Towards efficient state estimation in wall-bounded flows: hierarchical adjoint data assimilation

实现壁界流中的有效状态估计:分层伴随数据同化

基本信息

  • 批准号:
    2332057
  • 负责人:
  • 金额:
    $ 25.97万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-08-15 至 2026-07-31
  • 项目状态:
    未结题

项目摘要

A novel framework is currently under development, aiming to translate sparse observations of turbulent flows - where a fluid's speed varies chaotically - into accurate, detailed predictions of such flows. Observations from field tests are often limited by space and resolution. Despite these limitations, the predictions derived from such observations are crucial in a myriad of fields such as meteorology, oceanography, and aerospace engineering, yielding rich interpretations from sparse data. The process of merging these observations with complex simulations, known as Data Assimilation, poses significant challenges due to the intricate nature and wide range of scales present in turbulence. To address this, our project plans to develop a Hierarchical Adjoint-based Data Assimilation framework aiming to bridge simulations across multiple resolutions, while taking into account the sparse measurements at different scales. This project could significantly advance our understanding of turbulence and improve the synergy between numerical simulation and field tests, thereby enhancing the fidelity of these simulations. The project will develop open-source software tools encapsulating the Hierarchical Adjoint-based Data Assimilation (HADA) framework. These tools will be made available to researchers and practitioners alike, promoting broader usage and further development. Documentation and tutorials will accompany the software to facilitate ease of use. The technical core of this project revolves around a unique combination of adjoint-based data assimilation techniques and hierarchical methodology. We aim to employ an optimal eddy viscosity model to stabilize the adjoint fields, addressing existing challenges such as energy growth and slow convergence rates typically associated with adjoint-based techniques. The proposed Hierarchical Data Assimilation framework gradually reconstructs flow fields across a hierarchy of spatiotemporal resolutions, using eddy-viscosity fields as a bridge between different grid resolutions. This approach enhances the performance of adjoint-based data assimilation in turbulent flows, while simultaneously reducing the computational demands that conventional methods often incur. By delivering a reliable, efficient, and scalable state estimation tool, this framework can advance inverse problems for engineering and environmental systems. The enhanced understanding of adjoint sensitivity in turbulent flows it provides could significantly improve predictions and decision-making processes in areas such as weather forecasting, climate modeling, pollution dispersion, and other relevant areas. Moreover, the research will also foster interdisciplinary collaborations and serve as an invaluable educational resource.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.
目前正在开发一种新的框架,旨在将湍流的稀疏观测(流体的速度混乱地变化)转化为对这种流动的准确,详细的预测。现场试验的观测结果往往受到空间和分辨率的限制。尽管存在这些局限性,但从这些观测中得出的预测在气象学、海洋学和航空航天工程等无数领域都是至关重要的,可以从稀疏的数据中得到丰富的解释。将这些观测结果与复杂的模拟(称为数据同化)相结合的过程,由于湍流的复杂性质和广泛的尺度范围,带来了重大挑战。为了解决这个问题,我们的项目计划开发一个基于分层伴随的数据同化框架,旨在跨多个分辨率桥接模拟,同时考虑到不同尺度的稀疏测量。该项目可以大大提高我们对湍流的理解,并改善数值模拟和现场测试之间的协同作用,从而提高这些模拟的保真度。该项目将开发封装分层伴随数据同化(HADA)框架的开源软件工具。这些工具将提供给研究人员和从业人员,促进更广泛的使用和进一步发展。文档和教程将随软件一起提供,以便于使用。该项目的技术核心围绕着基于伴随的数据同化技术和分层方法的独特组合。我们的目标是采用一个最佳的涡流粘度模型来稳定伴随场,解决现有的挑战,如能源增长和缓慢的收敛速度通常与伴随为基础的技术。建议的分层数据同化框架逐步重建流场的时空分辨率的层次结构,使用涡粘性场作为不同的网格分辨率之间的桥梁。这种方法提高了伴随数据同化在湍流中的性能,同时减少了传统方法经常产生的计算需求。通过提供一个可靠的,高效的,可扩展的状态估计工具,该框架可以推进工程和环境系统的逆问题。它提供的对湍流中伴随敏感性的增强理解可以显着改善天气预报,气候建模,污染扩散和其他相关领域的预测和决策过程。此外,该研究还将促进跨学科合作,并作为一个宝贵的教育资源。该奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

