Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method
To predict the dendrite morphology and microstructure evolution in the solidification of molten metal, numerically, lattice Boltzmann method (LBM) - cellular automata (CA) model has been developed by integrating the LBM to solve the mass transport by diffusion and convection during solidification an...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lee, Wonjoo [verfasserIn] Jeong, Yuhyeong [verfasserIn] Lee, Jae-Wook [verfasserIn] Lee, Howon [verfasserIn] Kang, Seong-hoon [verfasserIn] Kim, Young-Min [verfasserIn] Yoon, Jonghun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: No title available - 49, Seite 15-24 |
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| Übergeordnetes Werk: |
volume:49 ; pages:15-24 |
| DOI / URN: |
10.1016/j.jmst.2020.01.047 |
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| Katalog-ID: |
ELV004162366 |
|---|
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| 024 | 7 | |a 10.1016/j.jmst.2020.01.047 |2 doi | |
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| 100 | 1 | |a Lee, Wonjoo |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method |
| 264 | 1 | |c 2020 | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a To predict the dendrite morphology and microstructure evolution in the solidification of molten metal, numerically, lattice Boltzmann method (LBM) - cellular automata (CA) model has been developed by integrating the LBM to solve the mass transport by diffusion and convection during solidification and the CA to determine the phase transformation with respect to the solid fraction based on the local equilibrium theory. It is successfully validated with analytic solutions such as Lipton-Glicksman-Kurz (LGK) model in static melt, and Oseen-Ivantsov solution under the fluid flow conditions in terms of tip radius and velocity of the dendrite growth. The proposed LBM-CA model does not only describe different types of dendrite formations with respect to various solidification conditions such as temperature gradient and growth rate, but also predict the primary dendrite arm spacing (PDAS) and the secondary dendrite arm spacing (SDAS), quantitatively, in directional solidification (DS) experiment with Ni-based superalloy. | ||
| 650 | 4 | |a Cellular automata (CA) | |
| 650 | 4 | |a Lattice Boltzmann method (LBM) | |
| 650 | 4 | |a Dendritic growth | |
| 650 | 4 | |a Directional solidification | |
| 700 | 1 | |a Jeong, Yuhyeong |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Jae-Wook |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Howon |e verfasserin |4 aut | |
| 700 | 1 | |a Kang, Seong-hoon |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Young-Min |e verfasserin |4 aut | |
| 700 | 1 | |a Yoon, Jonghun |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t No title available |g 49, Seite 15-24 |w (DE-627)569616417 |x 1005-0302 |7 nnns |
| 773 | 1 | 8 | |g volume:49 |g pages:15-24 |
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2020 |
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2020 |
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10.1016/j.jmst.2020.01.047 doi (DE-627)ELV004162366 (ELSEVIER)S1005-0302(20)30197-3 DE-627 ger DE-627 rda eng Lee, Wonjoo verfasserin aut Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To predict the dendrite morphology and microstructure evolution in the solidification of molten metal, numerically, lattice Boltzmann method (LBM) - cellular automata (CA) model has been developed by integrating the LBM to solve the mass transport by diffusion and convection during solidification and the CA to determine the phase transformation with respect to the solid fraction based on the local equilibrium theory. It is successfully validated with analytic solutions such as Lipton-Glicksman-Kurz (LGK) model in static melt, and Oseen-Ivantsov solution under the fluid flow conditions in terms of tip radius and velocity of the dendrite growth. The proposed LBM-CA model does not only describe different types of dendrite formations with respect to various solidification conditions such as temperature gradient and growth rate, but also predict the primary dendrite arm spacing (PDAS) and the secondary dendrite arm spacing (SDAS), quantitatively, in directional solidification (DS) experiment with Ni-based superalloy. Cellular automata (CA) Lattice Boltzmann method (LBM) Dendritic growth Directional solidification Jeong, Yuhyeong verfasserin aut Lee, Jae-Wook verfasserin aut Lee, Howon verfasserin aut Kang, Seong-hoon verfasserin aut Kim, Young-Min verfasserin aut Yoon, Jonghun verfasserin aut Enthalten in No title available 49, Seite 15-24 (DE-627)569616417 1005-0302 nnns volume:49 pages:15-24 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4753 AR 49 15-24 |
| spelling |
10.1016/j.jmst.2020.01.047 doi (DE-627)ELV004162366 (ELSEVIER)S1005-0302(20)30197-3 DE-627 ger DE-627 rda eng Lee, Wonjoo verfasserin aut Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To predict the dendrite morphology and microstructure evolution in the solidification of molten metal, numerically, lattice Boltzmann method (LBM) - cellular automata (CA) model has been developed by integrating the LBM to solve the mass transport by diffusion and convection during solidification and the CA to determine the phase transformation with respect to the solid fraction based on the local equilibrium theory. It is successfully validated with analytic solutions such as Lipton-Glicksman-Kurz (LGK) model in static melt, and Oseen-Ivantsov solution under the fluid flow conditions in terms of tip radius and velocity of the dendrite growth. The proposed LBM-CA model does not only describe different types of dendrite formations with respect to various solidification conditions such as temperature gradient and growth rate, but also predict the primary dendrite arm spacing (PDAS) and the secondary dendrite arm spacing (SDAS), quantitatively, in directional solidification (DS) experiment with Ni-based superalloy. Cellular automata (CA) Lattice Boltzmann method (LBM) Dendritic growth Directional solidification Jeong, Yuhyeong verfasserin aut Lee, Jae-Wook verfasserin aut Lee, Howon verfasserin aut Kang, Seong-hoon verfasserin aut Kim, Young-Min verfasserin aut Yoon, Jonghun verfasserin aut Enthalten in No title available 49, Seite 15-24 (DE-627)569616417 1005-0302 nnns volume:49 pages:15-24 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4753 AR 49 15-24 |
