Optimization of volume to point conduction problem based on a novel thermal conductivity discretization algorithm

doi:10.1016/j.cjche.2015.05.010

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (7): 1161-1168.DOI: 10.1016/j.cjche.2015.05.010

• 过程系统工程与过程安全 • 上一篇下一篇

Optimization of volume to point conduction problem based on a novel thermal conductivity discretization algorithm

Wenjing Du^1,2, Peili Wang^1,2, Lipeng Song¹, Lin Cheng¹

1 Institute of Thermal Science & Technology, Shandong University, Jinan 250061, China;
2 Department of Energy and Power Engineering, Shandong University, Jinan 250061, China

收稿日期:2015-03-13 修回日期:2015-05-08 出版日期:2015-07-28 发布日期:2015-08-21
通讯作者: Lin Cheng
基金资助:
Supported by the National Key Basic Research Program of China (2013CB228305).

Optimization of volume to point conduction problem based on a novel thermal conductivity discretization algorithm

Wenjing Du^1,2, Peili Wang^1,2, Lipeng Song¹, Lin Cheng¹

1 Institute of Thermal Science & Technology, Shandong University, Jinan 250061, China;
2 Department of Energy and Power Engineering, Shandong University, Jinan 250061, China

Received:2015-03-13 Revised:2015-05-08 Online:2015-07-28 Published:2015-08-21
Contact: Lin Cheng
Supported by:
Supported by the National Key Basic Research Program of China (2013CB228305).

摘要/Abstract

摘要： A conduction heat transfer process is enhanced by filling prescribed quantity and optimized-shaped high thermal conductivity materials to the substrate. Numerical simulations and analyses are performed on a volume to point conduction problem based on the principle of minimum entropy generation. In the optimization, the arrangement of high thermal conductivity materials is variable, the quantity of high thermal-conductivity material is constrained, and the objective is to obtain the maximum heat conduction rate as the entropy is the minimum. A novel algorithm of thermal conductivity discretization is proposed based on large quantity of calculations. Compared with other algorithms in literature, the average temperature in the substrate by the new algorithm is lower, while the highest temperature in the substrate is in a reasonable range. Thus the new algorithm is feasible. The optimization of volume to point heat conduction is carried out in a rectangular model with radiation boundary condition and constant surface temperature boundary condition. The results demonstrate that the algorithm of thermal conductivity discretization is applicable for volume to point heat conduction problems.

关键词: Volume to point conduction, Principle of minimum entropy generation, Algorithm of thermal conductivity discretization, Optimization

Abstract: A conduction heat transfer process is enhanced by filling prescribed quantity and optimized-shaped high thermal conductivity materials to the substrate. Numerical simulations and analyses are performed on a volume to point conduction problem based on the principle of minimum entropy generation. In the optimization, the arrangement of high thermal conductivity materials is variable, the quantity of high thermal-conductivity material is constrained, and the objective is to obtain the maximum heat conduction rate as the entropy is the minimum. A novel algorithm of thermal conductivity discretization is proposed based on large quantity of calculations. Compared with other algorithms in literature, the average temperature in the substrate by the new algorithm is lower, while the highest temperature in the substrate is in a reasonable range. Thus the new algorithm is feasible. The optimization of volume to point heat conduction is carried out in a rectangular model with radiation boundary condition and constant surface temperature boundary condition. The results demonstrate that the algorithm of thermal conductivity discretization is applicable for volume to point heat conduction problems.

Key words: Volume to point conduction, Principle of minimum entropy generation, Algorithm of thermal conductivity discretization, Optimization

Wenjing Du, Peili Wang, Lipeng Song, Lin Cheng. Optimization of volume to point conduction problem based on a novel thermal conductivity discretization algorithm[J]. Chinese Journal of Chemical Engineering, 2015, 23(7): 1161-1168.

Wenjing Du, Peili Wang, Lipeng Song, Lin Cheng. Optimization of volume to point conduction problem based on a novel thermal conductivity discretization algorithm[J]. Chin.J.Chem.Eng., 2015, 23(7): 1161-1168.

参考文献

[1] Z.Y. Guo, X.G. Liang, X.R. Zhang, The heat management of space station, Chin. Sci. Bull. 46 (16) (2001) 1403-1408.

[2] Z.Y. Guo, International Heat Transfer Research Frontier_Micro Scale Heat Transfer, Adv. Mech. 30 (1) (2000) 1-6.

[3] A. Bejan, Constructal-theory network of conducting paths for cooling a heat generation volume, Int. J. Heat Mass Transf. 40 (4) (1997) 799-808.

[4] A. Bejan, Shape and Structure, from Engineering to Nature, Cambridge University Press, New York, 2000. 52-81.

[5] A. Bejan, Entropy Generation Through Heat and Fluid Flow,Wiley, New York, 1982.

[6] A. Bejan, Entropy Generation Minimization, Wiley, New York, 1996.

[7] A. Bejan, Entropy generation minimization: the new thermodynamics of finite-size devices and finite-time processes, Appl. Phys. Rev. 79 (3) (1996) 1191-1218.

[8] Z.Y. Guo, H.Y. Zhu, X.G. Liang, Entransy—a physical quantity describing heat transfer ability, Int. J. Heat Mass Transf. 50 (13-14) (2007) 2545-2556.

[9] Z.Y. Guo, X.G. Cheng, Z.Z. Xia, Least dissipation principle of heat transport potential capacity and its application in heat conduction optimization, Chin. Sci. Bull. 48 (4) (2003) 406-410.

[10] X.G. Cheng, J.A. Meng, Z.Y. Guo, Potential capacity dissipation minimization and entropy generation minimization in heat conduction optimization, J. Eng. Thermophys. 26 (6) (2005) 1034-1036.

[11] X.G. Cheng, Entranspy and its Applications in Heat Transfer Optimization(Doctoral Dissertation) 2004.

[12] X.G. Cheng, Z.X. Li, Z.Y. Guo, The construction of high effect heat conduction channel according to bio-inspired optimization, Sci. China E 33 (3) (2003) 251-256.

[13] Z.Z. Xia, Z.Y. Guo, Simulate the optimization of heat conduction by using life evolution process, Prog. Nat. Sci. 11 (8) (2001) 845-852.

[14] Q. Chen, H.Y. Zhu, N. Pan, et al., An alternative criterion in heat transfer optimization, Proc. R. Soc. 467 (2011) (2011) 1012-1028.

[15] Q. Chen, X.G. Liang, Z.Y. Guo, Entranspy theory for the optimization of heat transfer—a review and update, Int. J. Heat Mass Transf. 63 (2013) (2013) 65-81.

[16] L.G. Chen, Progress in entranspy theory and its applications, Chin. Sci. Bull. 57 (34) (2012) 4404-4426.

[17] A. Bejan, Entropy generation minimization: the new thermodynamics of finite size devices and finite time processes, J. Appl. Phys. 79 (1996) 1191.

[18] G.Z. Han, Z.Y. Guo, Physical mechanismof heat conduction ability dissipation and its analytical expression, Proc. CSEE 27 (17) (2007) 98-102.

[19] Z.Z. Xia, Augmentation and Optimization on Heat Conduction and Convection Processes(Doctoral Dissertation) 2001.

[20] A.A. Penzias, Measurement of cosmic microwave background radiation, IEEE Trans. Microwave Theory Tech. 16 (9) (1968) 608-611.

[21] H.Y. Zhu, Z.J. Chen, Z.Y. Guo, The electric-thermal simulation experiment research of entropy generation minimization principle, Prog. Nat. Sci. 17 (12) (2007) 1692-1698.

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