CAREER: Efficient Uncertainty Quantification in Turbulent Combustion Simulations: Theory, Algorithms, and Computations

能量转换的实际系统（如内燃机和燃气轮机）和危险现象（如城市和荒地火灾）是由湍流燃烧控制的。建立湍流和化学模型，在已知不确定性的情况下做出准确的预测，对于设计更清洁、更好的发动机以及预测灾难性火灾风险以满足全球可持续发展的迫切需要至关重要。对湍流燃烧模拟的不确定性进行量化的最大挑战之一是与复杂化学相关的计算成本，其中涉及数万个参数。因此，目前不可能估计每个参数的不确定性并预测对湍流燃烧模拟的影响。本项目旨在建立一个理论基础，并开发一个计算框架来解决这些挑战。项目中开源软件包的开发将吸引广泛的社区，并为科学、技术、工程和数学（STEM）领域的本科生提供教育机会。本课题旨在解决两个技术问题：第一，如何量化湍流燃烧模拟中的动力学不确定性，以评估湍流燃烧模型的合理性和指导湍流燃烧模型的发展；第二，如何利用实验不仅验证，而且为化学模型的发展提供信息。本文的工作将验证层流和湍流火焰中普遍的动力学敏感方向假设，为利用主动子空间方法降低不确定动力学参数空间的维数奠定理论基础。此外，将开发一个计算框架和开源软件包，结合物理信息的主动子空间估计、自微编程和GPU加速，以可承受的计算成本实现大规模燃烧模拟中的正向和反向不确定性量化。该框架将用于证明1)基于标准燃烧测量约束动力学不确定性的可行性，以及2)量化动力学不确定性对层流和湍流火焰响应的影响。该项目的成功将为流动化学相互作用对动力学不确定性传播的影响提供基本见解，并使实际燃烧系统和实际燃料现象的不确定性量化变得有效和可处理。更广泛地说，该项目开发的框架将为未来在能源应用中整合物理科学和不确定性量化技术提供必要的工具和见解。该奖项反映了美国国家科学基金会的法定使命，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。

Practical systems for energy conversion (such as internal combustion engines and gas turbines) and hazardous phenomena (such as urban and wildland fires) are governed by turbulent combustion. Building turbulent and chemical models that make accurate predictions with known uncertainty are crucial to enable the design of cleaner and better engines as well as the prediction of disastrous fire risks to meet the urgent need for global sustainability. One of the biggest challenges in quantifying the uncertainty for turbulent combustion simulations is the computational cost associated with complex chemistry, where tens of thousands of parameters are involved. Thus, it is currently impossible to estimate the uncertainty for each parameter and to predict the effects on turbulent combustion simulations. This project aims to establish a theoretical foundation and develop a computational framework to address these challenges. The development of the open-source packages in the project will engage a broad community and provide educational opportunities to undergraduate students in the science, technology, engineering, and mathematics (STEM) fields.The current project aims at addressing two technical questions: first, how to quantify the kinetic uncertainty in turbulent combustion simulations to evaluate the soundness and guide the development of turbulent combustion models; and second, how to leverage experiments not only to validate but also inform the development of chemical models. The proposed work will validate the hypothesis of universal kinetic sensitivity direction in laminar and turbulent flames to build the theoretical foundation for reducing the dimensionality of the uncertain kinetic parameter space via the active subspace method. Furthermore, a computational framework and open-source packages will be developed, combining physics-informed active subspace estimation, auto-differentiable programming, and GPU acceleration, to enable the forward and inverse uncertainty quantification in large-scale combustion simulations with affordable computational cost. The framework will be adopted to demonstrate the feasibility to 1) constrain kinetic uncertainties based on canonical combustion measurements, and 2) quantify the effect of kinetic uncertainties on laminar and turbulent flame responses. The success of the project will provide fundamental insights on the impact of flow-chemistry interaction on the propagation of kinetic uncertainty and enable efficient and tractable uncertainty quantification in practical combustion systems and phenomena with real fuels. More broadly, the framework developed in the project will provide essential tools and insights for future integration of physical science and uncertainty quantification techniques in energy applications.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.