Thermal Performance Analysis of Nanofluids for Heat Dissipation Based on Fluent

Abstract

With the increasing demand for thermal management in electronic devices and industrial systems, nanofluids have emerged as a research hotspot due to their superior thermal conductivity and heat transfer efficiency. Among them, CuO-H₂O demonstrates excellent heat transfer performance due to its high thermal conductivity, Fe₃O₄-H₂O offers potential for further optimization by combining thermal and magnetic properties, and Al₂O₃-H₂O exhibits strong chemical stability, making it suitable for a wide range of applications. These three nanofluids are representative in terms of particle dispersibility, thermal conductivity, and physical properties, providing a comprehensive perspective on the impact of nanofluids on microchannel heat exchangers. This study investigates the heat transfer performance and flow characteristics of various types and volume fractions of nanofluids in microchannel heat exchangers. The results reveal that with increasing flow rates, the convective heat transfer coefficient and Nusselt number of nanofluids exhibit an approximately linear growth trend, primarily attributed to the turbulence enhancement effect caused by higher flow rates. Among the tested nanofluids, CuO-H₂O demonstrates the best performance, achieving a 4.89% improvement in the heat transfer coefficient and a 1.64% increase in the Nusselt number compared to pure water. Moreover, CuO-H₂O nanofluid significantly reduces wall temperatures, showcasing its superior thermal management capabilities. In comparison, the performance of Al₂O₃-H₂O and Fe₃O₄-H₂O nanofluids is slightly inferior. In terms of flow characteristics, the pressure drop and friction factor of nanofluids exhibit nonlinear variations with increasing flow rates. High-concentration CuO-H₂O nanofluid shows a substantial pressure drop, with an increase of 7.33% compared to pure water, but its friction factor remains relatively low and stabilizes at higher flow rates. Additionally, increasing the nanoparticle volume fraction enhances the convective heat transfer performance; however, excessively high concentrations may suppress heat transfer efficiency due to increased viscosity, leading to a decrease in the Nusselt number. Overall, CuO-H₂O nanofluid exhibits excellent thermal conductivity and flow optimization potential, making it a promising candidate for efficient thermal management in MCHEs. However, its application at high concentrations may face challenges related to increased flow resistance. These findings provide valuable theoretical support and optimization directions for the development of advanced thermal management technologies.

1. Introduction

Energy is the foundation of human development, and more than 70% of the available energy is in the form of heat [1]. As the consumption of energy resources continues to rise, the demand for high-performance thermal exchange materials has become increasingly high [2,3]. The heat exchange efficiency of thermal devices plays a vital role in the energy utilization of the entire system. Micro heat exchangers [4], with their compact structure and large surface density, have significant research value and are widely applied in fields such as microelectromechanical system (MEMS) cooling, large-scale integrated circuit cooling, and spacecraft thermal control [5,6,7]. Improving the efficiency of micro heat exchangers not only involves optimizing the size and internal structure of the heat exchanger but also improving the working fluid [8,9,10], which is one of the key ways to enhance convective heat transfer. Since the introduction of nanofluids by Choi et al. in 1995 [11], they have become a major focus of research due to their excellent heat transfer performance [12,13,14]. The unique heat transfer characteristics of nanofluids demonstrate significant potential in enhancing heat exchange efficiency. The high thermal conductivity of nanoparticles not only improves the overall thermal conductivity of the fluid but also optimizes the flow structure, thereby enhancing convective heat transfer performance. Moreover, nanofluids exhibit remarkable advantages in reducing thermal interfacial resistance, improving heat transfer stability, and resisting deposition. These features are particularly evident under high-temperature and high-flow-rate conditions, offering superior thermal management capabilities. Owing to their tunability and customization, nanofluids showcase strong innovative potential in emerging application areas such as electronic device cooling and solar thermal energy utilization [15,16].

