Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study

The requirements for high efficient heat exchange technology in many fields are gradually increasing. Nanofluids have attracted extensive attention due to their enhanced thermal conductivity. Revealing the mechanism of thermal conductivity enhancement is key to the preparation of high-efficiency nan...
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Gespeichert in:

Autor*in:	Li, Yanhua [verfasserIn] Zhai, Yuling [verfasserIn] Xuan, Zihao [verfasserIn] Guo, Wenjie [verfasserIn] Wang, Hua [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Thermal conductivity Molecular dynamics Radial distribution function Number density Trajectory

Übergeordnetes Werk:	Enthalten in: Journal of molecular liquids - New York, NY [u.a.] : Elsevier, 1983, 354
Übergeordnetes Werk:	volume:354

DOI / URN:	10.1016/j.molliq.2022.118877

Katalog-ID:	ELV007695772

Internformat


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245	1	0	\|a Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study
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520			\|a The requirements for high efficient heat exchange technology in many fields are gradually increasing. Nanofluids have attracted extensive attention due to their enhanced thermal conductivity. Revealing the mechanism of thermal conductivity enhancement is key to the preparation of high-efficiency nanofluids. In molecular dynamics (MD), the accuracy of simulation models is crucial to predict the thermal conductivity of nanofluids. Here, systems with various volume fractions were established by utilizing three different approaches. These approaches, denoted as Model 1, Model 2, and Model 3, involved changing the number of nanoparticles (NPs), the size of NPs, and the size of the simulation box as well as number of water molecules, respectively. The thermal conductivity values obtained from the simulations were validated using experimental data. The mechanism of thermal conductivity enhancement was explained by microscopic parameters. The results show that Model 1 was best able to capture the trends in thermal conductivity seen in the experimental data. Further, the thickness of the interfacial layer, estimated from the RDF and number density, was not affected by volume fraction and temperature, which instead determine the interactive forces between the NPs and the base fluid. The mechanism of enhancement of thermal conductivity may be attributed to the fact that the L-J potential energy of the Cu-O pair (−1.21 Kcal . mol−1) is stronger than that of the O-O pair (−0.15 Kcal . mol−1), thereby bringing water molecules into closer contact with the Cu NPs. As the volume fraction and temperature increase, the heat exchange between water molecules inside and outside the interfacial layer also increases, which accelerates the attainment of thermal equilibrium in the nanofluidic system.
650		4	\|a Thermal conductivity
650		4	\|a Molecular dynamics
650		4	\|a Radial distribution function
650		4	\|a Number density
650		4	\|a Trajectory
700	1		\|a Zhai, Yuling \|e verfasserin \|4 aut
700	1		\|a Xuan, Zihao \|e verfasserin \|4 aut
700	1		\|a Guo, Wenjie \|e verfasserin \|4 aut
700	1		\|a Wang, Hua \|e verfasserin \|4 aut
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936	b	k	\|a 35.21 \|j Lösungen \|j Flüssigkeiten \|x Physikalische Chemie
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matchkey_str	article:18733166:2022----::salsmnotemlodciiyoeadnlssfnacmnmcaimnao
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publishDate	2022
allfields	10.1016/j.molliq.2022.118877 doi (DE-627)ELV007695772 (ELSEVIER)S0167-7322(22)00415-9 DE-627 ger DE-627 rda eng 540 DE-600 35.21 bkl Li, Yanhua verfasserin aut Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The requirements for high efficient heat exchange technology in many fields are gradually increasing. Nanofluids have attracted extensive attention due to their enhanced thermal conductivity. Revealing the mechanism of thermal conductivity enhancement is key to the preparation of high-efficiency nanofluids. In molecular dynamics (MD), the accuracy of simulation models is crucial to predict the thermal conductivity of nanofluids. Here, systems with various volume fractions were established by utilizing three different approaches. These approaches, denoted as Model 1, Model 2, and Model 3, involved changing the number of nanoparticles (NPs), the size of NPs, and the size of the simulation box as well as number of water molecules, respectively. The thermal conductivity values obtained from the simulations were validated using experimental data. The mechanism of thermal conductivity enhancement was explained by microscopic parameters. The results show that Model 1 was best able to capture the trends in thermal conductivity seen in the experimental data. Further, the thickness of the interfacial layer, estimated from the RDF and number density, was not affected by volume fraction and temperature, which instead determine the interactive forces between the NPs and the