An Experimental Investigation of the Effects of Using Hexagonal BN–Water Nanofluids on the Thermal Performance and Pressure Drop of a Concentric Tube Heat Exchanger

Abstract

The thermal conductivity of recently produced hexagonal boron nitride (hBN)-containing nanofluids is comparatively higher than their viscosity, indicating that these materials belong to a relatively novel class. In this study, hBN–water nanofluids in stable and dispersed concentrations were used in parallel and counterflow experiments at volumes of 0.01%, 0.1%, and 1%, as well as at various flow rates and Reynolds numbers. When employing hBN–water nanofluid in a counter-flow heat exchanger instead of distilled water, the results showed a 16.7% increase in the overall heat transfer coefficient. The findings also showed that, in comparison to a counter-flow heat exchanger, employing hBN nanofluid as the cold fluid in a parallel-flow heat exchanger produced superior results in terms of an increase in heat transfer. The effects of nanofluid concentration on pressure drops were investigated through experiments conducted in both parallel- and counter-flow conditions.

1. Introduction

Despite the rapidly increasing demand for energy today, the rapid depletion of existing energy sources has motivated scientists to study alternative energy sources and use them more efficiently. Briefly, energy efficiency is the ability to carry out the same or greater amount of work while consuming less energy and maintaining a life standard and production quality and quantity. It is possible to achieve energy efficiency by using the sources more efficiently as well as taking advantage of advanced industrial processes and energy recovery. Increasing heat transfer in heat exchangers means a more economical, efficient, and effective use of energy. With an ever-growing demand for energy and a simultaneous reduction in available energy resources, the importance of increasing heat transfer in heat exchangers has become even more pronounced, according to Wu and Wang [1]. This is because of the increase in life standards and the increasing energy demand of industries in developing countries despite the insufficient resources and the serious economic problems that have occurred in meeting the energy needs of these countries—which have made scientific studies a primary goal [2]. However, suspensions prepared with micro-sized particles usually do not produce the desired results; instead, their properties are less stable and they block microchannels. Nanofluid is not a simple liquid–solid suspension. It should be considered as a stable, long-lasting mixture, where particles are hardly agglomerated. The number of studies on improving thermal performance in the systems using stable nanofluids has increased in recent years [3].

Bianco et al. [4] determined that heat transfer increased with an increasing water-based Al₂O₃ volumetric particle ratio and Re number in turbulent forced convection within a circular tube. Budak Ziyadanogullari et al. [5] compared Al₂O₃, TiO₂, and CuO nanofluids with water at different volumetric flow rates and concentration values in planar solar collectors, and compared heat transfer coefficients using nanofluids prepared at different concentrations (0.1, 2, 4, and 6%). In the study, it was seen that, with the increase in nanofluid volumetric concentration, pressure losses increased by 48%. Buongiorno et al. [6] indicated that nanofluids caused an abnormal increase in convective heat transfer. Moghaddami et al. [7] found that water-based Al₂O₃ nanofluid improved the thermal performance for Re numbers less than 40,000 and that ethylene-glycol-based Al₂O₃ nanofluid improved the thermal performance for Re numbers less than 1100. An experimental study was performed by Prakash et al. [8] in which two turbulators (one was a flat strip and the other one was a twisted strip) were placed into a concentric circular heat exchanger. Sonowane et al. [9] investigated the effects of the nanofluids used in heat pipes on heat transfer. According to Murshed et al. [10], 5 vol% TiO₂ nanoparticles increased the base fluid’s thermal conductivity by nearly 33%. Das et al.’s [11] study looked at the relationship between temperature and thermal conductivity for water as the base fluid and particles.

