基于反扰动非平衡分子动力学的纳米流体导热增强机理研究

doi:10.11949/j.issn.0438-1157.20181095

摘要/Abstract

摘要：

相较于水、乙二醇等常规流体，纳米流体出色的传热效果使其成为近十年来研究的热点之一。利用一种反扰动非平衡分子动力学方法对纳米流体的导热增强机理进行了模拟研究。在基液Ar 中加入 Cu 纳米颗粒后, 纳米流体的热通量和热导率均发生了不同程度的改变，纳米颗粒体积分数的变化，在一定程度上改变了纳米流体内部的能量传递过程。进一步分析了纳米流体热导率强化的微观作用机理，发现纳米颗粒的加入，使得纳米流体的微观结构具有了类似晶体的微观结构特性，在颗粒尺寸较小的情况下，流体内部受温度梯度作用效应明显。

关键词: 纳米流体, 热导率, 纳米粒子, 分子模拟, 两相流

Abstract:

Compared with conventional fluids such as water and ethylene glycol, the excellent heat transfer effect of nanofluids has made it one of the hotspots of research in the past decade. In this paper, the heat conduction enhancement mechanism of nanofluids is simulated by a reverse non equilibrium molecular dynamics method. The heat flux density and thermal conductivity of nanofluids change with the addition of Cu nanoparticles in Ar, and the volume fraction of nanoparticles changes the energy transfer process in nanofluids to a certain extent. Furthermore, the microscopic mechanism of the enhancement of thermal conductivity of nanofluids is analyzed. It was found that the addition of nanoparticles made the microstructure of nanofluids similar to that of crystals. Under the condition of smaller particle size, the effect of temperature gradient on the fluid is obvious.

Key words: nanofluids, thermal conductivity, nanoparticles, molecular simulation, two-phase flow

中图分类号:

TK 124

陈巨辉, 韩坤, 王帅, 李铭坤, 陈纪元, 马明. 基于反扰动非平衡分子动力学的纳米流体导热增强机理研究[J]. 化工学报, 2019, 70(6): 2147-2152.

Juhui CHEN, Kun HAN, Shuai WANG, Mingkun LI, Jiyuan CHEN, Ming MA. Study on thermal conductive enhancement mechanism of nanofluid based on anti-disturbance non-equilibrium molecular dynamics[J]. CIESC Journal, 2019, 70(6): 2147-2152.

图/表 9

表1 Cu/Ar相互作用的L-J势能参数[27]

Table 1 L-J potential energy parameters for Cu/Ar interaction[27]

粒子对	σ/nm	ε×10²⁸/J
Ar-Ar	0.3405	1.654
Cu-Cu	0.2338	65.581
Cu-Ar	0.2872	10.415

图1 液氩流体热导率随时间的变化趋势

Fig.1 Variation trend of thermal conductivity of liquid argon with time

图2 粒子位置分布

Fig.2 Particle position distribution

图3 热通量随时间的变化趋势

Fig.3 Trend of energy flux versus time

表2 不同体积分数纳米颗粒的热导率及变化率

Table 2 Thermal conductivity and rate of change of nanoparticles with different volume fractions

模拟对象	热导率
pure Ar	0.1259	—
Cu-0.5%-1 nm	0.1313	4.29
Cu-1.0%-1 nm	0.1366	8.50
Cu-1.5%-1 nm	0.1418	12.63

图4 温度梯度随时间的变化趋势

Fig.4 Trend of temperature gradient over time

图5 纳米流体热导率随时间的变化趋势

Fig.5 Trend of thermal conductivity of nanofluids over time

图6 不同体积分数纳米流体的热导率变化趋势

Fig.6 Variation trend of thermal conductivity of nanofluids with different volume fractions

图7 不同体积分数纳米流体的径向分布函数

Fig.7 Radial distribution function of nanofluids with different volume fractions(1 ?=0.1 nm)