base fluid. The mechanism of enhancement of thermal conductivity may be attributed to the fact that the L-J potential energy of the Cu-O pair (−1.21 Kcal . mol−1) is stronger than that of the O-O pair (−0.15 Kcal . mol−1), thereby bringing water molecules into closer contact with the Cu NPs. As the volume fraction and temperature increase, the heat exchange between water molecules inside and outside the interfacial layer also increases, which accelerates the attainment of thermal equilibrium in the nanofluidic system. Thermal conductivity Molecular dynamics Radial distribution function Number density Trajectory Zhai, Yuling verfasserin aut Xuan, Zihao verfasserin aut Guo, Wenjie verfasserin aut Wang, Hua verfasserin aut Enthalten in Journal of molecular liquids New York, NY [u.a.] : Elsevier, 1983 354 Online-Ressource (DE-627)302469664 (DE-600)1491496-7 (DE-576)259483915 1873-3166 nnns volume:354 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2807 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.21 Lösungen Flüssigkeiten Physikalische Chemie AR 354
spelling	10.1016/j.molliq.2022.118877 doi (DE-627)ELV007695772 (ELSEVIER)S0167-7322(22)00415-9 DE-627 ger DE-627 rda eng 540 DE-600 35.21 bkl Li, Yanhua verfasserin aut Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The requirements for high efficient heat exchange technology in many fields are gradually increasing. Nanofluids have attracted extensive attention due to their enhanced thermal conductivity. Revealing the mechanism of thermal conductivity enhancement is key to the preparation of high-efficiency nanofluids. In molecular dynamics (MD), the accuracy of simulation models is crucial to predict the thermal conductivity of nanofluids. Here, systems with various volume fractions were established by utilizing three different approaches. These approaches, denoted as Model 1, Model 2, and Model 3, involved changing the number of nanoparticles (NPs), the size of NPs, and the size of the simulation box as well as number of water molecules, respectively. The thermal conductivity values obtained from the simulations were validated using experimental data. The mechanism of thermal conductivity enhancement was explained by microscopic parameters. The results show that Model 1 was best able to capture the trends in thermal conductivity seen in the experimental data. Further, the thickness of the interfacial layer, estimated from the RDF and number density, was not affected by volume fraction and temperature, which instead determine the interactive forces between the NPs and the base fluid. The mechanism of enhancement of thermal conductivity may be attributed to the fact that the L-J potential energy of the Cu-O pair (−1.21 Kcal . mol−1) is stronger than that of the O-O pair (−0.15 Kcal . mol−1), thereby bringing water molecules into closer contact with the Cu NPs. As the volume fraction and temperature increase, the heat exchange between water molecules inside and outside the interfacial layer also increases, which accelerates the attainment of thermal equilibrium in the nanofluidic system. Thermal conductivity Molecular dynamics Radial distribution function Number density Trajectory Zhai, Yuling verfasserin aut Xuan, Zihao verfasserin aut Guo, Wenjie verfasserin aut Wang, Hua verfasserin aut Enthalten in Journal of molecular liquids New York, NY [u.a.] : Elsevier, 1983 354 Online-Ressource (DE-627)302469664 (DE-600)1491496-7 (DE-576)259483915 1873-3166 nnns volume:354 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2807 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.21 Lösungen Flüssigkeiten Physikalische Chemie AR 354
allfields_unstemmed	10.1016/j.molliq.2022.118877 doi (DE-627)ELV007695772 (ELSEVIER)S0167-7322(22)00415-9 DE-627 ger DE-627 rda eng 540 DE-600 35.21 bkl Li, Yanhua verfasserin aut Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The requirements for high efficient heat exchange technology in many fields are gradually increasing. Nanofluids have attracted extensive attention due to their enhanced thermal conductivity. Revealing the mechanism of thermal conductivity enhancement is key to the preparation of high-efficiency nanofluids. In molecular dynamics (MD), the accuracy of simulation models is crucial to predict the thermal conductivity of nanofluids. Here, systems with various volume fractions were established by utilizing three different approaches. These approaches, denoted as Model 1, Model 2, and Model 3, involved changing the number of nanoparticles (NPs), the size of NPs, and the size of the simulation box as well as number of water molecules, respectively. The thermal conductivity values obtained from the simulations were validated using experimental data. The mechanism of thermal conductivity enhancement was explained by microscopic parameters. The results show that Model 1 was best able to capture the trends in thermal conductivity seen in the experimental data. Further, the thickness of the interfacial layer, estimated from the RDF and number density, was not affected by volume fraction and temperature, which