Wen and Ding [12] determined, as a result of the experiments that they performed with laminar flow tubes, an increase by 41% and 46% in local heat transfer coefficients for Re =  1050 and 1600, respectively, when the volumetric particle ratio of nanoparticles was 0.016. Xuan and Lee [13] demonstrated that heat transfer performance is significantly improved when nanoparticles are added to a working fluid. According to Weimer [14], hBN nanoparticles exhibit good tribological properties. They can effectively lubricate surfaces operating at high temperatures and pressures. Prasad et al. [15] conducted experiments by adding stable and suspended Al₂O₃ nanofluid to a nested pipe heat exchanger to increase heat transfer. The impact of copper–water nanofluids on the system’s pressure drop and heat transfer in nested tube heat exchangers was experimentally investigated by Maghlany et al. [16]. According to the results of the experiment, the rotation that will occur in the inner pipe at a high concentration (3%) increases the amount of heat transfer as it will cause a higher thermal load requirement, thus causing a pressure drop. Sundar et al. [17] conducted experimental studies of different concentrations in the heat exchanger. According to the results of the experiment, they determined that increasing the volumetric concentration improved the thermal properties and that aluminum oxide showed a better nanofluid performance than titanium carbide. Huminic et al. [18] used nanofluids to examine the properties of heat transfer in double-tube helical heat exchangers. According to Kim et al. [19], when the particle size decreases, a nanofluid’s thermal conductivity rises linearly.

The reported thermophysical data provided by several researchers using comparable particles and base fluids lack consistency. These fluctuations can be attributed to various variables, including variations in the size and shape of the particles, the application of surfactants, which leads to changes in the aggregates’ mean size, and the degree of sedimentation. Additional experimental research is required to characterize various nanofluids because it is difficult to understand such differences in the absence of comprehensive knowledge regarding preparation techniques. Due to challenging procedures, increased nanofluid suspension manufacturing poses a serious risk to the usage of nanofluids in larger systems. But, as technology has advanced, producing them has been somewhat simpler for larger systems. To better understand the economic and environmental benefits of employing nanofluids in systems, more research is required.

Among the several polymorphs of BN, the hexagonal form, or hBN, is the softest and resembles graphite in structure. It can be regarded as a material for heat transfer fluids due to its strong in-plane thermal conductivity and chemical inertness. Future thermal engineering applications could benefit from the stable characteristics and enhanced thermal performances of nanofluids made of hBN and water. Although there is currently very little information available in the literature about hBN nanofluids, problems with their stability, thermal conductivity, and rheological behavior have been documented. Lian et al. [20] stated that hexagonal boron nitride (hBN) is in the class of non-oxide ceramics. It has important thermophysical properties, such as low density, low dielectric constant, high thermal conductivity, and excellent chemical stability and thermal resistance. It is a material whose value has increased in recent years and it is used in electronic, optical, energy, and mechanical applications as stated by Shi et al. [21]. In their investigation into the impact of various surfactants on the stability of BN–EG nanofluids, Guo et al. [22] found that the use of PVP as a reagent improved the long-term stability of nanofluids containing surfactant weight fractions of up to 10%. The thermal conductivity ratio and zeta potential variation over time were examined to perform a quantitative assessment of the preparation process and ideal surfactant concentrations. Measurements of the thermal conductivity of nanofluids at different times can be regarded as a useful quantitative indicator of their stability. Ilhan et al. [23] conducted a study on the thermophysical characteristics and enhancement of the heat transfer coefficient in hBN nanofluids. Kurt et al. [24] experimentally studied the rheological and thermal characteristics of hBN–water nanofluids. The performance of a water nanofluid and hexagonal boron nitride (hBN) coolant employed in a PVT collector was numerically examined by Buyukalaca et al. [25] based on a range of input parameters. The flow rate was adjusted between 14.5 and 43.4 L/h, and the solar radiation intensity was varied between 200 and 1000 W/m² to perform numerical analyses. The effect of a water-based hexagonal boron nitride nanofluid (hBN/water) on the thermal performance of a U-tube evacuated tube solar collector was demonstrated experimentally by Kumar and Tiwari [26]. Dispersed and ultrasonicated hBN nanoparticles (50 nm in size) were combined with distilled water to create an hBN–water nanofluid with different volumetric contents (0.25, 0.50, 0.75, 1, 1.25, 1.50, 1.75, and 2 vol%). At temperature ranges of 25, 30, 35, 40, 45, and 50 °C, as well as the above volumetric concentrations, the thermophysical characteristics of the hBN–water nanofluid were examined. The maximum energy efficiency (72.14 percent at 1.5 vol% and 0.051 kg/s) was determined by analyzing the collector’s thermal performance at various mass flow rates (0.0085, 0.01667, 0.0255, 0.034, 0.0425, and 0.051 kg/s). This is roughly 84% greater than water under the same flow conditions. The effects of conventional and lubricant-based hexagonal boron nitride (hBN) nanoparticles on engine wear, performance, and emission parameters of a diesel engine were investigated by Ramteke and Chelladurai [27]. BN-based nanofluids with varying concentrations—0.5 wt.%, 0.75 wt.%, and 1 wt.%—were created. UV spectroscopy and differential scanning calorimetry (DSC) were used to analyze the prepared nanofluids for dispersion and heat capacity evaluations, respectively. Based on the characterization results, 1 wt.% BN nanofluid was determined to be well dispersed and stable, and hence was recommended for additional experimental investigation. The current study’s findings show that a 1 wt.% BN-based nanofluid improved the diesel engine’s performance and emission characteristics while also exhibiting superior anti-wear qualities. The effects of utilizing nanofluids made with hexagonal boron nitride (hBN) nanoparticles on the pressure drop and thermal performance of a concentric tube heat exchanger were examined by Budak Ziyadanogullari and Percin [28]. Water–hBN nanofluids were used in parallel and counter-flow experiments at various flow rates and Reynolds numbers for stable, dispersed volume concentrations of 0.01%, 0.1%, and 1%.

