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

基本信息

  • 批准号:
    2227764
  • 负责人:
  • 金额:
    $ 24.17万
  • 依托单位:
  • 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

项目摘要

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大部分地区变得越来越热和干燥,野火变得越来越普遍。除了破坏自然资源和动物栖息地外,发生在人类附近的野火还会对建筑物造成毁灭性破坏,导致严重的发病率,死亡率和经济损失。野火很难预测和控制,因为它们是混乱的,三维的(3D),对当地的大气条件非常敏感。尽管它们对描述和控制野火很重要,但对火灾中大尺度涡旋、条纹和漩涡的形成、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进预测模型。该项目将开发一种使用摄像机网络的光学传感器,可以对野火相关燃烧过程进行时间分辨的体积(4D)测量。这项工作将开发,验证和应用一种基于背景导向纹影层析成像(BOST)的新型定量4D流动可视化诊断。通过采用流场的简约表示并开发用于断层重建的物理信息闭合,该研究将产生实验室规模野火事件代理的精确4D重建。实验重建将比较相同的火灾模拟,以了解夹带/拓扑结构的相互作用。现有的BOST算法解决了一组耦合的反问题,在多个步骤与非物理封闭,并使用一个粗略的表示的未知领域。所开发的方法将执行这些计算在一个步骤中与一个有效的小波基。此外,这项工作将基于流体动力学和燃烧的控制方程产生一个“物理信息”的BOST闭包,提高精度并促进温度和速度等附加场的量化。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,经过改进的BOST传感器将有助于首次全面表征复杂的野火相关配置中的火灾动力学,从而获得更全面的理解。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。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Estimating density, velocity, and pressure fields in supersonic flows using physics-informed BOS
  • DOI:
    10.1007/s00348-022-03554-y
  • 发表时间:
    2022-08
  • 期刊:
  • 影响因子:
    2.4
  • 作者:
    J. Molnar;L. Venkatakrishnan;B. Schmidt;T. Sipkens;S. J. Grauer
  • 通讯作者:
    J. Molnar;L. Venkatakrishnan;B. Schmidt;T. Sipkens;S. J. Grauer
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Bryan Schmidt其他文献

P184 - Sustained Training and Technical Assistance Improves Nutrition Environments in City of Philadelphia Shelters
  • DOI:
    10.1016/j.jneb.2018.04.213
  • 发表时间:
    2018-07-01
  • 期刊:
  • 影响因子:
  • 作者:
    Bryan Schmidt;Catherine Bartoli;Michelle Gross;Brian Zepka;Vanesa Karamanian
  • 通讯作者:
    Vanesa Karamanian

Bryan Schmidt的其他文献

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{{ truncateString('Bryan Schmidt', 18)}}的其他基金

EAGER: Maximizing Spatial Resolution and Accuracy of PIV with Optical Flow Velocimetry
EAGER:利用光流测速技术最大限度地提高 PIV 的空间分辨率和精度
  • 批准号:
    2306815
  • 财政年份:
    2022
  • 资助金额:
    $ 24.17万
  • 项目类别:
    Standard Grant

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    2007
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