Numerical Investigation of the Use of a New Nano-Particle in Microchannel


 The purpose of this paper is to study the effects of the use of Boron nitride (BN) as nano-particle on pressure drop and heat transfer in a microchannel. The governing equations for the fluid flow were solved by using Fluent CFD code and artificial neural network (ANN). Computational results acquired from Fluent CFD code and artificial neural network (ANN) for alumina (Al2O3) as nano-particle were compared with numerical values obtained in the literature for validation. On the basis of a water-cooled (only water, water+alumina and water+boron nitride) smooth microchannel were designed, and then the corresponding laminar flow and heat transfer were studied numerically. Results derived from the numerical tests (NT) and artificial neural network (ANN) show good agreement with the values mentioned in the literature and these results also show by the comparison research which was conducted considering the heat transfer and pressure loss parameters between BN and widely used alumina that BN is more convenient nano-particle.


Introduction
Microchannels are uid ow channels with small hydraulic diameters. Following the classi cation by Kandlikar and Grande [1], channels with a minimum cross-sectional dimension between 200 µm and 3 mm are classi ed as minichannels and between 1 µm and 200 µm are classi ed as microchannels. The ow in microchannels has been the topic of increased research interest over the past years. It is seen in many important applications, such as miniature heat exchangers, microscale process units, research nuclear reactors, materials processing and thin lm deposition technology, biotechnology system as well as in potential space application [2]. A lot of research has been done to improve thermal performance on micro systems in literature [3][4][5][6][7]. Kaya [5] researched the effects of rami cation length and angle on pressure drop and heat transfer in a rami ed microchannel. Results derived from the numerical tests indicate that the pressure drop increases with increasing both the rami cation length and angle. Furthermore, the highest temperature inside the rami ed microchannel increases with increasing the rami cation length as well as increasing the ratio volume fraction of ethanol. Zunaid et al. [8] studied the heat transfer and pressure drop characteristics of a straight rectangular and semi cylindrical projections microchannel heat sink. And, they stated that heat transfer increases with the use of semi cylindrical projections microchannel heat sink.
It is hard to cool the heated system with traditional liquids owing to the high power and high integration demand, so researchers concentrated on nano uids. Nano uids are substances gained by suspending solid particles in a uid. These uids are usually waterbased, considering thermal performance.
Sabaghan et al. [9] researched a comprehensive numerical procedure which is based on the two-phase approach to simulate a rectangular microchannel consisting of six longitudinal vortex generators (LVGs), and stated that using the mixture of EG:W (60:40 ethylene glycol and water) instead of pure water as a base-uid leads to the increase of heat transfer in the microchannel. Finally, the maximum normalized e ciency of the LVG-enhanced microchannel, compared to the plain channel, is around 14%. Furthermore, using nano uid can improve the normalized e ciency by 27%.
Abdolahi et al. [10] investigated the uid ow and heat transfer characteristics of laminar nano uid ow in microchannel heat sink (MCHS) with V-Type inlet/outlet arrangement, and showed that nano uid can improve the performance of MCHS with V-shaped inlet/outlet arrangement.
Belhadj et al. [11] researched a numerical study of laminar forced convective ow of nano uid-based water/Al 2 O 3 in a two-dimensional horizontal microchannel heat sink. And, they mentioned that their work contributes to ameliorate the cooling systems by integrating the nano uids in the next generation of microchannels heat sinks.
Shi et al. [12] created a new simulation method which is proposed in their paper to consider that the nano uids thermophysical properties is nonuniform and dynamic in the channels. It was validated by the classical experimental data. The effects of the nanoparticles concentration, Reynolds number and axial thermal conduction effect on the ow and heat transfer characteristics of nano uids in microchannels are analyzed. They said that the proposed numerical simulation method can simulate the forced ow and heat transfer of nano uids in the microchannel more accurately than the traditional single-phase model.
Studies on the effects of Boron minerals as nano uids are very limited in numbers in the literature in spite of the fact that these minerals are signi cant for cooling systems. Furthermore, the presence of new nano uids is considerable to do away with the troubles of cooling systems.
Hou et al. [13] showed a new nano uid called Boron nitride nanosheets considering thermal performance.
In this study, to acquire the effect of the use of Boron nitride as nano-particle in a microchannel on pressure drop and heat transfer, the 3-D numerical simulations by using Fluent CFD code and arti cial neural network (ANN) of the microchannels for different volume fraction of nano uid were assessed. The geometrical dimensions researched in this paper are demonstrated in Ref. Shi et al. [12]. Boron nitride and Alumina as nano-particle are utilized with water as base uid by changing inlet Reynolds numbers. And, which of these two nanoparticles have better properties in terms of pressure drop and heat transfer characteristics was judged.

