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）的挑战领域。沸腾的流程在各种行业中都普遍存在，从核热液压技术到咖啡机设计。但是，CFD沸腾的预测通常在很大程度上依赖于经验相关性。这些相关性解释了比计算网格小的尺度上发生的物理效应。 “成核沸腾”尤其如此 - 在加热表面几何“种子”相变的微尺度瑕疵导致气泡形成。为了进一步复杂化问题，已知微型层层在气泡和加热表面之间形成。通过（和相变的相变）准确预测该微层的传热对于预测气泡动态至关重要，因此宏观尺度的传热率对工业的重要性至关重要。