Boundary conditions for simulations of fluid flow and temperature field during ammonothermal crystal growth—a machine-learning assisted study of autoclave wall temperature distribution

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.