Real-Time and Adaptive Chemical Kinetic Model Reduction Coupled with Turbulence

在这个国家和所有工业国家，燃烧化石燃料仍然是提供高能量密度应用所需电力的主要手段。燃烧的化学涉及到大量的分子物种。要完全理解燃烧的物理学，必须知道这些物种在给定的操作条件下的行为和演化。这是一项艰巨的任务，特别是当操作涉及湍流混合时。化学反应和湍流之间的相互作用是非常复杂的，几十年来一直是一个热门的研究课题。近年来，更容易获得超级计算资源使研究界能够开发复杂而高效的计算工具来模拟复杂的化学反应。然而，计算能力仍然远远低于包括化学物质的湍流传输所需的计算能力。该项目将系统地解决这些限制，以便能够对涉及化学动力学和非稳定动力学的过程进行建模。这样的问题在生物学、大气、气候学和医学中也很常见。该项目还将帮助培训新一代研究人员，使用最先进的应用数学和计算机科学工具对工程问题进行建模和模拟。该研究项目的目标是开发一种即时简化方案，使涉及大量物种的反应湍流的建模和模拟成为可能。本论文的研究是基于湍流反应流中各组分之间高度相关的事实，这种相关性可以被用来降低求解物质输运方程的计算成本。该方法提出了一种与目前化学还原方案中的做法完全不同的工作流程，因为相关结构是动态提取的。因此，它提供了以下优点：(I)减量不是先验的；因此，它是一般性的，不限于特定的运行条件。(2)它解释了包含所有重要反应的完全湍流化学相互作用。(Iii)所得到的流形适用于所有火焰区域的湍流反应预测。(4)与目前使用的方法相比，该方法大大减少了计算成本和存储空间。这项研究跨越了几个学科：湍流燃烧、数据驱动建模、降阶建模和高性能计算。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。

Combustion of fossil fuels remains the primary means of providing power needs of high energy density applications in this country and all industrial nations. The chemistry of combustion involves a very large number of molecular species. To fully understand the physics of combustion, one must know how these species behave and evolve in a given operating condition. This is a dauting task, especially if the operation involves turbulent mixing. The interaction between chemical reaction and turbulence is very complex and has been a subject of intense study for over many decades. In recent years, easier access to supercomputing resources has enabled the research community to develop sophisticated and efficient computational tools to model complex chemical reactions. However, the computational power remains far less than what is needed for including turbulent transport of chemical species. This project will systematically address these limitations in order to enable modeling of processes involving chemical kinetics and unsteady dynamics. Such problems are also common in biology, atmospherics, climatology, and medicine. This project will also help train a new generation of researchers with the most modern tools in applied mathematics and computer science for modeling and simulation of engineering problems.The goal of this research project is to develop an on-the-fly reduction scheme that enables modeling and simulation of reactive turbulent flows involving a very for a large number of species. The thesis of this research is predicated on the fact that the compositional elements in turbulent reactive flow are highly correlated, and these correlations can be exploited to reduce the computational cost of solving species transport equations. The methodology presents a fundamentally different workflow from current practice in chemistry reduction schemes, in that the correlated structures are extracted on the fly. For that reason, it offers the following advantages: (i) The reduction is not done a priori; therefore, it is general and not restricted to a particular operating condition. (ii) It accounts for the full turbulent chemistry interactions with inclusion of all of the important reactions. (iii) The resulting manifolds are suitable for turbulent reactive predictions in all flame regimes. (iv) The method facilitates a significant reduction of the computational cost and storage as compared to those currently in use. This research crosses several disciplines: turbulent combustion, data driven modeling, reduced order modeling, and high-performance computing.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.