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

数据驱动模型，用于准确预测氧化表面上的核沸腾。

• 批准号: 2747174
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2747174
• 关键词: Data; driven; models; accurate; prediction

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.

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。沸腾流动在广泛的工业中普遍存在，例如，核热工水力学到咖啡机设计。然而，计算流体动力学预测沸腾往往严重依赖于经验关联。这些相关性说明了在比计算网格小的尺度上发生的物理效应。这对于“泡核沸腾”尤其如此--其中加热表面几何形状中的微尺度缺陷“种子”相变导致气泡的形成。使问题进一步复杂化的是，已知在气泡和加热表面之间形成微尺度层。准确预测通过该微层的热传递（和相变）对于预测气泡动力学是至关重要的，因此宏观尺度的热传递速率对工业是重要的。