Muhammad et al. [17] experimentally studied the thermal resistance of different concentrations of Al₂O₃-H₂O nanofluids in pipe flow and compared it with pure water. The results indicated that nanofluids have lower thermal resistance than pure water, and higher nanoparticle concentrations reduce thermal resistance. Hamed et al. [18] simulated the effect of CuO nanoparticle concentrations on the flow heat transfer characteristics in a wavy microchannel under different Reynolds numbers. The nanofluid with a volume fraction of 3% showed the best performance. C.J. Ho et al. [19] experimentally studied the flow heat transfer characteristics of Al₂O₃-H₂O nanofluids at different concentrations in microchannels. The addition of nanoparticles reduced thermal resistance by 12.61% and increased the convective heat transfer coefficient by 14.43%. Duygu et al. [20] experimentally investigated the heat transfer characteristics of nanofluids in a heat pipe, showing a 20% reduction in thermal resistance compared to pure water. Turgay [21] used numerical simulations to study the heat transfer characteristics of 4% Al₂O₃-H₂O nanofluid in microchannels, with a significant reduction in peak temperature during the flow process. Ahmad [22] employed both experimental and simulation methods to study the flow heat transfer characteristics of different concentrations and flow rates of Al₂O₃-H₂O nanofluids in two different-shaped microchannels. In both cases, the addition of nanofluids significantly improved the overall performance of the microchannels. Manay [23] conducted experimental research on the mixed convection heat transfer characteristics of ferrite-based (Fe₂O₃-NiO) nanofluids in multi-microchannel heat sinks. The results showed that compared to pure water, the addition of nanoparticles enhanced the natural convection effect, with better natural convection heat transfer at a height of H = 1.8 mm compared to H = 1 mm under the same Grashof number. Zhang et al. [24] primarily investigated the flow and heat transfer characteristics of Cu-H₂O nanofluid and water in microchannels with varying curvatures. The results demonstrated that the heat transfer performance of Cu-H₂O nanofluid outperformed that of pure water across all four types of microchannels. Additionally, both in laminar and turbulent flow regimes, curved microchannels exhibited superior heat transfer performance compared to straight microchannels. The heat transfer performance was found to improve with increases in channel curvature, nanoparticle volume fraction, and particle size. Kevin et al. [25] introduced Fe₃O₄ nanoparticles into the base fluid to enhance the heat transfer coefficient in microchannels and better understand the influence of connectors between two microchannels. It was observed that the connector significantly improved the heat transfer coefficient in the second microchannel by increasing the randomness of molecules and particles before entering the second channel. Under given conditions, the total heat transfer coefficient in both microchannels increased. The study of Reynolds numbers and the introduction of nanoparticles into the base fluid revealed that these two factors play a crucial role in the impact of the connector on the heat transfer coefficient. Guo et al. [26] used the Euler–Lagrange model or the Volume of Fluid (VOF) method to simulate the dynamic behavior in three-phase flows. Their research revealed how the cavitation effect induced by microbubbles enhanced the processing performance and optimized the flow field distribution to improve system efficiency. Tan et al. [27] constructed a CFD model coupling thermodynamic effects and fluid dynamics to accurately simulate cavitation phenomena in cryogenic fluids. Their findings revealed that thermodynamic effects suppress cavitation development, providing theoretical support for optimizing sensor design. These studies offer new insights into the application of CFD in the simulation of multiphase flow and thermodynamic effects. Particularly in the study of microchannel heat exchangers, CFD can simulate the heat transfer characteristics of nanofluids, flow field distribution, and nanoparticle enhancement effects, thereby optimizing microchannel design to improve heat transfer performance.

Existing research primarily focuses on single flow rate or specific flow rate conditions, lacking systematic comparative analysis of the flow and heat transfer characteristics of nanofluids under varying flow rates. Additionally, most studies concentrate on the flow and heat transfer characteristics of a single type of nanofluid, with limited comparative analyses of different nanofluids. This study investigates a disk-spiral micro heat exchanger using three-dimensional numerical simulations based on computational fluid dynamics (CFD). Several commonly used nanofluids with favorable physical properties and well-established preparation processes, including Al₂O₃-H₂O, CuO-H₂O, and Fe₃O₄-H₂O, were employed as cooling media with water as the base fluid. A comparative analysis of the flow and heat transfer characteristics of these nanofluids under different flow rates was conducted, and the effects of different nanofluid concentrations on flow and heat transfer were examined. This research not only deepens the understanding of the flow and heat transfer characteristics of nanofluids but also provides a scientific basis for their optimized application under practical operating conditions, addressing the gaps in existing studies regarding flow rate and multi-type nanofluid comparative analysis.

2. Model Development and Validation

2.1. Physical Model

The simplified structure of the micro heat exchanger is shown in Figure 1a. The main body of the heat exchanger is made of aluminum, while the CPU board is made of copper, with the specific dimensions shown in Figure 1b,c. The fluid inside the flow channels consists of water and nanofluids.

