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Thermophysical properties of base fluid, nanoparticles and nanofluids [36].

Table 1 Thermophysical properties of base fluid, nanoparticles and nanofluids [36].

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Grid independent study. Table 2
Grid independent study. Table 2
Fig. 2. Validation of present study with Leng et al. [45] and Ahmed et al. [46] results.
Fig. 2. Validation of present study with Leng et al. [45] and Ahmed et al. [46] results.
Fig. 4. Dimensionless axial velocity lines for Re=50. Fig. 1 shows the investigated geometry. This study aims to simulate heat transfer and nanofluid flow in 2-D microchannels with rectan- gular, trapezoidal and triangular rib shapes. The length and hydraulic diameter of the microchannel are 5 mm and 100 um, respectively. Tor wall of the microchannel is insulated but the mid-section of the bottom wall where the ribs are located, there is under a constant thermal flux equal to G=1000 W/m2. The inlet and outlet insulated length of the bottom wall are equal to 2.5 mm and 1 mm, respectively. Pitch, height and length of the ribs are considered equal to 300, 20 and 100 um. The inlet temperature of working fluid is T.=290 K. It is also assumed that water and the silver nanoparticles are in thermal balance; while nanoparticles are spherical. The flow along the microchannel is types of microchannel. Yang et al. [32] employed bi-phase and mono- phase methods to study the turbulent water-Al,O3 nanofluid flow in a 3-D rectangular ribbed microchannel with grooved ribs. They studied the effect of nanoparticles volume fraction and dimensions of grooved ribs. Their study indicated that two proposed models showed signifi- cant differences. Li et al. [33] numerically studied the laminar heat transfer and fluid flow of water as the cooling fluid in a new design of microchannel with triangular cavities and rectangular ribs. Their results demonstrated that this design of microchannel causes a significant improvement in heat transfer due to a better mixing. Mirzaei and Dehghan [34] carried out a numerical study on water- aluminum oxide nanofluid flow in a microchannel. Their findings indicated that aluminum oxide has a higher friction coefficient compared to pure water. Raisi et al. [35] investigated the laminar flow of water-alumina in a 2-D microchannel. They found that their proposed cooling methods -including forced and natural convection- are effective in cooling a heat source. Also, the effect of Rayleigh number on heat transfer rate is more significant compared to the Reynolds number. In the present study, a rectangular microchannel
Fig. 4. Dimensionless axial velocity lines for Re=50. Fig. 1 shows the investigated geometry. This study aims to simulate heat transfer and nanofluid flow in 2-D microchannels with rectan- gular, trapezoidal and triangular rib shapes. The length and hydraulic diameter of the microchannel are 5 mm and 100 um, respectively. Tor wall of the microchannel is insulated but the mid-section of the bottom wall where the ribs are located, there is under a constant thermal flux equal to G=1000 W/m2. The inlet and outlet insulated length of the bottom wall are equal to 2.5 mm and 1 mm, respectively. Pitch, height and length of the ribs are considered equal to 300, 20 and 100 um. The inlet temperature of working fluid is T.=290 K. It is also assumed that water and the silver nanoparticles are in thermal balance; while nanoparticles are spherical. The flow along the microchannel is types of microchannel. Yang et al. [32] employed bi-phase and mono- phase methods to study the turbulent water-Al,O3 nanofluid flow in a 3-D rectangular ribbed microchannel with grooved ribs. They studied the effect of nanoparticles volume fraction and dimensions of grooved ribs. Their study indicated that two proposed models showed signifi- cant differences. Li et al. [33] numerically studied the laminar heat transfer and fluid flow of water as the cooling fluid in a new design of microchannel with triangular cavities and rectangular ribs. Their results demonstrated that this design of microchannel causes a significant improvement in heat transfer due to a better mixing. Mirzaei and Dehghan [34] carried out a numerical study on water- aluminum oxide nanofluid flow in a microchannel. Their findings indicated that aluminum oxide has a higher friction coefficient compared to pure water. Raisi et al. [35] investigated the laminar flow of water-alumina in a 2-D microchannel. They found that their proposed cooling methods -including forced and natural convection- are effective in cooling a heat source. Also, the effect of Rayleigh number on heat transfer rate is more significant compared to the Reynolds number. In the present study, a rectangular microchannel
Fig. 3. Dimensionless isotherm lines at Re=100.
Fig. 3. Dimensionless isotherm lines at Re=100.
Fig. 5. Local coefficient of friction along the ribbed region of the microchannel.
Fig. 5. Local coefficient of friction along the ribbed region of the microchannel.
