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With the development of higher performance and miniaturization of electronic components, the flow heat transfer of working fluids in nanochannels has received more attention. To elucidate this phenomenon, molecular dynamics simulations are used to simulate the behaviors of fluids within nanochannels at temperatures of 300 K, 325 K, and 350 K. Water serves as a flow medium, with argon substituted for any non-condensable gases. In the flow process, argon atoms aggregate into clusters that are characterized by high potential energy. As the temperature rises, the concomitant increases in the fluid’s potential energy, which leads to the gradual diminution or complete dissipation of these clusters. A minor presence of gas atoms can facilitate fluid movement; however, an excess of argon promotes the formation of larger gaseous clusters in the central region of the channel, thereby impeding fluid flow. Concurrently, the application of heat to the fluid appreciably diminishes the coefficient of flow resistance. The temperature of the fluid in the near-wall region exceeds that of the central area. In the clusters, the atoms exhibit heightened activity, leading to an increase in the average molecular kinetic energy and a concomitant rise in temperature. The inherent hydrogen-bonding structure of water enhances heat transfer within the nanochannels. Argon atoms exert an influence on the number of hydrogen bonds, and rising temperatures disrupts the hydrogen-bond network established by water molecules, ultimately leading to a decrease of the Nusselt number. This investigation offers insights into the heat transfer dynamics of water molecular flow within microchannels under the perturbation of non-condensable gases, thereby furnishing theoretical guidance for enhancing heat transfer within electronic devices.
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Keywords:
- molecular dynamics /
- heat transfer /
- cluster /
- nanochannel
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Google Scholar
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Google Scholar
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Google Scholar
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图 1 模拟系统图
Figure 1. Simulation system.
图 2 水分子流动传热模拟验证 (a) 模型示意图; (b) 温度分布
Figure 2. Validation of water molecule flow heat transfer simulation: (a) Model Schematic; (b) temperature distribution.
图 3 流体的势能分布
Figure 3. Potential energy distribution of a fluid.
图 4 流体的数密度分布 (a) T0 = 300 K, H2O; (b) T0 = 300 K, Ar; (c) T0 = 325 K, H2O; (d) T0 = 325 K, Ar; (e) T0 = 350 K, H2O; (f) T0 = 350 K, Ar
Figure 4. Number density distribution of fluids: (a) T0 = 300 K, H2O; (b) T0 = 300 K, Ar; (c) T0 = 325 K, H2O; (d) T0 = 325 K, Ar; (e) T0 = 350 K, H2O; (f) T0 = 350 K, Ar.
图 5 流体的速度分布 (a) T0 = 300 K; (b) T0 = 325 K; (c) T0 = 350 K
Figure 5. Velocity distribution of fluids: (a) T0 = 300 K; (b) T0 = 325 K; (c) T0 = 350 K.
图 6 流体的速度滑移
Figure 6. Velocity slip of fluids.
图 7 流体的阻力系数
Figure 7. Drag coefficient of fluids.
图 8 流体的温度分布 (a) T0 = 300 K; (b) T0 = 325 K; (c) T0 = 350 K
Figure 8. Temperature distribution of fluids: (a) T0 = 300 K; (b) T0 = 325 K; (c) T0 = 350 K.
图 9 氢键示意图
Figure 9. Hydrogen bonding schematic.
图 10 流体的氢键数量 (a)氢键数量; (b)平均单个水分子形成的氢键数量
Figure 10. Number of hydrogen bonds in fluids: (a) Number of hydrogen bonds; (b) the average number of hydrogen bonds formed by a single water molecule.
图 11 流体的努塞尔数
Figure 11. Nussle number of the fluid.
表 1 流道内的粒子数量
Table 1. Number of particles in the channel.
${N_{{{\text{H}}_{2}}{\text{O}}}}$ NAr Case 1 2600 0 Case 2 2200 600 Case 3 2400 400 Case 4 2400 300 Case 5 2400 120 
DownLoad: CSV
表 2 各原子之间的相互作用势能参数及尺寸参数
Table 2. Interaction potential energy parameters and size parameters of each atom.
