Collaborative Research: ISS: GOALI: Transients and Instabilities in Flow Boiling and Condensation Under Microgravity

合作研究：ISS：GOALI：微重力下流动沸腾和冷凝的瞬态和不稳定性

• 批准号: 2126462
• 负责人: Joel Plawsky
• 金额: $ 26.97万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126462&HistoricalAwards=false
• 关键词: Collaborative; Research; ISS; GOALI; Transients

Extensive terrestrial studies have been conducted for over seventy years to develop, test and validate predictive theories for flow boiling and condensation as they occur in various applications in the petrochemical, pharmaceutical, biochemical, nuclear, and metallurgical industries. Once a boiling bubble forms on a heated surface, it soon grows large enough to detach from the nucleation site and travel with the flowing fluid. Evolution of boiling bubbles is governed by a delicate balance between the inertial, capillary, drag & lift hydrodynamic, and buoyancy forces. A small inaccuracy in measuring the bubble motion can lead to a large uncertainty in the interpretation of experimental data. Rigorous testing and validation of theories for two-phase flows at the individual bubble level remains a longstanding challenge. By eliminating strong buoyancy effects, long duration microgravity experimentation on the International Space Station's (ISS) Flow Boiling and Condensation Experiment (FBCE) Hardware offers a unique opportunity to study the role of capillary and hydrodynamic forces in the bubble growth and coalescence in a flowing fluid and condensate film. The project team will also participate in educational programs that use space themes to improve interest and skills in STEM by incorporating our project work into lesson plans and laboratory work for the benefit of college and high-school students. The proposed effort seeks to quantify the interactions between the flow field, temperature field, and phase distributions in flow boiling and condensation under transient and unstable conditions in the absence of gravity. Experiments will involve setting the ISS FBCE operating parameters and then recording the pressure and temperature responses within the system and imaging the two-phase flow. The focus is to force transients and instabilities in two-phase flows and to simulate system performance using numerical techniques. The ultimate goal is to see if theoretical predictions for boiling and condensation match up with the measurements in microgravity, and if not, to determine what is missing in the theories that is required to better match the experimental data. The project team will inspect the fine details of bubble dynamics using image analysis techniques, scrutinize the density-wave oscillations in two-phase flows, and correlate those with temperature and pressure-drop oscillations generated by the mechanical and thermal components in the system. Machine learning techniques will use microgravity experimental data to test and validate theoretical predictions models for phase-change fluid flows and enhance guidelines for the design of flow boiling and condensation equipment in terrestrial and gravity-independent applications.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.

已经进行了70多年的广泛陆地研究，以开发，检验和验证在石化，药物，生化，核和冶金工业的各种应用中发生流动沸腾和凝结的预测理论。一旦沸腾的气泡在加热的表面上形成，它很快就会生长到足以从成核位点脱离并与流动的流体一起传播。沸腾气泡的演变受惯性，毛细管，拖动和提升流体动力和浮力之间的微妙平衡。在测量气泡运动方面的少量不准确性会导致实验数据解释的巨大不确定性。 在单个气泡水平上对两相流的理论进行严格的测试和验证仍然是一个长期的挑战。通过消除强大的浮力效应，对国际空间站（ISS）流动沸腾和冷凝实验（FBCE）硬件进行了长时间的微重力实验，为研究毛细管和流体动力的作用提供了一个独特的机会，可以在流动液和冷凝物膜中泡泡生长和合并中的作用。项目团队还将通过将我们的项目工作纳入课程计划和实验室工作，从而利用太空主题来提高STEM的兴趣和技能，从而为大学和高中生的利益，从而提高STEM的兴趣和技能。提出的努力旨在量化流场，温度场和相位分布在不存在重力的情况下在瞬态和不稳定条件下的流量沸腾和凝结中的相互作用。实验将涉及设置ISS FBCE操作参数，然后记录系统内的压力和温度响应并对两相流进行成像。重点是在两相流中强制瞬态和不稳定性，并使用数值技术模拟系统性能。最终目标是查看沸腾和凝结的理论预测是否与微重力的测量值相匹配，如果没有，则确定理论中缺少什么以更好地匹配实验数据所需的理论。项目团队将使用图像分析技术检查气泡动力学的细节，仔细检查两相流中的密度波振荡，并将其与系统中的机械和热成分产生的温度和压力流动振荡相关联。 Machine learning techniques will use microgravity experimental data to test and validate theoretical predictions models for phase-change fluid flows and enhance guidelines for the design of flow boiling and condensation equipment in terrestrial and gravity-independent applications.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.