ISS: Understanding the Gravity Effect on Flow Boiling Through High-Resolution Experiments and Machine Learning

流动沸腾在陆地和空间应用中的能量-水关系中起着至关重要的作用。这些应用包括热电发电、电力电子和微电子的热管理、水净化以及加热、冷却和空调系统。然而，流动沸腾受到五种主要力的显著影响，例如表面张力、惯性、剪切、蒸汽蒸发动量和重力。通道尺寸和工作条件（如流速、热负荷和温度）的显著变化导致这五种力的不同贡献，从而导致流动沸腾模式和性能的急剧变化。此外，由于高昂的成本和努力，在宽范围的通道尺寸和工作条件下进行流动沸腾实验是极具挑战性的。在该项目中，将开发一套“流动沸腾的深度模型”，以通过结合使用地面和微重力实验以及基于机器学习的技术来了解这些主要力量对流动沸腾的影响。该模型的目的不仅是预测流动沸腾特性，而且还创建流型的合成图像。该项目将为在广泛的工作条件下进行虚拟流动沸腾实验铺平道路。此外，它将提供一个强大的平台，以更全面和更经济的方式研究和设计基于流动沸腾的水能源系统。开发流动沸腾的深层模型的挑战性目标将通过三个主要研究任务来实现。首先，将构建高分辨率实验和数据集。为了确保机器学习的准确和更连续的输入，将通过高分辨率实验在陆地条件下的各种工作条件下建立一个完整和准确的流动沸腾数据库，该实验与国际空间站（ISS）上的NASA流动沸腾和冷凝实验（FBCE）测试部分相同。还将收集关于国际空间站中的地球静止轨道CE的实验数据，以提供高质量的微重力数据集。其次，将通过机器学习实现力对物理变量的影响的建模。将建立一个端到端的多目标混合深度回归（MTHDR）框架，使用从地面和国际空间站实验收集的数据集来预测流动沸腾的物理变量。第三，图像合成将执行两相流模式。将开发基于生成对抗网络（GAN）的模型，以创建两相流模式的图像，从而建立一个框架，以了解甚至量化主要力量对极其复杂的两相流模式的影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Flow boiling plays an essential role in energy-water nexus in both terrestrial and space applications. These applications include thermoelectric power generation, thermal management of power electronics and microelectronics, water purification, and heating, cooling and air-conditioning systems. However, flow boiling is significantly affected by five major forces such as surface tension, inertia, shear, vapor evaporation momentum, and gravitational force. The significant changes of channel sizes and working conditions (such as flow rate, heat load, and temperature) result in various contributions of these five forces and hence drastic changes of flow boiling patterns and performance. In addition, it is extremely challenging to conduct experiments of flow boiling in a wide range of channel sizes and working conditions due to the prohibitive costs and efforts. In this project, a package of “Deep Models of Flow Boiling” will be developed to understand the effects of these major forces on flow boiling through the combined use of ground and microgravity experiments and the machine learning based techniques. The models are aimed to not only predicting flow boiling characteristics but also creating synthetic images of flow patterns. This project will pave the way for performing virtual flow boiling experiments under a wide range of working conditions. Furthermore, it would provide a powerful platform to study and design flow boiling-based water-energy systems in a significantly more comprehensive and economic way.The challenging objective of developing the deep models of flow boiling will be achieved by three major research tasks. First, high-resolution experiments and dataset will be constructed. In order to assure accurate and more continuous inputs for machine learning, a complete and accurate data pool of flow boiling will be built through high-resolution experiments under a wide range of working conditions in terrestrial conditions on a test setup that is identical with the test section of the NASA Flow Boiling and Condensation Experiment (FBCE) on the International Space Station (ISS). Experimental data on the FBCE in ISS will be also collected to provide a quality dataset in microgravity. Second, modeling of the force effect on physical variables will be achieved by machine learning. An end-to-end Multi-Target Hybrid Deep Regression (MTHDR) framework will be built to predict physical variables of flow boiling using the collected datasets from both ground and ISS experiments. Third, image synthesis will be performed for two-phase flow patterns. A generative adversarial network (GAN)-based model will be developed to create images of two-phase flow patterns so as to establish a framework to understand and even quantify the effects of major forces on extremely complex two-phase flow patterns.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.