CAREER: Advancing Knowledge of Atomization: Numerical Methods, Physics Extraction, and Reduced-Order Models

雾化是将连贯的液体结构分解成小液滴的过程，并且发生在许多工程设备中，包括燃料喷射、灭火和喷漆系统。 然而，目前缺乏关于雾化射流中发生的物理过程的知识，这阻碍了对雾化系统的改进。 原子化的模拟有可能提供这种知识，但由于产生的大量数据集和提取有用信息的挑战而没有产生影响。 这项工作将：1）先进的数值方法，使更多的系统进行准确的模拟，2）通过提取重要过程的物理进一步模拟的有用性，和3）使用新的知识，以开发基于物理的模型，供工程师设计新的雾化系统。先进的雾化知识将通过三个研究目标实现。 第一个将扩展数值方法的能力，预测破裂的液体片使用长度和时间尺度的基础上的物理和提供一致的和保守的气液和液固界面的运输。第二个目标是通过创建工具从大量数据集中提取原子化物理学，使模拟结果更有用。这些工具将计算关于主要破碎不稳定性和液滴起源的统计数据，这些数据量化了注入的液体如何破碎成小液滴。第三个目标是利用关于原子化的新知识，开发基于物理学的降阶模型。此外，雾化液滴光谱数据库将与其他开发雾化模型的研究人员共享。总的来说，这项工作将提供一套方法，工具和知识，使工程师能够建立更精确的atomizeres.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

Atomization is the process that breaks apart a coherent liquid structure into small droplets and occurs in many engineering devices including fuel injection, fire suppression, and painting systems. However, there is a current lack of knowledge on the physical processes that occur in an atomizing jet, which has impeded improvements to atomization systems. Simulations of atomization have the potential to provide this knowledge, but have not been impactful due to the massive datasets produced and the challenges of extracting useful information. This work will: 1) advance numerical methods allowing accurate simulations of more systems, 2) further the usefulness of simulations by extracting the physics of important processes, and 3) using new knowledge to developing physics-based models for use by engineers designing new atomization systems.Advancing knowledge of atomization will be achieved with three research objectives. The first will extend numerical method capabilities to predict the rupture of a liquid sheet using length and time scales based on physics and provide consistent and conservative transport of gas-liquid and fluid-solid interface. The second objective will make simulations results more useful by creating tools to extract atomization physics from massive datasets. The tools will compute statistics on the primary breakup instability and droplet ancestries that quantify how injected liquid breaks into small droplets. The third objective will develop physics-based reduced-order models using new knowledge on atomization. Additionally, an Atomizing Droplet Ancestry Database will be shared with other researchers developing atomization models. Overall, this work will provide a set of methods, tools, and knowledge that will allow engineers to build more precise atomizers.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.