原子种类 势能参数 ε/(kcal·mol–1) 尺寸参数 σ/nm O-O 0.1554 0.3166 H-H 0 0 Ar-Ar 0.2392 0.3040 Cu-Cu 0.2379 0.2340
DownLoad: CSV
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[1] Tang J Q, Liu Y, Huang B L, Xu D Y 2022 Int. J. Therm. Sci. 177 107573
Google Scholar
[2] Fathi M, Heyhat M M, Targhi M Z, Bigham S 2023 Int. Commun. Heat Mass Transf. 146 106868
Google Scholar
[3] Joseph P, Tabeling P 2005 Phys. Rev. E 71 035303
Google Scholar
[4] 苗瑞灿, 张石重, 陈占秀, 杨历 2020 化工进展 39 1641
Google Scholar
Miao R C, Zhang S Z, Chen Z X, Yang L 2020 Chem. Ind. Eng. Prog. 39 1641
Google Scholar
[5] 梅涛, 陈占秀, 杨历, 王坤, 苗瑞灿 2019 物理学报 68 094701
Google Scholar
Mei T, Chen Z X, Yang L, Wang K, Miao R C 2019 Acta Phys. Sin. 68 094701
Google Scholar
[6] Shi L, Hu C Z, Bai M L, Lv J Z 2019 Comput. Mater. Sci. 170 109127
Google Scholar
[7] Voronov R S, Papavassiliou D V, Lee L L 2007 Chem. Phys. Lett. 441 273
Google Scholar
[8] Zhang C B, Chen Y P 2014 Chem. Eng. Process. 85 203
Google Scholar
[9] Priezjev N V 2007 Phys. Rev. E 75 051605
Google Scholar
[10] Semiromi D T, Azimian A R 2010 Heat Mass Transfer. 46 791
Google Scholar
[11] Markvoort A J, Hilbers P A J, Nedea S V 2005 Phys. Rev. E 71 066702
Google Scholar
[12] Zhou W B, Han D M, Xia G D 2022 Appl. Surf. Sci. 591 153155
Google Scholar
[13] Bao L Y, Priezjev N V, Hu H B, Luo K 2017 Phys. Rev. E 96 033110
Google Scholar
[14] Ge S, Gu Y W, Chen M 2015 Mol. Phys. 113 703
Google Scholar
[15] Yao S T, Wang J S, Jin S F, Tan F G, Chen S P 2023 J. Mol. Liq. 371 121105
Google Scholar
[16] Alkhwaji A, Elbahloul S, Abdullah M Z, Bakar K F B A 2021 J. Mol. Liq. 324 114703
Google Scholar
[17] Hong S N, Choe S H, Jong U G, Pak M S, Yu C J 2019 Fluid Phase Equilib. 487 45
Google Scholar
[18] Xi B H, Zhao T, Gao Q W, Wei Z X, Zhao S L 2022 Chem. Eng. Sci. 254 117618
Google Scholar
[19] 张石重, 陈占秀, 杨历, 苗瑞灿, 张子剑 2020 化工进展 39 3892
Google Scholar
Zhang S Z, Chen Z X, Yang L, Miao R C, Zhang Z J 2020 Chem. Ind. Eng. Prog. 39 3892
Google Scholar
[20] Chen L, Wang S Y, Xiang X, Tao W Q 2020 Comput. Mater. Sci. 171 109223
Google Scholar
[21] Wu N N, Zeng L C, Fu T, Wang Z H, Lu C 2020 Int. J. Heat Mass Transf. 147 118905
Google Scholar
[22] Stukowski A 2010 Modell. Simul. Mat. Sci. Eng. 18 015012
Google Scholar
[23] Yao S T, Wang J S, Liu X L 2021 Int. J. Heat Mass Transf. 176 121441
Google Scholar
[24] Song Z, Shang X S, Cui Z, Liu Y, Cao Q 2023 Appl. Therm. Eng. 218 119362
Google Scholar
[25] 梅涛, 陈占秀, 杨历, 朱洪漫, 苗瑞灿 2020 物理学报 69 224701
Google Scholar
Mei T, Chen Z X, Yang L, Zhu H M, Miao R C 2020 Acta Phys. Sin. 69 224701
Google Scholar
[26] Tao J B, Song X Y, Chen W, Zhao S L, Liu H L 2020 Mol. Simul. 46 699
Google Scholar
[27] Motlagh M B, Kalteh M 2020 J. Mol. Liq. 318 114028
Google Scholar
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