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Article

Boundary Conditions for Simulations of Fluid Flow and Temperature Field during Ammonothermal Crystal Growth—A Machine-Learning Assisted Study of Autoclave Wall Temperature Distribution

1
Institute of Materials and Systems for Sustainability, Nagoya University, Nagoya 464-8601, Japan
2
International Research Fellow of Japan Society for the Promotion of Science, Nagoya University, Nagoya 464-8601, Japan
3
Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Sendai 980-8577, Japan
4
Mitsubishi Chemical Corporation, Ushiku, Ibaraki 300-1295, Japan
5
The Japan Steel Works, Ltd., Muroran, Hokkaido 051-8505, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Evgeniy N. Mokhov
Crystals 2021, 11(3), 254; https://doi.org/10.3390/cryst11030254
Received: 12 February 2021 / Revised: 25 February 2021 / Accepted: 1 March 2021 / Published: 4 March 2021
(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization)
Thermal boundary conditions for numerical simulations of ammonothermal GaN crystal growth are investigated. A global heat transfer model that includes the furnace and its surroundings is presented, in which fluid flow and thermal field are treated as conjugate in order to fully account for convective heat transfer. The effects of laminar and turbulent flow are analyzed, as well as those of typically simultaneously present solids inside the autoclave (nutrient, baffle, and multiple seeds). This model uses heater powers as a boundary condition. Machine learning is applied to efficiently determine the power boundary conditions needed to obtain set temperatures at specified locations. Typical thermal losses are analyzed regarding their effects on the temperature distribution inside the autoclave and within the autoclave walls. This is of relevance because autoclave wall temperatures are a convenient choice for setting boundary conditions for simulations of reduced domain size. Based on the determined outer wall temperature distribution, a simplified model containing only the autoclave is also presented. The results are compared to those observed using heater-long fixed temperatures as boundary condition. Significant deviations are found especially in the upper zone of the autoclave due to the important role of heat losses through the autoclave head. View Full-Text
Keywords: ammonothermal; solvothermal; hydrothermal; natural convection in enclosures; computational fluid dynamics; conjugate heat transfer; machine learning; GaN; crystal growth; supercritical fluid ammonothermal; solvothermal; hydrothermal; natural convection in enclosures; computational fluid dynamics; conjugate heat transfer; machine learning; GaN; crystal growth; supercritical fluid
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MDPI and ACS Style

Schimmel, S.; Tomida, D.; Saito, M.; Bao, Q.; Ishiguro, T.; Honda, Y.; Chichibu, S.; Amano, H. Boundary Conditions for Simulations of Fluid Flow and Temperature Field during Ammonothermal Crystal Growth—A Machine-Learning Assisted Study of Autoclave Wall Temperature Distribution. Crystals 2021, 11, 254. https://doi.org/10.3390/cryst11030254

AMA Style

Schimmel S, Tomida D, Saito M, Bao Q, Ishiguro T, Honda Y, Chichibu S, Amano H. Boundary Conditions for Simulations of Fluid Flow and Temperature Field during Ammonothermal Crystal Growth—A Machine-Learning Assisted Study of Autoclave Wall Temperature Distribution. Crystals. 2021; 11(3):254. https://doi.org/10.3390/cryst11030254

Chicago/Turabian Style

Schimmel, Saskia, Daisuke Tomida, Makoto Saito, Quanxi Bao, Toru Ishiguro, Yoshio Honda, Shigefusa Chichibu, and Hiroshi Amano. 2021. "Boundary Conditions for Simulations of Fluid Flow and Temperature Field during Ammonothermal Crystal Growth—A Machine-Learning Assisted Study of Autoclave Wall Temperature Distribution" Crystals 11, no. 3: 254. https://doi.org/10.3390/cryst11030254

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