instead determine the interactive forces between the NPs and the base fluid. The mechanism of enhancement of thermal conductivity may be attributed to the fact that the L-J potential energy of the Cu-O pair (−1.21 Kcal . mol−1) is stronger than that of the O-O pair (−0.15 Kcal . mol−1), thereby bringing water molecules into closer contact with the Cu NPs. As the volume fraction and temperature increase, the heat exchange between water molecules inside and outside the interfacial layer also increases, which accelerates the attainment of thermal equilibrium in the nanofluidic system. Thermal conductivity Molecular dynamics Radial distribution function Number density Trajectory Zhai, Yuling verfasserin aut Xuan, Zihao verfasserin aut Guo, Wenjie verfasserin aut Wang, Hua verfasserin aut Enthalten in Journal of molecular liquids New York, NY [u.a.] : Elsevier, 1983 354 Online-Ressource (DE-627)302469664 (DE-600)1491496-7 (DE-576)259483915 1873-3166 nnns volume:354 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2807 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.21 Lösungen Flüssigkeiten Physikalische Chemie AR 354
allfieldsGer	10.1016/j.molliq.2022.118877 doi (DE-627)ELV007695772 (ELSEVIER)S0167-7322(22)00415-9 DE-627 ger DE-627 rda eng 540 DE-600 35.21 bkl Li, Yanhua verfasserin aut Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The requirements for high efficient heat exchange technology in many fields are gradually increasing. Nanofluids have attracted extensive attention due to their enhanced thermal conductivity. Revealing the mechanism of thermal conductivity enhancement is key to the preparation of high-efficiency nanofluids. In molecular dynamics (MD), the accuracy of simulation models is crucial to predict the thermal conductivity of nanofluids. Here, systems with various volume fractions were established by utilizing three different approaches. These approaches, denoted as Model 1, Model 2, and Model 3, involved changing the number of nanoparticles (NPs), the size of NPs, and the size of the simulation box as well as number of water molecules, respectively. The thermal conductivity values obtained from the simulations were validated using experimental data. The mechanism of thermal conductivity enhancement was explained by microscopic parameters. The results show that Model 1 was best able to capture the trends in thermal conductivity seen in the experimental data. Further, the thickness of the interfacial layer, estimated from the RDF and number density, was not affected by volume fraction and temperature, which instead determine the interactive forces between the NPs and the base fluid. The mechanism of enhancement of thermal conductivity may be attributed to the fact that the L-J potential energy of the Cu-O pair (−1.21 Kcal . mol−1) is stronger than that of the O-O pair (−0.15 Kcal . mol−1), thereby bringing water molecules into closer contact with the Cu NPs. As the volume fraction and temperature increase, the heat exchange between water molecules inside and outside the interfacial layer also increases, which accelerates the attainment of thermal equilibrium in the nanofluidic system. Thermal conductivity Molecular dynamics Radial distribution function Number density Trajectory Zhai, Yuling verfasserin aut Xuan, Zihao verfasserin aut Guo, Wenjie verfasserin aut Wang, Hua verfasserin aut Enthalten in Journal of molecular liquids New York, NY [u.a.] : Elsevier, 1983 354 Online-Ressource (DE-627)302469664 (DE-600)1491496-7 (DE-576)259483915 1873-3166 nnns volume:354 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2807 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.21 Lösungen Flüssigkeiten Physikalische Chemie AR 354
allfieldsSound	10.1016/j.molliq.2022.118877 doi (DE-627)ELV007695772 (ELSEVIER)S0167-7322(22)00415-9 DE-627 ger DE-627 rda eng 540 DE-600 35.21 bkl Li, Yanhua verfasserin aut Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The requirements for high efficient heat exchange technology in many fields are gradually increasing. Nanofluids have attracted extensive attention due to their enhanced thermal conductivity. Revealing the mechanism of thermal conductivity enhancement is key to the preparation of high-efficiency nanofluids. In molecular dynamics (MD), the accuracy of simulation models is crucial to predict the thermal conductivity of nanofluids. Here, systems with various volume fractions were established by utilizing three different approaches. These approaches, denoted as Model 1, Model 2, and Model 3, involved changing the number of nanoparticles (NPs), the size of NPs, and the size of the simulation box as well as number of water molecules, respectively. The thermal conductivity values obtained from the simulations were validated using experimental data. The mechanism of thermal conductivity enhancement was explained by microscopic parameters. The results show that Model 1 was best able to capture the trends in thermal conductivity seen in the experimental data. Further, the thickness of the interfacial layer, estimated from the RDF