Data driven models for accurate prediction of nucleate boiling on oxidised surfaces.

沸腾流动的数值预测是计算流体动力学(CFD)的一个具有挑战性的领域。沸腾流动在广泛的行业中很普遍，从核热液压到咖啡机设计。然而，CFD对沸腾的预测往往严重依赖于经验关联。这些相关性解释了发生在比计算网格更小的尺度上的物理效果。这在“核态沸腾”中尤其如此--加热表面几何形状中的微小缺陷“种子”相变，导致气泡的形成。更复杂的是，已知在气泡和受热表面之间形成了一个微尺度的层。准确预测气泡微观层内的换热(以及相变)对于预测气泡动力学，进而预测宏观尺度的换热速率具有重要意义。

Numerical prediction of boiling flow is a challenging area of Computational Fluid Dynamics (CFD). Boiling flows are prevalent in a broad range of industries, ranging from e.g., nuclear thermal hydraulics to coffee machine design. However, CFD prediction of boiling is often heavily reliant on empirical correlations. These correlations account for physical effects occurring on scales smaller than the computational mesh. This is especially true of "nucleate boiling" - where micro-scale imperfections in the heated surface geometry "seed" phase change leading to the formation of bubbles. To further complicate matters, a micro-scale layer is known to form between the bubble and the heated surface. The accurate prediction of heat transfer through (and phase change of) this micro-layer is crucial to the prediction of bubble dynamics, and hence macro-scale heat transfer rates of importance to industry.