Collaborative Research: Physics-Informed Background-Oriented Schlieren Tomography of Wildfire-Relevant Combustion

合作研究：野火相关燃烧的物理信息背景导向纹影断层扫描

• 批准号: 2227763
• 负责人: Samuel Grauer
• 金额: $ 28.46万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227763&HistoricalAwards=false
• 关键词: Collaborative; Research; Physics; Informed; Background

Wildfires are becoming more and more common as climate change renders much of the world hotter and drier. Besides the destruction of natural resources and animal habitats, wildfires that occur near human populations cause devastating damage to structures, resulting in significant morbidity, mortality, and financial losses. Wildfires are difficult to anticipate and control because they are chaotic, three-dimensional (3D), and extremely sensitive to local atmospheric conditions. Despite their importance to the characterization and control of wildfires, the formation, evolution, and interaction of large-scale vortices, streaks, and whirls in fires remain poorly understood. New measurement strategies are needed to characterize and understand wildfire dynamics and improve predictive models. This project will develop an optical sensor using a network of cameras that can perform time-resolved, volumetric (4D) measurements of wildfire-relevant combustion processes.This work will develop, validate, and apply a novel, quantitative 4D flow visualization diagnostic based on background-oriented schlieren tomography (BOST). By employing a parsimonious representation of the flow field and developing a physics-informed closure for tomographic reconstruction, the research will produce accurate 4D reconstructions of laboratory-scale wildfire event surrogates. Experimental reconstructions will be compared to simulations of the same fires to understand entrainment/topology interactions. Existing BOST algorithms solve a set of coupled inverse problems in multiple steps with non-physical closures and use a coarse representation of the unknown field. The developed method will perform these calculations in one step with an efficient wavelet basis. Further, this work will produce a “physics-informed” BOST closure based on the governing equations of fluid dynamics and combustion, enhancing accuracy and facilitating the quantification of additional fields like temperature and velocity. Simulations and measurements will be cross validated using simple solid fuel fires. Moreover, the refined BOST sensor will facilitate comprehensive characterization of fire dynamics in complex, wildfire-relevant configurations for the first time, leading to a more complete understanding.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.

随着气候变化使世界上大部分地区变得更热，驱动力，野火变得越来越普遍。除了破坏自然资源和动物栖息地外，在人口附近发生的野火对结构造成了破坏的破坏，从而导致了严重的发病率，死亡率和财务损失。野火很难预测和控制，因为它们是混乱的，三维（3D），并且对当地大气条件非常敏感。尽管它们对野火的特征和控制重要性，但大规模涡流，条纹和火灾中的旋转的形成，演变和相互作用仍然知之甚少。需要新的测量策略来表征和理解野火动态并改善预测模型。该项目将使用一个可以对野火相关的组合过程进行时间分辨的，体积（4D）测量的相机网络开发光学传感器。这项工作将开发，验证和应用基于背景面向背景的Schlieren Smography（Bost）的新型，定量的4D流动可视化诊断。通过对流场的典型表示并开发出具有物理性的封闭层造影重建，这项研究将产生实验室尺度野火事件代理的准确4D重建。实验重建将与对相同火灾的模拟进行比较，以了解入口/拓扑相互作用。现有的BOST算法在多个步骤中求解了一组耦合的反问题，并具有非物理封闭的多个步骤，并使用未知场的粗略表示。开发的方法将以有效的小波为基础执行这些计算。此外，这项工作将基于流体动力学和组合的治理方程，产生“物理知识”的BOST闭合，提高准确性并支持其他场（如温度和速度）的数量。模拟和测量将使用简单的固体燃料火灾进行交叉验证。此外，精制的BOST传感器将首次促进复杂，与野火相关的配置中火力动态的全面表征，从而更加完整地理解。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来支持的。