Abstract We apply continuum models to analyze phase change, heat transfer, fluid flow, solute transport, and segregation in order to understand prior neutron imaging observations of the vertical gradient freeze growth of Eu-doped BaBrCl. The models provide a rigorous framework in which to understand the mechanisms that are responsible for the complicated evolution of interface shape and dopant distribution in the growth experiment. We explain how a transition in the solid/liquid interface shape from concave to convex is driven by changes in radial heat transfer caused by furnace design. We also provide a mechanistic explanation of how dynamic growth conditions and changes of the flow structure in the melt result in complicated segregation patterns in this system. A growth pause caused by controller lock-up is shown to result in a band of solute depletion in accordance with classical theory. However, changing flow patterns during growth result in a non-monotonic axial distribution of solute that cannot be explained by simple application of classical segregation models. We assert that the approach presented here, namely the use of rigorous models in conjunction advanced diagnostics, such as neutron imaging, provides an exciting path forward for process optimization and control, accelerating the incremental advances that have, in the past, typically relied on empiricism, experience, and intuition.

中文翻译：

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计算建模和中子成像以了解 BaBrCl:Eu 垂直梯度冻结生长过程中的界面形状和溶质分离

摘要 我们应用连续介质模型来分析相变、传热、流体流动、溶质输运和偏析，以了解对 Eu 掺杂的 BaBrCl 垂直梯度冻结生长的先前中子成像观察。这些模型提供了一个严格的框架，可以在其中了解导致生长实验中界面形状和掺杂剂分布复杂演变的机制。我们解释了固/液界面形状从凹面到凸面的转变是如何由炉子设计引起的径向传热变化驱动的。我们还提供了关于动态生长条件和熔体中流动结构的变化如何导致该系统中复杂的分离模式的机械解释。根据经典理论，显示由控制器锁定引起的生长暂停导致溶质消耗带。然而，生长过程中流动模式的变化会导致溶质的非单调轴向分布，这无法通过简单应用经典分离模型来解释。我们断言，这里介绍的方法，即使用严格的模型结合先进的诊断，如中子成像，为过程优化和控制提供了一条令人兴奋的前进道路，加速了过去通常依赖于经验主义的渐进式进步、经验和直觉。生长过程中流动模式的变化导致溶质的非单调轴向分布，这无法通过经典分离模型的简单应用来解释。我们断言，这里介绍的方法，即使用严格的模型结合先进的诊断，如中子成像，为过程优化和控制提供了一条令人兴奋的前进道路，加速了过去通常依赖于经验主义的渐进式进步、经验和直觉。生长过程中流动模式的变化导致溶质的非单调轴向分布，这无法通过经典分离模型的简单应用来解释。我们断言，这里介绍的方法，即使用严格的模型结合先进的诊断，如中子成像，为过程优化和控制提供了一条令人兴奋的前进道路，加速了过去通常依赖于经验主义的渐进式进步、经验和直觉。