Collaborative Research: Adaptive Data Assimilation for Nonlinear, Non-Gaussian, and High-Dimensional Combustion Problems on Supercomputers

合作研究：超级计算机上非线性、非高斯和高维燃烧问题的自适应数据同化

• 批准号: 1723191
• 负责人: Xinfeng Gao
• 金额: $ 14万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1723191&HistoricalAwards=false
• 关键词: Collaborative; Research; Adaptive; Data; Assimilation

Clean combustion is in urgent need for sustainability due to its direct and intimate connection with tropospheric air pollution, energy security, and climate change today. However, the combustion community still lacks a theoretical description that is accurate enough to make turbulent combustion models rigorous and quantitative for engineering application. Data assimilation, a powerful and versatile methodology, can maximize the utility of information from model predictions and measurements, and help reduce the uncertainty of the state of the modeling system. The project will create a new adaptive data assimilation methodology by confronting the mathematical challenges of applying data assimilation to combustion. This research will ultimately lead to the development of accurate, tractable, and predictive models for combustion engineering, which will help reduce the turn-around time for the expensive design and development cycle of clean combustion technologies. Software resulting from the project will be applicable, beneficial, and accessible to the broad research communities of combustion, fire, plasma, or biofluids.Combustion is a new application for data assimilation. Most, if not all, data-assimilation problems in combustion are strongly nonlinear, likely non-Gaussian, and very high-dimensional. This presents challenges to current data assimilation methods. Although nonlinear non-Gaussian data assimilation is becoming reality in some fields (e.g., meteorology, oceanography, and geosciences) with increasing computer power and advances in mathematical and statistical techniques, these data assimilation methods, unfortunately, are often subject to one or more constraints. For example, among successful data assimilation methods that can address nonlinearity and non-Gaussianity are the maximum likelihood ensemble filter (MLEF) and implicit particle filters (IPF). However, the former still implicitly assumes Gaussian probability density distribution at some points in the algorithm and the latter can be catastrophically expensive for high-dimensional problems. Therefore, to ensure a successful data assimilation application to combustion problems, new data assimilation methods must be created to effectively address nonlinearity and non-Gaussianity, efficiently solve high-dimensional systems, and simultaneously achieve high performance on supercomputers. This project aims to develop a new adaptive data assimilation method based on MLEF and IPF for nonlinear, non-Gaussian, high-dimensional systems. The new method will be demonstrated on a large-eddy simulation of flame in a slot burner of interest to combustion science and engineering.

由于清洁燃烧与当今对流层空气污染、能源安全和气候变化有着直接而密切的联系，因此迫切需要可持续发展。然而，燃烧界仍然缺乏足够准确的理论描述来使湍流燃烧模型在工程应用中变得严格和定量。数据同化是一种强大且通用的方法，可以最大限度地利用模型预测和测量的信息，并有助于降低建模系统状态的不确定性。该项目将通过应对将数据同化应用于燃烧的数学挑战，创建一种新的自适应数据同化方法。这项研究最终将导致燃烧工程准确、易于处理和预测的模型的开发，这将有助于减少清洁燃烧技术昂贵的设计和开发周期的周转时间。该项目产生的软件将对燃烧、火灾、等离子体或生物流体的广泛研究社区适用、有益且可访问。燃烧是数据同化的新应用程序。大多数（如果不是全部）燃烧中的数据同化问题都是强非线性的，可能是非高斯的，并且维度非常高。这对当前的数据同化方法提出了挑战。尽管随着计算机能力的增强以及数学和统计技术的进步，非线性非高斯数据同化在某些领域（例如气象学、海洋学和地球科学）正在成为现实，但不幸的是，这些数据同化方法通常受到一种或多种限制。例如，可以解决非线性和非高斯性的成功数据同化方法包括最大似然集成滤波器（MLEF）和隐式粒子滤波器（IPF）。然而，前者仍然隐含地假设算法中某些点的高斯概率密度分布，而后者对于高维问题可能会造成灾难性的代价。因此，为了确保数据同化成功应用于燃烧问题，必须创建新的数据同化方法来有效解决非线性和非高斯性，有效解决高维系统，同时在超级计算机上实现高性能。该项目旨在针对非线性、非高斯、高维系统开发一种基于 MLEF 和 IPF 的新型自适应数据同化方法。新方法将在燃烧科学和工程感兴趣的槽式燃烧器中的火焰大涡模拟中进行演示。

项目成果

CFD Modeling of Bluff-Body Stabilized Premixed Flames with Data Assimilation

利用数据同化对钝体稳定预混火焰进行 CFD 建模

• DOI: 10.2514/6.2020-0352
• 发表时间: 2020
• 期刊: AIAA meeting papers on disc
• 作者: Wang, Y.;Walters, S.;Overton, N.;Guzik, S. M.;Gao, X.
• 通讯作者: Gao, X.

Assessing Stretched-Vortex Subgrid-Scale Models in Finite Volume Methods for Unbounded Turbulent Flows

评估无界湍流有限体积方法中的拉伸涡亚网格尺度模型

• DOI: 10.1007/s10494-020-00206-1
• 发表时间: 2020
• 期刊: Flow turbulence and combustion
• 影响因子: 2.4
• 作者: Walters, Sean;Gao, Xinfeng;Johansen, Hans;Guzik, Stephen
• 通讯作者: Guzik, Stephen

Performance assessment of the maximum likelihood ensemble filter and the ensemble Kalman filters for nonlinear problems

针对非线性问题的最大似然集成滤波器和集成卡尔曼滤波器的性能评估

• DOI: 10.1007/s40687-022-00359-7
• 发表时间: 2022
• 期刊: Research in the Mathematical Sciences
• 影响因子: 1.2
• 作者: Wang, Yijun;Zupanski, Milija;Tu, Xuemin;Gao, Xinfeng
• 通讯作者: Gao, Xinfeng

The maximum likelihood ensemble smoother for the Kuramoto–Sivashinsky equation

Kuramoto-Sivashinsky 方程的最大似然系综平滑器

• DOI: 10.1093/imamat/hxac026
• 发表时间: 2022
• 期刊: IMA Journal of Applied Mathematics
• 影响因子: 1.2
• 作者: Hurst, Christopher;Zupanski, Milija;Gao, Xinfeng
• 通讯作者: Gao, Xinfeng

High‐order implicit‐explicit additive Runge–Kutta schemes for numerical combustion with adaptive mesh refinement

• DOI: 10.1002/fld.5084
• 发表时间: 2022-03
• 期刊: International Journal for Numerical Methods in Fluids
• 影响因子: 1.8
• 作者: J. Christopher;S. Guzik;Xinfeng Gao