The following non-dimensionless parameters have been used fot the dimensionless procedure [37]:
The following non-dimensionless parameters have been used fot the dimensionless procedure [37]:
f, s and nf represent the fluid, solid particles and nanofluids. Patel et al. [41] equation is used to calculate the effective thermal conductivity of suspensions. — 1 . 4 cme ae
f, s and nf represent the fluid, solid particles and nanofluids. Patel et al. [41] equation is used to calculate the effective thermal conductivity of suspensions. — 1 . 4 cme ae
Fig. 7. Pressure loss in different rib forms and 1 < Re < 100.
Fig. 7. Pressure loss in different rib forms and 1 < Re < 100.
Fig. 8. Average coefficient of friction in different rib forms and Reynold numbers.
Fig. 8. Average coefficient of friction in different rib forms and Reynold numbers.
Fig. 9. Different friction coefficients in various Reynold numbers and volume fractions.
Fig. 9. Different friction coefficients in various Reynold numbers and volume fractions.
Fig. 10. Average Nusselt number for different rib forms.
Fig. 10. Average Nusselt number for different rib forms.
with the raise in nanoparticles volume fraction. This happens due to the density augmentation following higher volume fraction. Also, with an increase in nanoparticles volume fraction, the possibility and number of contacts between walls and rib surfaces increase. Analyzing the hydrodynamic behaviors of nanofluids with higher volume fraction demonstrated that nanofluids have more velocity loss in ribs encountering, compared to distilled water which is the main contributory factor in the higher friction coefficient at higher volume fractions of nanoparticles. Furthermore, velocity variations in the ribs corners lead to maximum friction coefficient increment in rectangular and trapezoidal ribs. coefficient of friction is maximum for the rectangular ribs and mini- mum for triangular one, which is because of sudden changes in velocity factors on the peak of the triangle. Before and after the triangular rib and due to the step surface of rib, velocity changes gradually while in rectangular and trapezoidal ribs, these variations happen over a longer surface (the top surface of rib). Fig. 10 indicated average Nusselt number versus rib forms for different Reynolds numbers. The trian- gular form has the highest average Nusselt number. Moreover, the trapezoidal form has higher Nusselt numbers compared to the rectan- gular one. The reason is abrupt variations in velocity profile for triangular compared with others. In all cases, an increase in volume fraction of nanoparticles results in an increase in heat transfer which is due to thermal conductivity enhancement of working fluid compared to
with the raise in nanoparticles volume fraction. This happens due to the density augmentation following higher volume fraction. Also, with an increase in nanoparticles volume fraction, the possibility and number of contacts between walls and rib surfaces increase. Analyzing the hydrodynamic behaviors of nanofluids with higher volume fraction demonstrated that nanofluids have more velocity loss in ribs encountering, compared to distilled water which is the main contributory factor in the higher friction coefficient at higher volume fractions of nanoparticles. Furthermore, velocity variations in the ribs corners lead to maximum friction coefficient increment in rectangular and trapezoidal ribs. coefficient of friction is maximum for the rectangular ribs and mini- mum for triangular one, which is because of sudden changes in velocity factors on the peak of the triangle. Before and after the triangular rib and due to the step surface of rib, velocity changes gradually while in rectangular and trapezoidal ribs, these variations happen over a longer surface (the top surface of rib). Fig. 10 indicated average Nusselt number versus rib forms for different Reynolds numbers. The trian- gular form has the highest average Nusselt number. Moreover, the trapezoidal form has higher Nusselt numbers compared to the rectan- gular one. The reason is abrupt variations in velocity profile for triangular compared with others. In all cases, an increase in volume fraction of nanoparticles results in an increase in heat transfer which is due to thermal conductivity enhancement of working fluid compared to
Fig. 12. Local Nusselt number along the microchannel and different rib forms in p=0.04.
Fig. 12. Local Nusselt number along the microchannel and different rib forms in p=0.04.
Fig. 13. The performance evaluation criteria for different rib forms.
Fig. 13. The performance evaluation criteria for different rib forms.
Fig. 14. Dimensionless temperatures along the microchannel for p=0.04.
Fig. 14. Dimensionless temperatures along the microchannel for p=0.04.
Fig. 15. Dimensionless axial velocity along the microchannel for distilled water.
Fig. 15. Dimensionless axial velocity along the microchannel for distilled water.
Related topics:
NanotechnologyNanoscale heat transferComputational Fluid Dynamics (CFD) modelling and simulationCFD AnalysisWast heat recoveryNanofluid (Heat Transfer)Numerical methods for CFDComputational Fluid Dynamics ( CFD) Modeling and SimulationCFD simulationComputational Fluids Dynamics (CFD)
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