ISS: Transient Behavior of Flow Condensation and Its Impacts on Condensation Rate

冷凝器用于排出热量并将液体返回/收集到蒸发器/锅炉，是任何汽液两相系统的重要组成部分，如热管和蒸汽室、水回收和收集、蒸汽压缩系统和朗肯循环动力系统。然而，由于主要的膜状冷凝模式，即蒸汽以覆盖表面的薄膜的形式冷凝，在工业实践中，冷凝换热速率通常比沸腾或蒸发低几倍，导致冷凝器尺寸过大和重量过重。高效的流动冷凝是非常可取的。两相流动的不稳定性或流动沸腾和冷凝过程中的振荡会对系统产生不利影响。尽管人们对流动沸腾中的两相振荡现象进行了大量的研究，但对两相冷凝流动不稳定性的研究还很少。该项目将提供宝贵的知识，并导致新的控制策略，以实现理想的流动模式。如果成功，该研究项目不仅可以提高凝汽器运行的稳定性，而且如果能够适当地利用两相振荡来强化流动凝结，还可以提高效率。该项目还提供了一个独特的机会，促进热科学、空间技术和机器学习之间的跨学科合作。这些类型的合作将极大地惠及热学、机器学习、航天工业、陆地水能产业、利益相关者和科技教育等领域。研究目的是了解地面和微重力环境中流动凝结的瞬变行为及其对流动凝结速率的影响。这项研究中获得的新知识将促进对支配流动凝结振荡的操作参数的理解。研究目标可通过五个任务来实现：(A)系统地表征流动冷凝的瞬变行为；(B)了解不同的两相流流型与蒸汽流量变化率和/或冷却速率的关系；(C)确定产生强烈两相振荡的操作条件范围；(D)表征振荡模式对流动冷凝速率的影响，重点是强化流动冷凝；以及(E)开发基于机器学习的模型，以准确预测流动冷凝、流动冷凝速率和压降的瞬时行为，这些模型比传统的两阶段实验更容易为研究社区、行业、学生和公众所接受。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Condensers, which are used to reject heat and return/collect liquid to the evaporators/boilers, are essential parts of any liquid-vapor two-phase systems such as heat pipes and vapor chambers, water recovery and harvest, vapor compression systems, and Rankine cycle power systems. However, due to the dominant filmwise condensation mode, where vapors are condensed in the form of a film that covers the surface, in industry practice, condensation heat transfer rate is typically several folds lower than that of boiling or evaporation, leading to oversized- and overweighted-condensers. Highly efficient flow condensation is greatly desirable. Two-phase flow instabilities or oscillations in flow boiling and condensation can have detrimental effects on the system. Although extensive efforts have been taken to understand and manage the two-phase oscillation in flow boiling, only a few studies have been conducted in understanding two-phase condensing flow instabilities. This project would provide valuable knowledge and lead to novel control strategies in achieving the desirable flow pattern. If successful, this research project would improve not only the stability of condenser operations, but also the efficiency if two-phase oscillations can be properly utilized to enhance flow condensation. This project also offers a unique opportunity to promote interdisciplinary collaborations among thermal science, space technology, and machine learning. These types of collaborations would greatly benefit the communities of thermal science, machine learning, space industry, terrestrial water-energy industries, stakeholders, and science and technology education.The research objectives are to understand transient behaviors of flow condensation and their impacts on flow condensation rate in both ground and microgravity environments. New knowledge obtained in this study would advance understandings of operation parameters that govern flow condensation oscillations. The research objectives can be realized in five tasks: (a) systematically characterizing transient behaviors of flow condensation, (b) understanding the dependence of different two-phase flow patterns on the vapor flow changing rate and/or cooling rate, (c) identifying the range of operation conditions that generate intensive two-phase oscillations; (d) characterizing the influence of oscillation modes on flow condensation rate with an emphasis on enhancing flow condensation; and (e) developing machine-learning-based models to accurately predict transient behaviors of flow condensation, flow condensation rate and pressure drop, which are more accessible than the traditional two-phase experiments to research communities, industries, students, and the general public.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.