2.2. Mathematical Model

This study conducts simulation analysis of microchannel heat exchanger structures using Fluent to efficiently and accurately model the flow and heat transfer characteristics of microchannel heat exchangers within a reasonable range, providing theoretical support for their optimized design. The following tasks were undertaken.

2.2.1. Assumptions and Scope of the Model

Assumptions: The fluid is treated as a single-phase, steady, incompressible fluid. Gravitational effects are neglected, as is the influence of natural convection and radiation between the wall and the air. Only convective heat transfer between the wall and the fluid is considered. Due to the small dimensions of the flow channels and the nanometer-scale diameter of the solid particles, the working fluid is treated as a continuous phase.

The model is applicable to conditions in microchannel heat exchangers where convective heat transfer dominates, with minimal effects from natural convection and radiation, and where a uniform, single-phase nanofluid is used. The governing equations include the continuity equation, momentum equation, and energy equation. The forms of these equations are as follows:

Continuity Equation:

$$\nabla •\overrightarrow{U}=0$$

(1)

Momentum Equation:

$${\rho }_{\mathrm{f}}(\overrightarrow{U}•\nabla \overrightarrow{U})=-\nabla P+\nabla ({\mu }_f\nabla \overrightarrow{U})$$

(2)

Energy Equation:

$${\rho }_f{c}_{p,f}(U•\nabla T_f)=\nabla ({\lambda }_f\nabla T_f)+\mathsf{\Phi }$$

(3)

In the equations, ρ represents density (kg/m³), λ represents thermal conductivity (W/(m·K)), μ represents viscosity (Pa·s), and c_p represents specific heat capacity (J/(kg·K)). Additionally, U, T, and P represent velocity (m/s), temperature (K), and pressure (Pa), respectively. Φ represents the dissipation function [28,29], and f represents the effective value.

2.2.2. Model Boundary Conditions and Calculation Method Settings

To ensure accurate description of the physical problem, stable and reliable solution processes, and results that align with practical operating conditions, the following settings were made for the model boundary conditions and calculation methods:

(1)

The inlet boundary is set as a velocity inlet with a temperature of 299.5 K;

(2)

The outlet boundary is set with a pressure of standard atmospheric pressure;

(3)

The bottom surface is fixed with a heat flux density of 10,000 W/m², and no-slip boundary conditions are applied to the walls;

(4)

The SIMPLE algorithm is used for solving the equations.

The SIMPLE algorithm is a pressure–velocity coupling method used to solve the governing equations of incompressible fluids. The SIMPLE method links the pressure and velocity fields through an iterative process. The main equations involved in the solution are outlined below.

The momentum equation is solved for each control volume to calculate the velocity components. Taking the x-negative direction as an example, its general form is:

$$\rho {\displaystyle \frac{\partial u}{\partial t}}+\rho (u\cdot \nabla u)=-{\displaystyle \frac{\partial p}{\partial x}}+\nabla \cdot (\mu \nabla u)+S_u$$

(4)

In the equations, u represents the velocity component (m/s), ρ represents the density (kg/m³), p represents the pressure (Pa), μ represents dynamic viscosity (Pa·s), S_u represents the source term (kg/(m·s²)), and similar forms apply for other directions (such as y, z).

To correct the initial pressure estimate, the SIMPLE algorithm introduces a pressure correction equation:

$$\nabla \cdot ({\displaystyle \frac{\nabla p\prime }{a_p}})=\nabla \cdot u^{*}$$

(5)

In the equations, p′ represents the pressure correction (Pa); a_p represents the pressure correction coefficient (Pa·s/m), derived from the discretization of the momentum equation; and u^∗ represents the divergence term of the predicted velocity field (m/s). The pressure correction equation adjusts the pressure by ensuring mass conservation (continuity equation).

The core of the SIMPLE algorithm is to use the continuity equation to ensure that the divergence of the velocity field is zero:

$$\nabla u=0$$

(6)

In the equations, u represents the velocity field (m/s).

By applying the pressure correction p′, the velocity field in the momentum equation is updated:

$$u=u^{*}-{\displaystyle \frac{\nabla p\prime }{a_p}}$$

(7)

In the equations, u′ represents the corrected velocity field (m/s), u^∗ represents the predicted velocity field (m/s), p′ represents the pressure correction (Pa), and a_p represents the pressure correction coefficient (Pa·s/m).