There are many studies on nanofluids in the literature. These studies mostly focus on nanofluids created by the addition of conventional nanoparticles such as CuO, TiO₂, Al₂O₃, etc. However, there are very few studies on nanofluids with the addition of hBN nanoparticles in the literature. The thermal conductivity of hBN–water nanofluids has been observed to grow more quickly than their viscosity, which makes them potential heat transfer fluids for a variety of engineering applications. Consequently, research on the convective heat transfer behavior of hBN nanofluids is somewhat needed. In this study, numerical and experimental research was carried out to eliminate this deficiency.

2. Materials and Methods

Three different concentrations of hBN—0.01%, 0.1%, and 1%—were obtained from a company that specializes in nanofluid synthesis to be employed in the experimental setup’s 50% volume hBN–water mixes. The hBN nanofluid has some superior thermophysical properties, such as high thermal conductivity, excellent lubricating characteristics, superior electrical non-conductivity, and reflection of UV light, as well as being non-toxic and high-temperature-resistant. The thermophysical properties of the samples produced are shown in

Table 1. hBN–water nanofluid and water thermophysical properties.

k (W/mK)	${\mathit{C}}_{\mathit{p}}$ (J/kgK)	ν m2/s	ρ (kg/m3)	Fluid
75	1585	3.97 ×${ 10}^{-7}$	2325	1% hBN–water concentration
70	1597	3.61 ×${ 10}^{-7}$	2300	0.1% hBN–water concentration
62.6	1610	3.52 ×${ 10}^{-7}$	2270	0.01% hBN–water concentration
0.623	4178	0.72 ×${ 10}^{-6}$	1000	Water

.

A basic heat exchanger of the concentric type was used in the test section. Water passes as the hot fluid between the inner and outer tubes of the heat exchanger, while the hBN–water stable suspension nanofluid passes through the inner tube as a cold fluid. Concentric tube heat exchanger dimensions: 1500 mm in length; 12 mm in diameter for the inner pipe made of copper; 33 mm in diameter for the outer pipe made of carbon steel; 2 mm thick inner wall; 4 mm of insulation material covering the test section for preventing heat loss. Two J-type thermocouples with a 1.6 mm diameter were installed at either end of the copper tube to measure the temperature of the nanofluid at both the inlet and the outlet. To circulate the prepared nanofluid throughout the system, first, it was poured into the fluid feeding reservoir and the pump was turned on. By first putting the device into an electromagnetic flow meter and then into the test region—the copper tube inside the concentric tube heat exchanger—the nanofluid flow was determined. Hot water flowed through the circular space between the inner and outer tubes of the heat exchanger while the nanofluid moved via an internal tube. The pressure differential of the nanofluid between the inlet and the outlet was measured using a U manometer, while the inlet, outlet, and wall temperatures were recorded using thermocouples. An experimental setup is shown in Figure 1.

