CAREER: Embedded Data Assimilation for Complex Turbulent Reacting Flows

职业：复杂湍流反应流的嵌入式数据同化

• 批准号: 2236904
• 负责人: Jonathan MacArt
• 金额: $ 56.47万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236904&HistoricalAwards=false
• 关键词: CAREER; Embedded; Data; Assimilation; Complex

Addressing the challenges of climate change requires advanced, efficient, low-emission combustion technologies as well as an educated workforce to understand and solve these challenges. A key limiting factor is the inability of current simulation techniques to accurately predict turbulent combustion in the regimes needed for the design of low-emission combustors and sustainable fuels. Due to practical limits on computing resources, the computational simulations used for engineering design rely on simplified mathematical expressions for some aspects of turbulence and chemical physics; these models are almost always disconnected from each other and so do not capture key physical interactions. Recently, efficient numerical methods to calibrate complex models during flow simulations have been developed using techniques from machine learning and constrained optimization. While successful for simple nonreacting turbulent flows, these models have not been applied to highly nonlinear turbulent reacting flows. The principal objective of this project is to develop efficient methods to calibrate models for the missing physics in simulations of complex turbulent reacting flows, including flows in engineering geometries, which will enhance the predictive accuracy of practical calculations. The resulting methods will be useful across many areas of science and engineering and will be made publicly available in an open-source software package. The project will facilitate interdisciplinary partnerships and student education across traditional borders by developing an annual summer symposium on data and modeling for turbulent combustion. The project will also support the development of an education and research program for an underresourced high school, which will encourage broad understanding of energy science and participation in solutions to national and global energy challenges.This project will address the need for accurate, efficient turbulent combustion models by developing turbulence closures and optimization methods for both canonical and complex simulations of turbulent reacting flows. An adjoint-based optimization method will enable efficient optimization of closure models over the Navier–Stokes equations for canonical flows such as turbulent jet flames and wedge-shaped flameholders. The primary challenge in applying adjoint-based optimization is the need for intrusive access to a code’s data structures, which is practically impossible to achieve for general-purpose computational fluid dynamics (CFD) solvers. To address this, a novel co-optimization framework will be developed to leverage both adjoint-based optimization over canonical flows and ensemble Kalman-based (adjoint-free) optimization over geometrically complex flows and experimental data. This combined approach will train models for both the canonical and complex physics while alleviating the current limitations of embedded optimization for general-purpose CFD codes. More broadly, the scientific community is interested in developing methods to leverage large datasets; therefore, this project’s methods have potential to be adopted widely across disciplines. The resulting data, optimization framework, and trained models will be distributed as open-source software to facilitate replication, reuse, and extension by researchers in academia and industry.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.

应对气候变化的挑战需要高级，高效，低排放组合技术以及受过教育的劳动力，以了解和解决这些挑战。一个关键的限制因素是当前模拟技术无法准确预测低排放复合材料和可持续燃料所需的制度中的湍流组合。由于计算资源的实际限制，用于工程设计的计算模拟依赖于湍流和化学物理的某些方面的简单数学表达式；这些模型几乎总是彼此断开的，因此不要捕获关键的物理相互作用。最近，使用机器学习和约束优化的技术开发了在流仿真过程中校准复杂模型的有效数值方法。尽管成功用于简单的非反应湍流，但这些模型尚未应用于高度非线性的湍流反应流。该项目的主要目的是开发有效的方法来校准复杂湍流反应流的模拟中缺失物理的模型，包括工程几何形状的流，这将提高实际计算的预测准确性。最终的方法将在许多科学和工程领域都有用，并将在开源软件包中公开使用。该项目将通过开发有关动荡组合的数据和建模的年度夏季研讨会，促进跨传统边界的跨学科伙伴关系和学生教育。该项目还将支持为资源不足的高中提供教育和研究计划的制定，这将鼓励人们对能源科学的广泛了解，并参与解决国家和全球能源挑战的解决方案。该项目将通过开发湍流封闭和优化方法来满足对精确，有效的湍流组合模型的需求，并为典型和复杂的反应反应量的模拟而进行了优化方法。基于伴随的优化方法将对诸如湍流喷射火焰和楔形火焰持有者等规范流的Navier-Stokes方程进行有效优化。应用基于伴随的优化的主要挑战是需要对代码数据结构的侵入性访问，这对于通用计算流体动力学（CFD）求解器几乎是不可能实现的。为了解决这个问题，将开发一个新颖的合作框架，以利用基于隔离的规范流和基于卡尔曼的集合（无伴随的）优化的优化，对几何复杂的流和实验数据进行了优化。这种组合的方法将训练模型的规范和复杂物理，同时减轻通用CFD代码的嵌入式优化的当前局限性。更广泛地说，科学界有兴趣开发利用大型数据集的方法。因此，该项目的方法有可能在跨学科中广泛采用。最终的数据，优化框架和训练有素的模型将作为开源软件分配，以促进学术界和行业研究人员的复制，再利用和扩展。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。