Modeling of two-phase flow transient behavior and instabilities has traditionally been one of the more challenging endeavors in heat transfer research due to the need to distinguish between a wide range of instability modes systems can manifest depending on differences in operating conditions, as well as the difficulty in experimentally determining key characteristics of these phenomena. This study presents a new mechanistic model for Density Wave Oscillations (DWOs) in vertical upflow boiling using conclusions drawn from analysis of flow visualization images and transient experimental results as a basis from which to begin modeling. Counter to many prior studies attributing DWOs to feedback effects between flow rate, pressure drop, and flow enthalpy causing oscillations in position of the bulk boiling boundary, the present instability mode stems primarily from body force acting on liquid and vapor phases in a separated flow regime leading to liquid accumulation in the near-inlet region of the test section, which eventually departs and moves along the channel, acting to re-wet liquid film along the channel walls and re-establish annular, co-current flow. This process was modeled by dividing the test section into three distinct control volumes and solving transient conservation equations for each, yielding predictions of frequencies at which this process occurs as well as amplitude of associated pressure oscillations. Values for these parameters were validated against an experimental database of 236 FC-72 points and show the model provides good predictive accuracy and capably captures the influence of parametric changes to operating conditions.

中文翻译：

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预测垂直上流沸腾中密度波振荡频率和振幅的机械模型

传统上，两相流瞬态行为和不稳定性的建模一直是传热研究中更具挑战性的工作之一，因为需要区分各种不稳定模式，系统可能会根据运行条件的差异而表现出来，以及很难通过实验确定这些现象的关键特征。本研究利用流动可视化图像分析和瞬态实验结果得出的结论作为开始建模的基础，提出了垂直上流沸腾中密度波振荡（DWO）的新机制模型。与许多先前的研究将 DWO 归因于流速、压降和流动焓之间的反馈效应导致整体沸腾边界位置的振荡相比，目前的不稳定模式主要源于作用在分离流态中的液相和气相的体积力导致测试部分近入口区域的液体积聚，最终离开并沿通道移动，起到沿通道壁重新润湿液膜并重新建立环形并流的作用。该过程的建模方法是将测试部分划分为三个不同的控制体积，并求解每个控制体积的瞬态守恒方程，从而预测该过程发生的频率以及相关压力振荡的振幅。这些参数的值根据 236 个 FC-72 点的实验数据库进行了验证，表明该模型提供了良好的预测准确性，并能够捕获参数变化对操作条件的影响。