For solving other scalars (such as energy, turbulence models, species concentration, etc.), the corresponding scalar transport equations are also included in the solution:

$${\displaystyle \frac{\partial (\rho \varphi )}{\partial t}}+\nabla \cdot (\rho u\varphi )=\nabla \cdot (\mathsf{\Gamma }\nabla \varphi )+S_{\varphi }$$

(8)

In the equations, ϕ represents the scalar variable (unit of scalar), Γ represents the diffusion coefficient (m²/s), and S_ϕ represents the source term (unit of scalar/(m³·s)).

2.2.3. Calculation of Nanofluid Physical Property Parameters

The physical property parameters of the nanoparticles are shown in

Table 1. Physical property parameters of nanoparticles.

Physical Parameter	Al2O3	CuO	Fe3O4
ρ (kg/m3)	3950	6500	5200
cp (J/(kg·k))	765	540	670
K (w/(m·k))	35	25	9.7

. The nanoparticles are assumed to have a spherical shape, with no agglomeration effects between particles, and both the nanoparticles and base fluid are uniformly distributed with a low fluid volume fraction. The calculation methods for the thermophysical properties of the nanofluid are as follows:

Density Calculation:

$${\rho }_{nf}=\phi {\rho }_p+(1-\phi ){\rho }_f$$

(9)

Thermal Conductivity Calculation:

$${\displaystyle \frac{k_{nf}}{k_f}}={\displaystyle \frac{k_p+2k_f-2\phi (k_f-k_p)}{k_p+2k_f+\phi (k_f-k_p)}}$$

(10)

Specific Heat Capacity Calculation:

$${\rho }_{nf}{c}_{nf}=(1-\phi ){\rho }_f{c}_f+\phi {\rho }_p{c}_p$$

(11)

Viscosity Calculation:

$${\mu }_{nf}={\mu }_f(1+2.5\phi )$$

(12)

In the equations, ρ represents density (kg/m³), k represents thermal conductivity (W/(m·K)), c_p represents specific heat capacity (J/(kg·K)), and φ represents the volume fraction of nanoparticles in the nanofluid. The subscripts nf, p, and f represent the nanofluid, nanoparticles, and base fluid, respectively.

2.3. Model Validation

2.3.1. Subsubsection

To verify that the grid resolution does not significantly affect the results, the micro heat exchanger is divided into five different mesh groups with 647,000; 1,297,000; 2,055,000; 3,351,000; and 4,048,000 cells, as shown in Figure 2. The flow and heat transfer characteristics under an inlet velocity of 0.5 m/s are simulated for each grid. As shown in Figure 3, when the grid number reaches 3,351,000, the pressure drop and outlet temperature of the heat exchanger remain almost unchanged. Therefore, it is concluded that the simulation results are independent of the grid number when the mesh count is 3,351,000. Thus, a grid size of 3,351,000 cells is used for the subsequent analysis in this study.

2.3.2. Model Accuracy Verification

To verify the accuracy of the model, the pressure drop values obtained from the simulations in this study were compared with the pressure drop calculation formula proposed by Steinke et al. [30] The Steinke pressure drop calculation formula is as follows:

$$\mathsf{\Delta }P=f\cdot {\displaystyle \frac{L}{D_h}}\cdot {\displaystyle \frac{\rho u^2}{2}}$$

(13)

In the equation, f represents the friction factor, L represents the length of the microchannel (m), D_h represents the hydraulic diameter of the microchannel (m), ρ represents the fluid density (kg/m³), and u represents the average fluid velocity (m/s).

The calculation results are shown in Figure 4. The maximum error in pressure drop is 4.1%, which is within the acceptable error range for engineering applications, thus demonstrating the accuracy of the computational model.

2.4. Parameter Definitions

To characterize the influence of nanofluids on the flow and heat transfer performance of microchannels, the following parameters are defined for processing the simulation data:

Convective heat transfer coefficient:

$$h={\displaystyle \frac{q{A}_q}{A_c(T_w-T_f)}}$$

(14)

Average Nusselt number:

$$Nu={\displaystyle \frac{h{D}_h}{k}}$$

(15)

In the equations, q is the heat flux density (W/m²); A_q is the heating area (m²); A_c is the heat exchange area (m²), which refers to the contact area between the solid and liquid; T_w is the heat exchanger wall temperature at the contact surface (K); T_f is the fluid temperature (K); and D_h is the characteristic length (m).