| allfields_unstemmed |
10.1016/j.jmst.2020.01.047 doi (DE-627)ELV004162366 (ELSEVIER)S1005-0302(20)30197-3 DE-627 ger DE-627 rda eng Lee, Wonjoo verfasserin aut Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To predict the dendrite morphology and microstructure evolution in the solidification of molten metal, numerically, lattice Boltzmann method (LBM) - cellular automata (CA) model has been developed by integrating the LBM to solve the mass transport by diffusion and convection during solidification and the CA to determine the phase transformation with respect to the solid fraction based on the local equilibrium theory. It is successfully validated with analytic solutions such as Lipton-Glicksman-Kurz (LGK) model in static melt, and Oseen-Ivantsov solution under the fluid flow conditions in terms of tip radius and velocity of the dendrite growth. The proposed LBM-CA model does not only describe different types of dendrite formations with respect to various solidification conditions such as temperature gradient and growth rate, but also predict the primary dendrite arm spacing (PDAS) and the secondary dendrite arm spacing (SDAS), quantitatively, in directional solidification (DS) experiment with Ni-based superalloy. Cellular automata (CA) Lattice Boltzmann method (LBM) Dendritic growth Directional solidification Jeong, Yuhyeong verfasserin aut Lee, Jae-Wook verfasserin aut Lee, Howon verfasserin aut Kang, Seong-hoon verfasserin aut Kim, Young-Min verfasserin aut Yoon, Jonghun verfasserin aut Enthalten in No title available 49, Seite 15-24 (DE-627)569616417 1005-0302 nnns volume:49 pages:15-24 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4753 AR 49 15-24 |
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10.1016/j.jmst.2020.01.047 doi (DE-627)ELV004162366 (ELSEVIER)S1005-0302(20)30197-3 DE-627 ger DE-627 rda eng Lee, Wonjoo verfasserin aut Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To predict the dendrite morphology and microstructure evolution in the solidification of molten metal, numerically, lattice Boltzmann method (LBM) - cellular automata (CA) model has been developed by integrating the LBM to solve the mass transport by diffusion and convection during solidification and the CA to determine the phase transformation with respect to the solid fraction based on the local equilibrium theory. It is successfully validated with analytic solutions such as Lipton-Glicksman-Kurz (LGK) model in static melt, and Oseen-Ivantsov solution under the fluid flow conditions in terms of tip radius and velocity of the dendrite growth. The proposed LBM-CA model does not only describe different types of dendrite formations with respect to various solidification conditions such as temperature gradient and growth rate, but also predict the primary dendrite arm spacing (PDAS) and the secondary dendrite arm spacing (SDAS), quantitatively, in directional solidification (DS) experiment with Ni-based superalloy. Cellular automata (CA) Lattice Boltzmann method (LBM) Dendritic growth Directional solidification Jeong, Yuhyeong verfasserin aut Lee, Jae-Wook verfasserin aut Lee, Howon verfasserin aut Kang, Seong-hoon verfasserin aut Kim, Young-Min verfasserin aut Yoon, Jonghun verfasserin aut Enthalten in No title available 49, Seite 15-24 (DE-627)569616417 1005-0302 nnns volume:49 pages:15-24 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4753 AR 49 15-24 |
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10.1016/j.jmst.2020.01.047 doi (DE-627)ELV004162366 (ELSEVIER)S1005-0302(20)30197-3 DE-627 ger DE-627 rda eng Lee, Wonjoo verfasserin aut Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To predict the dendrite morphology and microstructure evolution in the solidification of molten metal, numerically, lattice Boltzmann method (LBM) - cellular automata (CA) model has been developed by integrating the LBM to solve the mass transport by diffusion and convection during solidification and the CA to determine the phase transformation with respect to the solid fraction based on the local equilibrium theory. It is successfully validated with analytic solutions such as Lipton-Glicksman-Kurz (LGK) model in static melt, and Oseen-Ivantsov solution under the fluid flow conditions in terms of tip radius and velocity of the dendrite growth. The proposed LBM-CA model does not only describe different types of dendrite formations with respect to various solidification conditions such as temperature gradient and growth rate, but also predict the primary dendrite arm spacing (PDAS) and the secondary dendrite arm spacing (SDAS), quantitatively, in directional solidification (DS) experiment with Ni-based superalloy. Cellular automata (CA) Lattice Boltzmann method (LBM) Dendritic growth Directional solidification Jeong, Yuhyeong verfasserin aut Lee, Jae-Wook verfasserin aut Lee, Howon verfasserin aut Kang, Seong-hoon verfasserin aut Kim, Young-Min verfasserin aut Yoon, Jonghun verfasserin aut Enthalten in No title available 49, Seite 15-24 (DE-627)569616417 1005-0302 nnns volume:49 pages:15-24 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4753 AR 49 15-24 |
