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.

沸腾流的数值预测是计算流体动力学 (CFD) 中一个具有挑战性的领域。沸腾流在许多行业中都很普遍，从核热水力学到咖啡机设计。然而，CFD 对沸腾的预测通常严重依赖于经验相关性。这些相关性解释了在小于计算网格的尺度上发生的物理效应。对于“核沸腾”尤其如此，其中受热表面几何形状“种子”中的微尺度缺陷发生相变，导致气泡的形成。更复杂的是，气泡和加热表面之间会形成一层微米级层。准确预测通过该微层的传热（及其相变）对于预测气泡动力学至关重要，因此宏观传热速率对工业具有重要意义。