3. Results and Analysis

3.1. Effect of Different Types of Nanofluids on Flow and Heat Transfer Performance

Figure 5 reveals the variation in the convective heat transfer coefficient (h) with flow rate for different types of nanofluids. It shows that as the flow rate increases, the convective heat transfer coefficient exhibits an approximately linear increase, with the rate of increase gradually accelerating. As the flow rate rises, the turbulence intensity strengthens, significantly enhancing the thermal convective heat transfer efficiency. Further analysis indicates that the heat transfer coefficients of Al₂O₃-H₂O, CuO-H₂O, and Fe₃O₄-H₂O nanofluids are significantly higher than that of pure water, demonstrating the superior thermal conductivity performance of nanofluids. Specifically, CuO-H₂O nanofluid has the highest heat transfer coefficient, which is about 4.89% higher than that of water as the coolant, highlighting the notable thermal conductivity advantages of CuO nanoparticles. While the performance of Al₂O₃-H₂O and Fe₃O₄-H₂O is similar, they are slightly lower than CuO-H₂O. These results suggest that nanofluids made with nanoparticles possessing higher thermal conductivity can significantly enhance heat transfer performance.

Figure 6 illustrates the trend in Nusselt number (Nu) with a varying flow rate for different nanofluid media. As the flow rate increases, the Nusselt number shows a linear growth trend, with the rate of increase gradually slowing down, demonstrating a consistent pattern. This indicates that an increase in flow rate significantly enhances convective heat transfer efficiency, which is in accordance with the basic principles of thermal convection. In line with the trend in the heat transfer coefficient, CuO-H₂O nanofluid exhibits the highest Nusselt number, which increases of about 1.64% compared to pure water, further confirming its excellent heat transfer enhancement performance. In comparison, Fe₃O₄-H₂O nanofluid shows performance similar but slightly superior to that of pure water. However, the Nusselt number of Al₂O₃-H₂O nanofluid is lower than that of pure water, possibly due to poor dispersion of the nanoparticles, which hinders its expected heat transfer performance. Overall, nanofluids significantly enhance the Nusselt number, primarily due to their excellent thermal conductivity and improved convective heat transfer capabilities, providing strong support for the enhancement of fluid heat transfer performance.

Figure 7 illustrates the gradual decrease in the average wall temperature as the flow rate increases for different types of nanofluids. This phenomenon is attributed to the significant enhancement in heat transfer efficiency with increasing flow rate, which allows the heat at the wall to be more rapidly absorbed by the fluid. In terms of reducing the average wall temperature, the performance of different coolants is ranked as follows: CuO-H₂O > Fe₃O₄-H₂O > Al₂O₃-H₂O > pure water, with CuO-H₂O demonstrating the most significant cooling effect, highlighting its excellent thermal conductivity and convective heat transfer efficiency. Additionally, the graph also indicates that both increasing the flow rate and using nanofluids effectively lower the temperature of the heat exchanger wall, thereby significantly improving the performance of thermal management systems.

Figure 8 reveals that for all types of nanofluids, the pressure drop increases non-linearly with flow rate. Compared to pure water, the pressure drop for nanofluids is notably higher, primarily due to the increase in fluid viscosity caused by the addition of nanoparticles, which enhances flow resistance. Under the same concentration (1.2%), CuO-H₂O exhibits the highest pressure drop, which is approximately 7.33% higher than that of pure water. In contrast, the pressure drops for Fe₃O₄-H₂O and Al₂O₃-H₂O are relatively lower, increasing by about 3.8% and 5.5%, respectively, compared to pure water. This suggests that the density and distribution of CuO nanoparticles have a more significant impact on flow resistance, while the particle characteristics of Al₂O₃-H₂O and Fe₃O₄-H₂O have a relatively smaller effect on fluid viscosity.

Figure 9 illustrates the variation in the frictional resistance coefficient of different types of nanofluids with flow rate. It can be seen that, as the flow rate increases, the frictional resistance coefficient of these nanofluids generally decreases, with the rate of decrease gradually slowing down. Under the same flow conditions, pure water has the highest frictional resistance coefficient as the cooling fluid, while CuO-H₂O nanofluid has the lowest frictional resistance coefficient. Al₂O₃-H₂O and Fe₃O₄-H₂O nanofluids have frictional resistance coefficients that are smaller than water but larger than CuO-H₂O nanofluid. Additionally, as the flow rate increases further, the frictional resistance coefficients of different types of nanofluids tend to converge. Therefore, increasing the flow rate is an effective method for reducing the frictional resistance coefficient.