Numerical model
Governing equations for incompressible, steady state and laminar ow in a microchannel were calculated numerically using Fluent CFD code.
The steady-state conservation of mass, momentum and energy equations can be shown in the following compact form [5]: For the simulations, a uniform heat ux of 200 000 W/cm 2 was applied on the bottom surface of the substrate. The top wall surface of the channels and the outer surfaces of the microchannel were accepted to be insulated. For the ow eld, the velocity applied at the inlet was assumed to be uniform and a pressure condition was applied at the outlet.
The microchannel utilized in the numerical tests (NT) has overall length of 44.8 mm and the hydraulic diameter of 341 μm while other geometric parameters can be found in Shi et at. [12].
The numerical computation was done with a numerical grid shown in Fig. 1, where the white color shows the regions of very frequent mesh.
Different grades of grid re nement were tested. The grid re nement research demonstrated that a total number of about 3399973 elements was su cient to acquire a grid-independent solution. The properties of the nano uid of Alumina and the pure water in detail can be seen in Shi et al. (2018). Some properties of Boron nitride (BN) nano uid such as density, speci c heat and thermal conductivity are 2300 kg/m 3 , 1150 J/kgK and 52 W/mK, respectively.

Arti cial Neural Network Model
In order to model the given governing equations, a neural network model is constructed in MATLAB which is shown in the Fig. 2. This model has ve inputs, one hidden layer with ten neurons and two outputs. Hidden layer neurons has the activation function of hyperbolic tangent sigmoid transfer function.
Training of the neural network is chie y based on two groups of data. Former group is derived from numerical computations. Second collection of data pairs are directly taken from the reference [12].
In MATLAB there is a toolbox named as " tting app" that is used to form and train a neural network model. The obtained results are combined and supported to the tting application as inputs and targets. Seventy percent of this data are used for training, fteen percent are used for validation and the rest are used for testing the trained model. The training algorithm is selected as Levenberg-Marquardt due to its low convergence speed to the best result.
Fitting app exports an input-output static m-le function. Hence, an alternative way of computing equations that governs numerical models without repeating, is obtained. After successful completion of network training same input data are applied to the network and the outputs are obtained.
Neural network outputs are pretty match up with numerical results.

