材料科学
增长率
体积流量
马朗戈尼效应
Crystal(编程语言)
钢筋
过程(计算)
最优控制
晶体生长
计算机模拟
流量(数学)
流量控制(数据)
电磁线圈
机械
光学
复合材料
对流
控制理论(社会学)
计算机科学
数学
物理
控制(管理)
数学优化
电气工程
工程类
热力学
电信
几何学
人工智能
操作系统
程序设计语言
作者
Lei Wang,Atsushi Sekimoto,Yuto Takehara,Yasunori Okano,Toru Ujihara,S. Dost
出处
期刊:Crystals
[MDPI AG]
日期:2020-09-07
卷期号:10 (9): 791-791
被引量:9
标识
DOI:10.3390/cryst10090791
摘要
We have developed a reinforcement learning (RL) model to control the melt flow in the radio frequency (RF) top-seeded solution growth (TSSG) process for growing more uniform SiC crystals with a higher growth rate. In the study, the electromagnetic field (EM) strength is controlled by the RL model to weaken the influence of Marangoni convection. The RL model is trained through a two-dimensional (2D) numerical simulation of the TSSG process. As a result, the growth rate under the control of the RL model is improved significantly. The optimized RF-coil parameters based on the control strategy for the 2D melt flow are used in a three-dimensional (3D) numerical simulation for model validation, which predicts a higher and more uniform growth rate. It is shown that the present RL model can significantly reduce the development cost and offers a useful means of finding the optimal RF-coil parameters.
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