Theoretical analysis equations are given below [3]:

$$C_{nf}=(1-{\varnothing }_v)C_w+{\varnothing }_v{C}_p$$

(1)

$${\rho }_{nf}=\left(1-\varnothing \right){\rho }_f+{\varnothing \rho }_p$$

(2)

$$({C_p)}_{nf}=[\left(1-\varnothing \right)({\rho C_p)}_f+\varnothing ({\rho C_p)}_n]/[\varnothing {\rho }_n+\left(1-\varnothing \right){\rho }_f]$$

(3)

$$Q_a={\dot{m}}_a\ast c_{p,a}\ast \left(Ta,out-Ta,in\right)$$

(4)

$$Q_v={\dot{m}}_v\ast c_{p,v}\ast \left(T_{v,out}-T_{v,in}\right)$$

(5)

Equation (13) was used to calculate the total heat transfer coefficient ($U_{top}$) value. The heat transfer surface area ($A_i)$ in the equation was derived from Equation (9) and the logarithmic temperature difference (${∆T}_{lm})$ was calculated in Equation (8):

$${∆T}_1=T_{h,i}-T_{c,o}$$

(6)

$${∆T}_2=T_{h,o}-T_{c,i}$$

(7)

$${∆T}_{lm}=\frac{{∆T}_2-{∆T}_1}{\mathrm{l}\mathrm{n}(\frac{T_2}{T_1})}$$

(8)

$${\mathrm{A}}_{\mathrm{i}}=\mathsf{\pi }\ast {\mathrm{D}}_{\mathrm{i}}\ast \mathrm{L}$$

(9)

$${ Q}_{ort}=U\ast A_i\ast {∆T}_{lm }$$

(10)

The hydraulic diameter ($D_h$) was calculated in Equation (11).

$$D_h=D_{oi}-D_{io }$$

(11)

$$h_0=\frac{Nu\ast k }{D_h}$$

(12)

The $h_o$ value was calculated in Equation (12). The U value was calculated in Equation (13), while the $h_i$ value was found in Equation (13):

$$\frac{1}{U}=\frac{1}{h_i}+\frac{1}{h_o}$$

(13)

As a result, the experimental Nusselt number ($Nu)$ was found in Equation (14):

$$Nu=\frac{h_i\ast D_{h }}{k}$$

(14)

The flow in the inner pipe varied in the range of 4000 < Re < 150,000:

$${Re}_{water=}\frac{p_{water}\ast V_{water}\ast D_i}{{\mu }_{water}}$$

(15)

$$f={\left[1.58\ast \ln \left(Re\right)-3.82\right]}^{-2}$$

(16)

$$Nu=\frac{\frac{f}{2}\ast \left(Re-1000\right)\ast Pr}{1+12.7\ast ({\frac{f}{2})}^{0.5}\ast ({Pr}^{\frac{2}{3}}-1)}$$

(17)

Load loss ($h_k$) was identified in Equation (18):

$$∆P=f\ast \frac{L}{D}\ast \frac{1}{2}\ast v^2$$

(18)

$$h_K=\left(load loss\right)=\frac{∆P}{pg}=f\ast \frac{L}{D}\ast \frac{{u_m}^2}{2g}$$

(19)

The coefficient of friction was obtained by different formulas at different values of the Reynolds number, and, in the case of 4000 < Re < 20,000, the correlation in Equation (20) was used for the friction factor.

$$\mathrm{f}=\frac{0.3164 }{{Re}^{0.25}}$$

(20)

3. Results

3.1. Heat Transfer Results for Parallel Flow

The experimental findings demonstrated that, in comparison to a counter-flow heat exchanger, the use of hBN–water nanofluid as the cold fluid in a parallel-flow heat exchanger produced superior results in terms of overall heat transfer and an increased Nusselt number. As shown in Figure 2, when hBN was used as the cold fluid rather than distilled water, the change in Nusselt number calculated for different flow rates for the parallel-flow heat exchanger reached its maximum value, with an enhancement pf 10.3%, at the highest Reynolds number (Re = 7534) and at the 1% volumetric hBN–water concentration.

According to the experimental findings, using hBN nanofluid as the cold fluid in a parallel-flow heat exchanger rather than distilled water produced superior outcomes in terms of enhanced overall heat transfer when compared to a counter-flow heat exchanger. Figure 3 illustrates how the use of hBN as the cool fluid increased the overall heat transfer coefficient for the parallel-flow heat exchanger by 19.7%. This improvement was greatest at the highest Reynolds number (Re = 7534).