3.2. Effect of Different Nanoparticle Concentrations on Flow and Heat Transfer Performance

Figure 10 illustrates the convective heat transfer coefficient of nanofluids with different volume fractions as a function of flow velocity, showing a near-linear increase. This phenomenon is primarily attributed to the significant enhancement of turbulence intensity with increased flow velocity, which effectively promotes the convective heat transfer process. From the chart, it can be observed that as the concentration of CuO-H₂O nanoparticles increases, the convective heat transfer coefficient improves significantly. Specifically, the nanofluid with a volume fraction of 3.6% exhibits the highest heat transfer coefficient. Although the heat transfer coefficient of the 1.2% volume fraction nanofluid increases to a lesser extent, its value is still noticeably higher than that of pure water. Under high flow velocity conditions (Q = 3.26 L/min), the convective heat transfer coefficients of the 1.2%, 2.4%, and 3.6% concentrations of CuO-H₂O nanofluid are increased by 4.89%, 9.60%, and 12.91%, respectively, compared to pure water, with the 3.6% concentration showing the most prominent heat transfer enhancement.

Figure 11 presents the variation in the Nusselt number (Nu) for nanofluids with different volume fractions at different flow velocities, revealing an approximately linear increase. Specifically, the Nusselt number of the 1.2% CuO-H₂O nanofluid is consistently higher than that of pure water. However, when the nanoparticle volume fraction is increased to 2.4% and 3.6%, the Nusselt number is lower than that of pure water. This may be due to the higher nanoparticle concentration, which increases the fluid’s viscosity, thereby increasing flow resistance and reducing the flow velocity, which in turn suppresses effective convective heat transfer. Therefore, it can be concluded that increasing the concentration of nanofluids does not always linearly improve heat transfer efficiency; rather, excessively high concentrations may have an adverse effect, leading to a decrease in the Nusselt number.

Figure 12 shows that as the flow velocity increases, the temperature on the heat exchanger wall decreases progressively for nanofluids with different volume fractions. This phenomenon indicates that the increased flow velocity significantly enhances the efficiency of convective heat transfer, thereby improving the thermal performance of the wall and reducing its temperature. Further investigation reveals that as the volume fraction of CuO nanoparticles increases (1.2%, 2.4%, 3.6%), the temperature of the heat exchanger wall decreases even further. Higher CuO concentrations correspond to larger thermal conductivity, which effectively enhances the heat transfer performance of the nanofluid, thereby more significantly reducing the temperature of the heat exchanger wall. Simulation results show that the wall temperature of pure water is consistently higher than that of CuO-H₂O nanofluid. At lower flow velocities, the temperature difference between the wall of the heat exchanger for CuO-H₂O nanofluid and pure water is quite large. However, as the flow velocity increases, the temperature difference gradually diminishes. This could be due to the significantly reduced residence time of the fluid at higher velocities, which, although increasing the heat transfer rate per unit time, reduces the temperature difference (driving force for heat transfer) between the fluid and the wall, thereby limiting further heat exchange and making the temperature reduction effect on the heat exchanger wall less significant.

Figure 13 illustrates that as the flow velocity increases, the pressure drop for nanofluids with different volume fractions gradually increases. The study shows that the higher the concentration of the fluid, the greater the corresponding pressure drop. At the same flow velocity, the pressure drop for CuO-H₂O nanofluid increases significantly with increasing concentration, especially at a 3.6% concentration, where the pressure drop is notably higher than at 1.2% and 2.4% concentration levels. The higher concentration of nanoparticles leads to a significant increase in the fluid’s viscosity, which in turn increases the flow resistance within the pipe. Additionally, the interactions between the particles may alter the microflow characteristics, further intensifying the increase in macroscopic pressure drop.

Figure 14 shows the variation in the friction factor with flow rate for nanofluids at different volume fractions. The friction factor of nanofluids in the microchannel decreases with increasing flow rate, exhibiting a consistent trend, with the rate of decrease gradually slowing. Moreover, a higher nanoparticle volume fraction results in a lower friction factor, with the impact of volume fraction being more significant at lower flow rates.

