Entropy Generation Analysis of a Microchannel Heat Sink with Al2O3 and CNT Nanofluid

Author(s):  
Abhilash K. Tilak ◽  
Ranjit S. Patil
Entropy ◽  
2015 ◽  
Vol 18 (1) ◽  
pp. 10 ◽  
Author(s):  
Mohammad Nasiri ◽  
Mohammad Rashidi ◽  
Giulio Lorenzini

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1528
Author(s):  
Wenlong Li ◽  
Zhihui Xie ◽  
Kun Xi ◽  
Shaojun Xia ◽  
Yanlin Ge

A model of rectangular microchannel heat sink (MCHS) with porous medium (PM) is developed. Aspect ratio of heat sink (HS) cell and length-width ratio of HS are optimized by numerical simulation method for entropy generation minimization (EGM) according to constructal theory. The effects of inlet Reynolds number (Re) of coolant, heat flux on bottom, porosity and volume proportion of PM on dimensionless entropy generation rate (DEGR) are analyzed. From the results, there are optimal aspect ratios to minimize DEGR. Given the initial condition, DEGR is 33.10% lower than its initial value after the aspect ratio is optimized. With the increase of Re, the optimal aspect ratio declines, and the minimum DEGR drops as well. DEGR gets larger and the optimal aspect ratio remains constant with the increasing of heat flux on bottom. For the different volume proportion of PM, the optimal aspect ratios are diverse, but the minimum DEGR almost stays unchanged. The twice minimized DEGR, which results from aspect ratio and length-width ratio optimized simultaneously, is 10.70% lower than the once minimized DEGR. For a rectangular bottom, a lower DEGR can be reached by choosing the proper direction of fluid flow.


Author(s):  
K. Bala Subrahmanyam ◽  
Aparesh Datta ◽  
Pritam Das

This numerical study investigates the simultaneous application of axial wall conduction effect and entropy generation minimization as two principles to identify heat transfer performance in a microchannel heat sink with fan cavity and ribs. In this conjugate analysis, three different materials for a microchannel heat sink considered are silicon, aluminium, and copper. In addition to the fan cavity (F), effects of different rib configurations arranged symmetrically inside the fan cavity, that is, backward triangle rib (FB), rectangular rib (FR), forward triangle rib (FF), and diamond rib (FD) with Reynolds numbers ranging from 136 to 588 are studied. The comparative study between silicon and copper in terms of local wall and bulk fluid temperatures, increment in solid wall to fluid thermal conductivity ratio within the range (247.07 <  ksf < 669.44), local Nusselt number (Nu x), axial conduction number (M), and entropy generation number ( Ns, a) were furnished and examined. Structural optimization is performed on diamond rib configuration geometrical parameters to observe entropy generation number and wall conduction effects trend as thermal performance is greatly improved to 2.49, at the lowest Ns, a to 0.31 at Re 391.47, with copper in the back to back cavities case. However based on the numerical results, comparative importance of axial wall conduction effect consideration in the present design of microsink, silicon is showing best results in overcoming at Re 588.4, consistently in all optimization cases.


2020 ◽  
Vol 45 (4) ◽  
pp. 333-342
Author(s):  
Krishan Kumar ◽  
Rajan Kumar ◽  
Rabinder Singh Bharj

AbstractThe performance of the microchannel heat sink (MCHS) in electronic applications needs to be optimized corresponding to the number of channels (N). In this study optimization of the number of channels corresponding to the diameter of the microchannel ({D_{N}}) using an entropy generation minimization approach is achieved for the MCHS used in electronic applications. The numerical study is performed for constant total heat flow rate ({\dot{q}_{tot}}) and total mass flow rate ({\dot{m}_{tot}}). The results indicate that the dominance of frictional entropy generation ({S_{gen,Fr}}) increases with the reduction in diameter. However, the entropy generation due to heat transfer ({S_{gen,HT}}) decreases with the reduction in diameter. Therefore, the optimum diameter ({D^{\ast }}) is calculated corresponding to the minimum total entropy generation ({S_{gen,total}}) for the optimum number of channels ({N^{\ast }}). Furthermore, the entropy generation number ({N_{S}}) and Bejan number (Be) are also calculated.


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