参考文献 30

1	ChoiS U S, EastmanJ A. Enhancing thermal conductivity of fluids with nanoparticles[J]. ASME FED, 1995, 231(1): 99-105
2	WangX, JingD. Determination of thermal conductivity of interfacial layer in nanofluids by equilibrium molecular dynamics simulation[J]. International Journal of Heat and Mass Transfer, 2019, 128: 199-207.
3	CuiW, ShenZ, YangJ, et al. On the increased heat conduction and changed flow boundary-layer of nanofluids by molecular dynamics simulation[J]. Digest Journal of Nanomaterials & Biostructures, 2015, 10(1): 207-219.
4	MilaneseM, IacobazziF, ColangeloG, et al. An investigation of layering phenomenon at the liquid–solid interface in Cu and CuO based nanofluids[J]. International Journal of Heat & Mass Transfer, 2016, 103: 564-571.
5	BabaeiH, KeblinskiP, KhodadadiJ M. A proof for insignificant effect of Brownian motion-induced micro-convection on thermal conductivity of nanofluids by utilizing molecular dynamics simulations[J]. Journal of Applied Physics, 2013, 113(8): 084302.
6	崔文政, 沈照杰, 毛东旭, 等. 纳米流体中纳米颗粒微运动的分子动力学模拟[J]. 化工学报, 2017, 68(S1): 48-53.
	CuiW Z, ShenZ J, MaoD X, et al. Micro-movements of nanoparticles in nanofluids: molecular dynamics simulation[J].CIESC Journal, 2017, 68(S1): 48-53.
7	MaheshwaryP B, HandaC C, NemadeK R. A comprehensive study of effect of concentration, particle size and particle shape on thermal conductivity of titania/water based nanofluid[J]. Applied Thermal Engineering, 2017, 119： 79-88.
8	SedighiM, MohebbiA. Investigation of nanoparticle aggregation effect on thermal properties of nanofluid by a combined equilibrium and non-equilibrium molecular dynamics simulation[J]. Journal of Molecular Liquids, 2014, 197(9): 14-22.
9	LouZ, YangM. Molecular dynamics simulations on the shear viscosity of Al₂O₃ nanofluids[J]. Computers & Fluids, 2015, 117: 17-23.
10	KangH, ZhangY, YangM, et al. Nonequilibrium molecular dynamics simulation of coupling between nanoparticles and base-fluid in a nanofluid[J]. Physics Letters A, 2012, 376(4): 521-524.
11	LingL, ZhangY, MaH, et al. Molecular dynamics simulation of effect of liquid layering around the nanoparticle on the enhanced thermal conductivity of nanofluids[J]. Journal of Nanoparticle Research, 2010, 12(3): 811-821.
12	AchhalE M, JabraouiH, ZeroualS, et al. Modeling and simulations of nanofluids using classical molecular dynamics: particle size and temperature effects on thermal conductivity[J]. Advanced Powder Technology, 2018, 29(10): 2434-2439.
13	JavanmardiM J, JafarpurK. A molecular dynamics simulation for thermal conductivity evaluation of carbon nanotube-water nanofluids[J]. Journal of Heat Transfer, 2013, 135(4): 042401.
14	AimoliC G, MaginnE J, AbreuC R A. Transport properties of carbon dioxide and methane from molecular dynamics simulations[J]. Journal of Chemical Physics, 2014, 141(13): 11131.
15	BedrovD, SmithG D. Thermal conductivity of molecular fluids from molecular dynamics simulations: application of a new imposed-flux method[J]. Journal of Chemical Physics, 2000, 113(18): 8080-8084.
16	DystheD K, FuchsA H, RousseauB. Fluid transport properties by equilibrium molecular dynamics（Ⅰ）: Methodology at extreme fluid states[J]. The Journal of Chemical Physics, 1999, 110(8): 4047-4059.
17	IkeshojiT, HafskjoldB. Non-equilibrium molecular dynamics calculation of heat conduction in liquid and through liquid-gas interface[J]. Molecular Physics, 1994, 81(2): 251-261.
18	HafskjoldB, IkeshojiT, RatkjeS K. On the molecular mechanism of thermal diffusion in liquids[J]. Molecular Physics, 1993, 80(6): 1389-1412.
19	Müller-PlatheF. A simple nonequilibrium molecular dynamics method for calculating the thermal conductivity[J]. Journal of Chemical Physics, 1997, 106(14): 6082-6085.
20	ArtolaP A, RousseauB. Thermal diffusion in simple liquid mixtures: what have we learnt from molecular dynamics simulations?[J]. Molecular Physics, 2013, 111(22/23): 3394-3403.
21	ZhangM, Müller-PlatheF. Reverse nonequilibrium molecular-dynamics calculation of the Soret coefficient in liquid benzene/cyclohexane mixtures[J]. The Journal of Chemical Physics, 2005, 123(12): 124502.
22	ZhangM. Thermal diffusion in liquid mixtures and polymer solutions by molecular dynamics simulations [D]. Darmstadt: Technischen Universität, 2006.
23	ReithD, Müller-PlatheF. On the nature of thermal diffusion in binary Lennard-Jones liquids[J]. The Journal of Chemical Physics, 2000, 112(5): 2436-2443.
24	MohebbiA. Prediction of specific heat and thermal conductivity of nanofluids by a combined equilibrium and nonequilibrium molecular dynamics simulation[J]. Journal of Molecular Liquids, 2012, 175(22): 51-58.
25	TopalI, ServantieJ. Molecular dynamics study of the thermal conductivity in nanofluids[J]. Chemical Physics, 2019, 516: 147-151.
26	HuC, BaiM, LvJ, et al. An investigation on the flow and heat transfer characteristics of nanofluids by nonequilibrium molecular dynamics simulations[J]. Numerical Heat Transfer Part B Fundamentals, 2016, 70(2): 152-163.
27	CuiW, ShenZ, YangJ, et al. Molecular dynamics simulation on the microstructure of absorption layer at the liquid solid interface in nanofluids[J]. International Communications in Heat and Mass Transfer, 2016, 71: 75-85.
28	SarkarS, SelvamR P. Thermal conductivity computation of nanofluids by equilibrium molecular dynamics simulation: nanoparticle loading and temperature effect[C]//MRS Proceedings. 2007.
29	TengK L, HsiaoP Y, HungS W, et al. Enhanced thermal conductivity of nanofluids diagnosis by molecular dynamics simulations[J]. Journal of Nanoscience & Nanotechnology, 2008, 8(7): 3710-3718.
30	HamiltonR L, CrosserO K. Thermal conductivity of heterogeneous two-component systems[J]. Ind. Eng. Chem. Fundam., 1962, 1(3): 27-40.

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