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

实际的能源转换系统(如内燃机和燃气轮机)和危险现象(如城市和荒野火灾)都由湍流燃烧控制。建立湍流和化学模型，在已知不确定性的情况下作出准确的预测，对于能够设计更清洁和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，目前还不可能估计每个参数的不确定性并预测其对湍流燃烧模拟的影响。该项目旨在建立一个理论基础并开发一个计算框架来应对这些挑战。该项目的开源程序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。本项目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，以评估其可靠性并指导湍流燃烧模型的开发；第二，如何利用实验不仅验证化学模型，而且为其发展提供信息。所提出的工作将验证层流和湍流火焰中普遍运动敏感方向的假设，为利用活动子空间方法对不确定运动参数空间进行降维奠定理论基础。此外，还将开发一个计算框架和开源程序包，结合物理信息的活动子空间估计、自动可微编程和GPU加速，以负担得起的计算成本实现大规模燃烧模拟中的正反向不确定性量化。该框架将被用来论证1)基于正则燃烧测量来约束动力学不确定性，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的可行性。该项目的成功将提供关于流动-化学相互作用对动力学不确定性传播的影响的基本见解，并使实际燃烧系统和使用真实燃料的现象中的不确定性能够有效和易于处理的量化。更广泛地说，该项目中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NSF的法定使命，并通过使用基金会的智力优势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.