Results And Discussion
In this paper, to obtain the effect of the use of Boron nitride as nano-particle in a microchannel on pressure drop and heat transfer, the 3-D numerical simulations by using Fluent CFD code and arti cial neural network (ANN) of the microchannels for different volume fraction of nano uid were assessed. The geometrical dimensions studied in this paper are shown in Ref. Shi et al. [12]. Boron nitride and Alumina as nano-particle are used with water as base uid by changing inlet Reynolds numbers. Which of these two nanoparticles were considered to have superior properties in terms of pressure drop and heat transfer characteristics was evaluated.
At rst, numerical technique was veri ed with results given in Shi et al. [12]. The inlet temperature of uid had a constant value of 303.15 K for different Reynolds numbers. A uniform heat ux, q=200000 W/cm 2 , was applied to the bottom surface of the microchannel. As can be seen from Fig. 3, the pressure drop increases with the increasing Reynolds numbers, so it can be seen clearly that the consumption of pumping power directly increases. Moreover, the pressure drop also increases owing to the increase of the mixture density with the addition of 1% and 3% Al 2 O 3 as nanoparticle. Results obtained from the numerical tests (NT) and arti cial neural network (ANN) show good agreement with results given in Shi et al. [12]. Fig. 4 gives the comparison of the heat transfer coe cients values for different Reynolds numbers obtained by the numerical tests (NT) and arti cial neural network (ANN) with the results given in Shi et al. [12].
As can be seen from Fig. 4, the heat transfer coe cient increases with the increasing Reynolds number, so it is clear that the thermal performance directly increases. Moreover, the thermal performance also increases due to the improvement of the thermal properties with the addition of 1% and 3% Al 2 O 3 as nano-particle. Results obtained from the numerical tests (NT) and arti cial neural network (ANN) demonstrates good agreement with results given in Shi et al. [12]. Figure 5 gives the Comparison of the pressure contour obtained numerically for pure water with Re=600, 1200 and 1800, respectively.
After these validation studies, the effect of the use of Boron nitride as nano-particle in a microchannel on pressure drop and heat transfer were studied in detail. Fig. 6 shows the relationship between the maximum temperatures of a microchannel for a range of Reynolds numbers. It can be seen that the maximum temperature decreases with the increasing Reynolds numbers since the thermal resistance decreases as the Reynolds numbers increases. This nonlinear trend is in agreement with already published papers in literature [14]. The contours of these temperature are given in Fig. 7.
Comparison of heat transfer coe cient values longitudinal development along microchannel for volume fraction=1% and 3% (for Alumina+water and BN+water ) obtained by numerical tests (NT) and arti cial neural network (ANN), is given Fig. 8.
As can be seen from Fig. 8, the heat transfer coe cient of BN is much higher compared with Al 2 O 3 for same Reynolds numbers, because it is clear that BN compared with Al 2 O 3 has high thermal properties.
Comparison of pressure drop values longitudinal development along microchannel for volume fraction=1% and 3% (for Alumina+water and BN+water ) obtained by numerical tests (NT) and arti cial neural network (ANN), is given in Fig. 9.
As can be seen from Fig. 9, the pressure drop values of BN is slightly lower compared with Al 2 O 3 for same Reynolds numbers, as it is clear that BN compared with Al 2 O 3 has low density.
These results obtained by g. 8 and g. 9 show that the properties of uid pair considerably affect heat transfer and pressure drop. And, it can be concluded that the improvement of the heat transfer and pressure drop in the microchannel relates to the properties of uid pattern.
In cooling systems, the heat transfer properties are desired to be high while the pressure loss are wanted to be minimum. The changing of geometric parameters to increase the heat transfer properties and reduce the pressure loss yields no results because the con gurations which are made to make heat transfer better give rise to pressure loss. In this situation, the best thing to be done is to add new nano uids to the literature which can replace the old nano uids. When the relevant literature is taken into account, it is seen that alumina is the most common used nano uids. It is seen through the comparison research which was carried out considering the heat transfer and pressure loss parameters between BN and widely used alumina that BN is more convenient nano uid. The friction coe cient decreased with the increasing Reynolds numbers, and it was almost constant at high Reynolds numbers (Fig. 10). The reason is that at low Reynolds numbers, viscous effects and friction coe cient of the uid ow are high. Viscous effects decrease with the increasing Reynolds numbers therefore decreasing the effects of surface friction. Comparison of friction coe cients obtained numerically at different Reynolds numbers and the friction coe cient which nally become almost constant.

Conclusions
In this paper, to obtain the effect of the use of Boron nitride as nano-particle in a microchannel on pressure drop and heat transfer, the 3-D numerical simulations by using Fluent CFD code and arti cial neural network (ANN) of the microchannels for different volume fraction of nano uid were assessed. The geometrical dimensions studied in this paper are given in Ref. Shi et al. [12]. Boron nitride and Alumina as nano-particle are used with water as base uid by changing inlet Reynolds numbers. And, which of these two nanoparticles have superior properties in terms of pressure drop and heat transfer characteristics was evaluated.
Results obtained from the numerical tests (NT) and arti cial neural network (ANN) show good agreement with the values given in the literature.
The pressure drop increases with the increasing Reynolds numbers, so it is clear that the consumption of pumping power directly increases. Moreover, the pressure drop also increases due to the increase of the mixture density with the addition of 1% and 3% nano-particle.
The heat transfer coe cient increases with the increasing Reynolds number, so it is clear that the thermal performance directly increases. Moreover, the thermal performance also increases due to the improvement of the thermal properties with the addition of 1% and 3% nano-particle.
The maximum temperature decreases with the increasing Reynolds numbers because the thermal resistance decreases as the Reynolds numbers increases. This nonlinear trend is in agreement with already published work in literature [14].
The properties of uid pair signi cantly affect heat transfer and pressure drop. And, it can be concluded that the improvement of the heat transfer and pressure drop in the microchannel relates to the properties of uid pattern.
It is seen through the comparison research which was done considering the heat transfer and pressure loss parameters between BN and widely used alumina that BN is more convenient nano-particle.
The friction coe cient decreased with the increasing Reynolds numbers, and it was almost constant at high Reynolds numbers. The reason is that at low Reynolds numbers, viscous effects and friction coe cient of the uid ow are high. Viscous effects decrease with the increasing Reynolds numbers decreasing thus the effects of surface friction. Comparison of friction coe cients obtained numerically at different Reynolds numbers and the friction coe cient which nally become almost constant. Figure 1 CFD surface mesh for the microchannels Figure 2 Internal schema of the network.