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，在许多工程设备中都有应用，包括燃油喷射、灭火和喷漆系统。然而，目前对雾化射流中发生的物理过程缺乏了解，这阻碍了雾化系统的改进。雾化模拟有可能提供这些知识，但由于产生的大量数据集和提取有用信息的挑战，尚未产生影响。这项工作将：1)推进数值方法，允许对更多系统进行精确的模拟；2)通过提取重要过程的物理来进一步提高模拟的实用性；3)利用新知识开发基于物理的模型，供工程师设计新的雾化系统使用。推进雾化的知识将实现三个研究目标。第一个将扩展数值方法的能力，使用基于物理的长度和时间尺度来预测液片的破裂，并提供一致和保守的气液和流固界面输运。第二个目标将通过创建工具来从大量数据集中提取原子化物理，使模拟结果更有用。该工具将计算初级破碎不稳定性和液滴起源的统计数据，从而量化注入的液体如何破碎成小液滴。第三个目标是利用原子化的新知识开发基于物理的降阶模型。此外，原子化液滴祖先数据库将与其他开发原子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，使工程师能够建造更精确的雾化器。该奖项反映了美国国家科学基金会的法定使命，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。

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.