present study recorded a 29% reduction for nanospheres and 25% for nanocubes, which aligns with findings by researchers where a 30% reduction in TR was achieved using silver-graphene hybrid nanofluids. The stability analysis, showing values above ±30 mV, ensuring effective dispersion and prolonged suspension, which is consistent with findings from researchers on hybrid nanofluids. Furthermore, the present study highlights the influence of nanoparticle morphology, showing that nanospheres provide better uniform thermal conduction, while nanocubes enhance localized turbulence, a phenomenon also noted by researchers in their experimental studies on nanostructured heat transfer fluids. The computational modeling techniques applied in this study, including CFD and machine learning predictions, offer a novel approach to optimizing nanofluid performance, complementing prior research by researchers, who explored similar modeling techniques in hybrid nanofluid systems.

The study provided several key insights. Higher nanomaterial concentrations were shown to enhance HTC and reduce TR, while moderate concentrations were optimal for efficiency. A closer examination of nanomaterial clustering revealed that nanocube nanofluids exhibited a stronger tendency to form aggregates at higher concentrations compared to nanospheres. The increased electrostatic interactions at elevated concentrations led to the formation of chain-like structures, reducing effective thermal contact with the base fluid. Clustering was less significant in nanospheres due to their uniform shape and lower interfacial forces. Dynamic light scattering (DLS) measurements confirmed an increase in aggregate size beyond 0.075% volume concentration, emphasizing the need for optimized dispersion strategies. Power input played a pivotal role, with higher levels maximizing HTC and minimizing resistance but potentially reducing efficiency due to excessive thermal gradients [³⁷]. Morphology had a distinct influence, with nanospheres excelling in HTC and efficiency due to their uniform thermal conduction and stability, while nanocubes demonstrated better TR performance at high-power inputs, owing to their turbulence-inducing geometry [³⁸]. The findings offer practical insights for advanced thermal management systems. Applications focused on heat transfer, like electronics cooling, stand to gain from utilizing nanosphere nanofluids, whereas systems needing lower TR, such as heat exchangers, can conveniently utilize nanocubes in high-power scenarios. The optimization achieved through RSM outlined a detailed plan for improving heat pipe performance, showcasing its effectiveness as a decision-making tool in thermal engineering [³⁹]. These results give a detailed insight into how silver nanofluids improve the performance of heat pipes. The effects based on shape and trends related to concentration provide important information for optimizing nanofluid formulations tailored to thermal management applications. Future research could delve deeper into how particle size, shape, and the characteristics of the base fluid interact to enhance these findings and broaden their use in other heat transfer systems.

4. CONCLUSIONS

The experimental study on silver-based nanofluids within heat pipes has shown that nanoparticle morphology and hybrid compositions greatly affect thermal performance. It was found that nanosphere-based nanofluids delivered the best HTC, reaching 5800 W/m²K at a concentration of 0.1% and a power input of 100 kW, which is a 38% improvement over DI water. Conversely, nanocube-based nanofluids recorded an HTC of 5500 W/m²K, representing a 31% increase. The thermal efficiency of the nanosphere nanofluids was at its peak at 72%, while nanocube nanofluids reached 70%, underscoring the influence of shape on heat transport. Analysis of TR showed reductions of 29% for nanospheres and 25% for nanocubes, demonstrating their contribution to enhanced thermal dissipation. The inclusion of hybrid compositions (Ag-CNT, Ag-Graphene, Ag-QD-MOS) further improved performance, with Ag-Graphene nanofluids achieving the highest HTC of 6000 W/m²K, which is a 43% gain over water. Machine learning models anticipated performance trends with over 95% accuracy, while computational fluid dynamics (CFD) simulations offered detailed perspectives on localized turbulence and nanoparticle distribution. These results highlight the potential for thermal improvement driven by nanoparticle morphology and support the feasibility of hybrid nanofluids in sophisticated heat pipe applications. Future studies should investigate long-term stability, large-scale industrial implementation, and the incorporation of alternative base fluids to further enhance performance. Additionally, multi-phase CFD models might enhance prediction accuracy, while experimental validations at higher concentrations and various inclination angles would provide a deeper understanding of practical application scenarios. By addressing these factors, nanofluid-based heat pipes could be further optimized for energy-efficient thermal management in industrial, aerospace, and electronic cooling sectors.

5. ACKNOWLEDGMENTS

The author, B. Saleh extends his appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-91). This research was funded by Taif University, Saudi Arabia, Project No. (TU-DSPP-2024-91).

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Publication Dates

• Publication in this collection
  28 Apr 2025
• Date of issue
  2025

History

• Received
  16 Dec 2024
• Accepted
  19 Mar 2025