and number density, was not affected by volume fraction and temperature, which instead determine the interactive forces between the NPs and the base fluid. The mechanism of enhancement of thermal conductivity may be attributed to the fact that the L-J potential energy of the Cu-O pair (−1.21 Kcal . mol−1) is stronger than that of the O-O pair (−0.15 Kcal . mol−1), thereby bringing water molecules into closer contact with the Cu NPs. As the volume fraction and temperature increase, the heat exchange between water molecules inside and outside the interfacial layer also increases, which accelerates the attainment of thermal equilibrium in the nanofluidic system. Thermal conductivity Molecular dynamics Radial distribution function Number density Trajectory Zhai, Yuling verfasserin aut Xuan, Zihao verfasserin aut Guo, Wenjie verfasserin aut Wang, Hua verfasserin aut Enthalten in Journal of molecular liquids New York, NY [u.a.] : Elsevier, 1983 354 Online-Ressource (DE-627)302469664 (DE-600)1491496-7 (DE-576)259483915 1873-3166 nnns volume:354 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2807 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.21 Lösungen Flüssigkeiten Physikalische Chemie AR 354
language	English
source	Enthalten in Journal of molecular liquids 354 volume:354
sourceStr	Enthalten in Journal of molecular liquids 354 volume:354
format_phy_str_mv	Article
bklname	Lösungen Flüssigkeiten
institution	findex.gbv.de
topic_facet	Thermal conductivity Molecular dynamics Radial distribution function Number density Trajectory
dewey-raw	540
isfreeaccess_bool	false
container_title	Journal of molecular liquids
authorswithroles_txt_mv	Li, Yanhua @@aut@@ Zhai, Yuling @@aut@@ Xuan, Zihao @@aut@@ Guo, Wenjie @@aut@@ Wang, Hua @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	302469664
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language_de	englisch
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author	Li, Yanhua
spellingShingle	Li, Yanhua ddc 540 bkl 35.21 misc Thermal conductivity misc Molecular dynamics misc Radial distribution function misc Number density misc Trajectory Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study
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topic_title	540 DE-600 35.21 bkl Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study Thermal conductivity Molecular dynamics Radial distribution function Number density Trajectory
topic	ddc 540 bkl 35.21 misc Thermal conductivity misc Molecular dynamics misc Radial distribution function misc Number density misc Trajectory
topic_unstemmed	ddc 540 bkl 35.21 misc Thermal conductivity misc Molecular dynamics misc Radial distribution function misc Number density misc Trajectory
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title	Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study
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title_full	Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study
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doi_str_mv	10.1016/j.molliq.2022.118877
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title_sort	establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: a molecular dynamics study
title_auth	Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study
abstract	The requirements for high efficient heat exchange technology in many fields are gradually increasing. Nanofluids have attracted extensive attention due to their enhanced thermal conductivity. Revealing the mechanism of thermal conductivity enhancement is key to the preparation of high-efficiency nanofluids. In molecular dynamics (MD), the accuracy of simulation models is crucial to predict the thermal conductivity of nanofluids. Here, systems with various volume fractions were established by utilizing three different approaches. These approaches, denoted as Model 1, Model 2, and Model 3, involved changing the number of nanoparticles (NPs), the size of NPs, and the size of the simulation box as well as number of water molecules, respectively. The thermal conductivity values obtained from the simulations were validated using experimental data. The mechanism of thermal conductivity enhancement was explained by microscopic parameters. The results show that Model 1 was best able to capture the trends in thermal conductivity seen in the experimental data. Further, the thickness of the interfacial layer, estimated from the RDF and number density, was not affected by volume fraction and temperature, which instead determine the interactive forces between the NPs and the base fluid. The mechanism of enhancement of thermal conductivity may be attributed to the fact that the L-J potential energy of the Cu-O pair (−1.21 Kcal . mol−1) is stronger than that of the O-O pair (−0.15 Kcal . mol−1), thereby bringing water molecules into closer contact with the Cu NPs. As the volume fraction and temperature increase, the heat exchange between water molecules inside and outside the interfacial layer also increases, which accelerates the attainment of thermal equilibrium in the nanofluidic system.