The overall heat transfer coefficient rises with an increase in the Reynolds number. Furthermore, when the concentration of the nanofluid grew, so did the overall heat transfer coefficient. As can be seen in Figure 4 the highest Reynolds number (Re = 7534) and hBN concentration at volume 1% were associated with the highest overall heat transfer coefficient (U) (1285 W/m²K). The temperature exchange for the distilled water and the different concentrations of hBN nanofluid across the pipe surface were calculated. More heat convection was obtained in parallel flow than in counter flow. As seen in Figure 4, if heat convection increases, surface temperature decreases. Therefore, the surface temperature was higher in counter flow than parallel flow. The highest surface temperature values were for the distilled water.

3.2. Heat Transfer Results for Counter Flow

The experimental findings showed that using hBN nanofluid as the cold fluid in a parallel-flow heat exchanger rather than distilled water produced better results in terms of overall heat transfer and an increase in the Nusselt number. When hBN was employed as the cold fluid and at the highest Reynolds number (Re = 7534), the change in Nusselt number for a counter-flow heat exchanger determined for various flow rates achieved its maximum value, with an increase of 8.1%, at a 1% volumetric hBN–water concentration.

The maximum Reynolds number (Re = 7534) was achieved at the highest Nusselt number, as shown in Figure 5. As concentration and flow increase, the Nusselt number also rises. The experimental results for the counter-flow heat exchanger were assessed by theoretical calculations, as were the analysis results for parallel-flow heat exchangers.

These findings suggest that, in comparison to a counter-flow heat exchanger, using hBN nanofluid as the cold fluid in a parallel-flow heat exchanger produces superior results in terms of enhanced overall heat transfer. Distilled water was not used in this heat exchanger. When hBN was utilized as the cold fluid, the change in the overall heat transfer coefficient for the counter-flow heat exchanger, which was calculated for various flow rates, reached its maximum value at the highest Reynolds number (Re = 7534), with an enhancement of 16.6%.

As can be seen in Figure 6, the highest Reynolds number (Re = 7534) and a volume 1% of hBN–water concentration were associated with the highest overall heat transfer coefficient (U) (983 W/m²K). The collisions and interactions between the nanoparticles were enhanced by an increase in the concentration of nanofluid. As a result, heat transfer from the pipe wall to the nanofluid is accelerated by particle movement near the wall.

The pipe wall surface decreased as the nanofluid concentration rose. This proves that more heat transfer occurs in the fluid when there is a higher concentration of nanoparticles. Figure 7 illustrates how the surface temperature dropped as the flow rate increased. The highest concentration of hBN (1%), however, produced the lowest surface temperature.

3.3. Pressure Drop Results for Parallel Flow

Theoretical computations were used to assess the parallel-flow heat exchanger experiment results. As a result, the counter-flow heat exchanger had a higher head loss (h_k) than the parallel-flow heat exchanger. Head loss (h_k) is a measurement of the extra fluid height that the pump must achieve to remove friction losses in the tube. As a result, the counter-flow heat exchanger had a higher pressure drop than the parallel-flow heat exchanger.

As shown in Figure 8, the friction factor (f) decreases with an increasing flow rate, while it increases with an increasing concentration. At the lowest Reynolds number (Re = 4000), an increase of 9.1% was observed in the hBN–water concentration at a volume of 0.01% compared to distilled water; this increase was 11% at a volume of 0.1% and reached 13% at a volume of 1%. The pressure drop in the system was measured at different flow rates and constant heat flux, and then the head loss (h_k) values were calculated.

Figure 9 displays the head loss results, which show that as the Reynolds number increased, so did the head loss (h_k) values. At a volume of 1% and the highest Reynolds number (Re = 7534), the highest head loss value was found to be 7.8% greater than the value measured for water in the hBN–water concentration. The base fluid’s viscosity and thermal conductivity were both enhanced by the addition of nanoparticles. As a result, the nanofluid’s head loss, pressure drop, and friction factor values increased. These increases were 6.8% and 10.8%, respectively, at the same flow rate when compared to water at a 0.1% and 1% hBN–water concentration.

3.4. Pressure Drop Results for Counter Flow

Similar to the analysis results in parallel-flow heat exchangers, the experimental results for counter-flow heat exchangers were evaluated by theoretical calculations.