4. Discussion

This study uses numerical simulation to comparatively analyze the flow and heat transfer performance of three typical nanofluids in a microchannel heat exchanger and investigates the effect of volume fraction on the flow and heat transfer properties of nanofluids. The following conclusions are drawn:

(1)

The effect of flow velocity on heat transfer performance: As flow velocity increases, the convective heat transfer coefficient and Nusselt number for different types of cooling fluids exhibit a linear growth trend, with an accelerating growth rate. This phenomenon can be attributed to the enhanced turbulence intensity caused by higher flow velocities, which thins the thermal boundary layer and promotes energy and momentum mixing. Consequently, the efficiency of convective heat transfer is improved, leading to a rapid increase in both the convective heat transfer coefficient and Nusselt number.

(2)

Comparison of heat transfer performance among different nanofluids: At the same concentration, CuO-H₂O nanofluid demonstrates superior heat transfer performance compared to other cooling fluids, while Al₂O₃-H₂O, due to its relatively poor particle dispersion, fails to surpass pure water in heat transfer effectiveness. The likely reason is that the high thermal conductivity and excellent dispersion of CuO-H₂O contribute to its outstanding performance, whereas the particle agglomeration and poor dispersion of Al₂O₃-H₂O hinder its ability to significantly enhance thermal conductivity.

(3)

Effect of concentration on the performance of nanofluids: With an increase in the volume fraction of CuO-H₂O nanofluid, the convective heat transfer coefficient exhibits an upward trend. At a concentration of 3.6%, the heat transfer efficiency improves by 12.91% compared to pure water. However, at higher concentrations (e.g., 2.4% and 3.6%), a decrease in the Nusselt number is observed. The likely explanation is that at low concentrations, nanoparticles enhance thermal conductivity and heat transfer performance. In contrast, at higher concentrations, the increased viscosity inhibits fluid flow, leading to a reduction in the Nusselt number.

(4)

Effect of flow velocity on pressure drop and frictional resistance: As flow velocity increases, the pressure drop and friction factor of nanofluids are significantly higher than those of pure water, particularly for CuO-H₂O nanofluid, indicating the pronounced impact of particle density and distribution on flow resistance. However, increasing flow rate contributes to a reduction in the friction factor, thereby improving flow performance. This can be attributed to the intensified wall shear stress and resistance caused by higher flow velocities, coupled with the notable influence of nanoparticle density and distribution on flow resistance. At higher flow velocities, the friction factor decreases, enhancing overall flow performance.

(5)

Relationship between wall temperature variation and flow velocity: As flow velocity increases, the wall temperature of the heat exchanger gradually decreases, and a higher CuO concentration significantly lowers the wall temperature, indicating that nanofluids effectively enhance thermal conduction efficiency. However, at excessively high flow velocities, the wall temperature difference tends to stabilize. The likely explanation is that higher flow velocities intensify heat transfer, reducing the wall temperature. However, when the flow velocity becomes too high, the temperature gradient stabilizes, limiting further improvements in heat transfer performance.

The findings of this study are consistent with those of Chao et al. [31], indicating that the properties of nanofluids, including nanoparticle type, dispersion, and concentration, significantly influence both heat transfer and flow performance. CuO nanofluids, due to their superior thermal conductivity and good dispersion, generally exhibit better performance compared to other types of nanofluids. However, at higher concentrations, the viscosity effect becomes a limiting factor, manifesting as a decrease in the Nusselt number and an increase in flow resistance. Therefore, when selecting the optimal nanofluid, a balance must be found between enhancing heat transfer efficiency and controlling flow resistance.

5. Outlook

Although this study has demonstrated the potential of nanofluids in enhancing heat transfer performance, several issues remain to be addressed. First, the dispersion of nanoparticles significantly influences both flow and heat transfer performance. Future research could explore more effective dispersion methods to prevent particle deposition and aggregation. Second, high-concentration nanofluids may result in increased flow resistance and pressure drops. Therefore, identifying an optimal concentration range is essential to balance flow performance with heat transfer efficiency.

Future research should further investigate the behavior of nanofluids under varying operating conditions, such as different temperature and flow velocity ranges. Additionally, practical engineering applications should be considered to optimize heat exchanger designs and nanofluid formulations for improved performance in fields such as electronic cooling and thermal management. Furthermore, the long-term stability, cost, and technical feasibility of nanofluids remain challenges that need to be addressed. Future work should focus on enhancing the overall performance of nanofluids and conducting feasibility studies for their industrial applications.