abstractGer	The requirements for high efficient heat exchange technology in many fields are gradually increasing. Nanofluids have attracted extensive attention due to their enhanced thermal conductivity. Revealing the mechanism of thermal conductivity enhancement is key to the preparation of high-efficiency nanofluids. In molecular dynamics (MD), the accuracy of simulation models is crucial to predict the thermal conductivity of nanofluids. Here, systems with various volume fractions were established by utilizing three different approaches. These approaches, denoted as Model 1, Model 2, and Model 3, involved changing the number of nanoparticles (NPs), the size of NPs, and the size of the simulation box as well as number of water molecules, respectively. The thermal conductivity values obtained from the simulations were validated using experimental data. The mechanism of thermal conductivity enhancement was explained by microscopic parameters. The results show that Model 1 was best able to capture the trends in thermal conductivity seen in the experimental data. Further, the thickness of the interfacial layer, estimated from the RDF and number density, was not affected by volume fraction and temperature, which instead determine the interactive forces between the NPs and the base fluid. The mechanism of enhancement of thermal conductivity may be attributed to the fact that the L-J potential energy of the Cu-O pair (−1.21 Kcal . mol−1) is stronger than that of the O-O pair (−0.15 Kcal . mol−1), thereby bringing water molecules into closer contact with the Cu NPs. As the volume fraction and temperature increase, the heat exchange between water molecules inside and outside the interfacial layer also increases, which accelerates the attainment of thermal equilibrium in the nanofluidic system.
abstract_unstemmed	The requirements for high efficient heat exchange technology in many fields are gradually increasing. Nanofluids have attracted extensive attention due to their enhanced thermal conductivity. Revealing the mechanism of thermal conductivity enhancement is key to the preparation of high-efficiency nanofluids. In molecular dynamics (MD), the accuracy of simulation models is crucial to predict the thermal conductivity of nanofluids. Here, systems with various volume fractions were established by utilizing three different approaches. These approaches, denoted as Model 1, Model 2, and Model 3, involved changing the number of nanoparticles (NPs), the size of NPs, and the size of the simulation box as well as number of water molecules, respectively. The thermal conductivity values obtained from the simulations were validated using experimental data. The mechanism of thermal conductivity enhancement was explained by microscopic parameters. The results show that Model 1 was best able to capture the trends in thermal conductivity seen in the experimental data. Further, the thickness of the interfacial layer, estimated from the RDF and number density, was not affected by volume fraction and temperature, which instead determine the interactive forces between the NPs and the base fluid. The mechanism of enhancement of thermal conductivity may be attributed to the fact that the L-J potential energy of the Cu-O pair (−1.21 Kcal . mol−1) is stronger than that of the O-O pair (−0.15 Kcal . mol−1), thereby bringing water molecules into closer contact with the Cu NPs. As the volume fraction and temperature increase, the heat exchange between water molecules inside and outside the interfacial layer also increases, which accelerates the attainment of thermal equilibrium in the nanofluidic system.
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title_short	Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study
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author2	Zhai, Yuling Xuan, Zihao Guo, Wenjie Wang, Hua
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doi_str	10.1016/j.molliq.2022.118877
up_date	2024-07-06T17:08:33.029Z
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Establishment of thermal conductivity model and analysis of enhancement mechanism in nanofluids: A molecular dynamics study

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