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Enthalten in No title available 49, Seite 15-24 volume:49 pages:15-24 |
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Cellular automata (CA) Lattice Boltzmann method (LBM) Dendritic growth Directional solidification |
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Lee, Wonjoo @@aut@@ Jeong, Yuhyeong @@aut@@ Lee, Jae-Wook @@aut@@ Lee, Howon @@aut@@ Kang, Seong-hoon @@aut@@ Kim, Young-Min @@aut@@ Yoon, Jonghun @@aut@@ |
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2020-01-01T00:00:00Z |
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Lee, Wonjoo |
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Lee, Wonjoo misc Cellular automata (CA) misc Lattice Boltzmann method (LBM) misc Dendritic growth misc Directional solidification Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method |
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Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method Cellular automata (CA) Lattice Boltzmann method (LBM) Dendritic growth Directional solidification |
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misc Cellular automata (CA) misc Lattice Boltzmann method (LBM) misc Dendritic growth misc Directional solidification |
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misc Cellular automata (CA) misc Lattice Boltzmann method (LBM) misc Dendritic growth misc Directional solidification |
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Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method |
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Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method |
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Lee, Wonjoo Jeong, Yuhyeong Lee, Jae-Wook Lee, Howon Kang, Seong-hoon Kim, Young-Min Yoon, Jonghun |
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numerical simulation for dendrite growth in directional solidification using lbm-ca (cellular automata) coupled method |
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Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method |
| abstract |
To predict the dendrite morphology and microstructure evolution in the solidification of molten metal, numerically, lattice Boltzmann method (LBM) - cellular automata (CA) model has been developed by integrating the LBM to solve the mass transport by diffusion and convection during solidification and the CA to determine the phase transformation with respect to the solid fraction based on the local equilibrium theory. It is successfully validated with analytic solutions such as Lipton-Glicksman-Kurz (LGK) model in static melt, and Oseen-Ivantsov solution under the fluid flow conditions in terms of tip radius and velocity of the dendrite growth. The proposed LBM-CA model does not only describe different types of dendrite formations with respect to various solidification conditions such as temperature gradient and growth rate, but also predict the primary dendrite arm spacing (PDAS) and the secondary dendrite arm spacing (SDAS), quantitatively, in directional solidification (DS) experiment with Ni-based superalloy. |
| abstractGer |
To predict the dendrite morphology and microstructure evolution in the solidification of molten metal, numerically, lattice Boltzmann method (LBM) - cellular automata (CA) model has been developed by integrating the LBM to solve the mass transport by diffusion and convection during solidification and the CA to determine the phase transformation with respect to the solid fraction based on the local equilibrium theory. It is successfully validated with analytic solutions such as Lipton-Glicksman-Kurz (LGK) model in static melt, and Oseen-Ivantsov solution under the fluid flow conditions in terms of tip radius and velocity of the dendrite growth. The proposed LBM-CA model does not only describe different types of dendrite formations with respect to various solidification conditions such as temperature gradient and growth rate, but also predict the primary dendrite arm spacing (PDAS) and the secondary dendrite arm spacing (SDAS), quantitatively, in directional solidification (DS) experiment with Ni-based superalloy. |
| abstract_unstemmed |
To predict the dendrite morphology and microstructure evolution in the solidification of molten metal, numerically, lattice Boltzmann method (LBM) - cellular automata (CA) model has been developed by integrating the LBM to solve the mass transport by diffusion and convection during solidification and the CA to determine the phase transformation with respect to the solid fraction based on the local equilibrium theory. It is successfully validated with analytic solutions such as Lipton-Glicksman-Kurz (LGK) model in static melt, and Oseen-Ivantsov solution under the fluid flow conditions in terms of tip radius and velocity of the dendrite growth. The proposed LBM-CA model does not only describe different types of dendrite formations with respect to various solidification conditions such as temperature gradient and growth rate, but also predict the primary dendrite arm spacing (PDAS) and the secondary dendrite arm spacing (SDAS), quantitatively, in directional solidification (DS) experiment with Ni-based superalloy. |
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Numerical simulation for dendrite growth in directional solidification using LBM-CA (cellular automata) coupled method |
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Jeong, Yuhyeong Lee, Jae-Wook Lee, Howon Kang, Seong-hoon Kim, Young-Min Yoon, Jonghun |
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