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Qi Wang其他文献

Density functional theory study on the transition metal atoms encapsulated C20 cage clusters
过渡金属原子封装C20笼团簇的密度泛函理论研究
  • DOI:
    10.1088/2053-1591/aacc89
  • 发表时间:
    2018-06
  • 期刊:
  • 影响因子:
    2.3
  • 作者:
    Zhen Zhao;Zhi Li;Qi Wang
  • 通讯作者:
    Qi Wang
Synthesis and electromagnetic absorption properties of Fe3O4@C nanofibers/bismaleimide nanocomposites
Fe3O4@C纳米纤维/双马来酰亚胺纳米复合材料的合成及电磁吸波性能
Steam Gasification of Catalytic Pyrolysis Char for Hydrogen-rich Gas Production
催化热解炭蒸汽气化生产富氢气体
  • DOI:
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Wu-xing Sun;Yan Zhou;Qi Wang;Shu-rong Wang
  • 通讯作者:
    Shu-rong Wang
Fabrication of visible-light active Fe2O3-GQDs/NF-TiO2 composite film with highly enhanced photoelectrocatalytic performance
具有高度增强光电催化性能的可见光活性Fe2O3-GQDs/NF-TiO2复合薄膜的制备
  • DOI:
    10.1016/j.apcatb.2016.11.046
  • 发表时间:
    2017-05
  • 期刊:
  • 影响因子:
    22.1
  • 作者:
    Qi Wang;Naxin Zhu;Engin Liu;Chenlu Zhang;John C. Crittenden;Yi Zhang;Yanqing Cong
  • 通讯作者:
    Yanqing Cong
Degradation rather than warming delays onset of reproductive phenology of annual Chenopodium glaucum on the Tibetan Plateau
退化而不是变暖延迟了青藏高原一年生灰藜繁殖物候的开始
  • DOI:
    10.1016/j.agrformet.2021.108688
  • 发表时间:
    2021-12
  • 期刊:
  • 影响因子:
    6.2
  • 作者:
    Ji Suonan;Shujuan Cui;Wangwang Lv;Wenying Wang;Bowen Li;Peipei Liu;Huan Hong;Yang Zhou;Qi Wang;Lili Jiang;Tsechoe Dorji;Shiping Wang
  • 通讯作者:
    Shiping Wang

Qi Wang的其他文献

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{{ truncateString('Qi Wang', 18)}}的其他基金

Collaborative Research: SAI-R: Dynamical Coupling of Physical and Social Infrastructures: Evaluating the Impacts of Social Capital on Access to Safe Well Water
合作研究:SAI-R:物理和社会基础设施的动态耦合:评估社会资本对获得安全井水的影响
  • 批准号:
    2228533
  • 财政年份:
    2022
  • 资助金额:
    $ 25.97万
  • 项目类别:
    Standard Grant
The 48th Northeast Bioengineering Conference
第48届东北生物工程大会
  • 批准号:
    2225607
  • 财政年份:
    2022
  • 资助金额:
    $ 25.97万
  • 项目类别:
    Standard Grant
I-Corps: Enhancing Sensory Processing via Noninvasive Neuromodulation
I-Corps:通过无创神经调节增强感觉处理
  • 批准号:
    2232149
  • 财政年份:
    2022
  • 资助金额:
    $ 25.97万
  • 项目类别:
    Standard Grant
Collaborative Research: A Whole-Community Effort to Understand Biases and Uncertainties in Using Emerging Big Data for Mobility Analysis
协作研究:全社区共同努力,了解使用新兴大数据进行出行分析时的偏差和不确定性
  • 批准号:
    2114197
  • 财政年份:
    2021
  • 资助金额:
    $ 25.97万
  • 项目类别:
    Continuing Grant
Collaborative Research: Advancing STEM Online Learning by Augmenting Accessibility with Explanatory Captions and AI
协作研究:通过解释性字幕和人工智能增强可访问性,推进 STEM 在线学习
  • 批准号:
    2118824
  • 财政年份:
    2021
  • 资助金额:
    $ 25.97万
  • 项目类别:
    Standard Grant
SCC-IRG Track 2: Toxic-Free Footprints to Improve Community Health against Respiratory Hazards
SCC-IRG 第 2 轨道:无毒足迹改善社区健康,预防呼吸系统危害
  • 批准号:
    2125326
  • 财政年份:
    2021
  • 资助金额:
    $ 25.97万
  • 项目类别:
    Continuing Grant
RAPID/Collaborative Research: High-Frequency Data Collection for Human Mobility Prediction during COVID-19
RAPID/协作研究:用于 COVID-19 期间人类流动性预测的高频数据收集
  • 批准号:
    2027744
  • 财政年份:
    2020
  • 资助金额:
    $ 25.97万
  • 项目类别:
    Standard Grant
CAREER: Enhancing perception and cognition while minimizing side effects through closed-loop peripheral neural stimulation
职业:通过闭环周围神经刺激增强感知和认知,同时最大限度地减少副作用
  • 批准号:
    1847315
  • 财政年份:
    2019
  • 资助金额:
    $ 25.97万
  • 项目类别:
    Continuing Grant
Collaborative Research: Personalized Systems for Wayfinding for First Responders
协作研究:为急救人员提供寻路的个性化系统
  • 批准号:
    1761950
  • 财政年份:
    2018
  • 资助金额:
    $ 25.97万
  • 项目类别:
    Standard Grant
Collaborative Research: Computational Modeling of How Living Cells Utilize Liquid-Liquid Phase Separation to Organize Chemical Compartments
合作研究:活细胞如何利用液-液相分离来组织化学区室的计算模型
  • 批准号:
    1815921
  • 财政年份:
    2018
  • 资助金额:
    $ 25.97万
  • 项目类别:
    Standard Grant

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