Following measurements of the system’s pressure drop at various flow rates and a constant heat flux, friction factor (f) values were computed. Figure 10 displays the results for the friction factor. At the highest Reynolds number (Re = 7534) and a concentration of 1%, the greatest pressure drop was noted. Figure 10 illustrates how the friction factor dropped as the Reynolds number increased. Furthermore, as the flow rate increased, the friction factor decreased, whereas the concentration increased. The hBN–water concentration was found to increase by 12% at the lowest Reynolds number (Re = 4000) when compared to distilled water at a volume of 0.01%; this increase increased to 14.1% at a volume of 0.1% and to 16.5% at a volume of 1%.

After measuring the system’s pressure drop at various flow rates and a constant heat flux, head loss (h_k) values were computed. Figure 11, which displays the head loss results, shows that head loss increased as the Reynolds number increased. At a volume of 1% and the highest Reynolds number (Re = 7534), the highest head loss value was found to be 12% greater than the value measured for water in the hBN–water concentration.

Uncertainty Analysis

Error analysis of experimental data was performed using uncertainty analysis. Volumetric flow, pressure loss, and temperature measurements were all performed for this investigation. Uncertainty estimations were performed using the sensitivity of the measurement equipment (J-type thermocouple, ±0.03 °C; Elimko E-680 Series Universal Dataloggers, ±0.6% °C; Welko LRS40 4S/130, ±0.2 bar). With the numbers derived from these measurements, the Reynolds number (Re), friction factor (f), and Nusselt number (Nu) were computed. With the aid of these data, the parameters’ overall uncertainty values are as follows:

$$\frac{w_{Nu}}{Nu}={[{(\frac{w_h}{h})}^2+{(\frac{w_{d_e}}{d_e})}^2+{(\frac{w_k}{k})}^2]}^{1/2}$$

(21)

$$\frac{w_{\mathrm{R}\mathrm{e}}}{\mathrm{R}\mathrm{e}}={[{(\frac{w_U}{U})}^2+{(\frac{w_{d_e}}{d_e})}^2+{(\frac{w_V}{V})}^2]}^{1/2}$$

(22)

$$\frac{w_f}{f}={[{(\frac{w_{\mathsf{\Delta }P}}{\mathsf{\Delta }P})}^2+{(\frac{w_L}{L})}^2+{(\frac{w_{d_e}}{d_e})}^2+{(\frac{w_p}{p})}^2+{(\frac{w_V}{V})}^2]}^{1/2}$$

(23)

The total uncertainties for the Nusselt number, Reynolds number, and friction factor were found to be ±8.60%, ±9.21%, and ±9.10%, respectively.

4. Conclusions

This work examined the effects of hBN-prepared nanofluids on pressure drop and thermal performance in a concentric tube heat exchanger. In the inner tube, suspended and stable hBN–water nanofluid moved at varying concentrations and flow rates as a cold fluid, while hot fluid moved at varying rates between the inner and outer tubes. Following the experiments, the following summary of the results was obtained:

• When hBN–water was used as the cold fluid at a flow rate of 1.5 L/min, the change in the overall heat transfer coefficient calculated for various flow rates in a parallel-flow heat exchanger reached its maximum value, with an improvement of 19.7%. The improvement for a counter-flow system was 16.7%.
• As the flow rate increases, the friction factor decreases and the head loss increases. Pressure drop increases with an increase in flow rate and concentration.
• The Nusselt number and average heat transfer coefficient at high Reynolds numbers are higher than the values at low Reynolds numbers. As expected in all flow conditions, the friction factor decreased with an increase in the Reynolds number.
• According to the experimental results, head loss and the increases in the friction factor were higher in the counter-flow heat exchanger.
• According to experimental results, it was seen that the increase in head loss and friction factor in counter flow in the heat exchanger was greater than in parallel flow.
• In the opposite flow, the nanofluid’s inlet and outlet temperature differences are smaller than those of the parallel flow, resulting in a lower heat transfer.
• When distilled water and an empty tube were combined, the lowest Nusselt number was achieved at the lowest Reynolds number (Re = 4000). The maximum Nusselt number was recorded at a volume of 1%, Re = 7534.
• In future studies, the thermophysical properties of hexagonal boron nitride can be evaluated more clearly by comparing the performance of hBN nanofluid with other nanofluids by using different nanofluids.