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

化石燃料的燃烧仍然是美国和所有工业国家提供高能量密度应用的电力需求的主要手段。 燃烧的化学过程涉及大量的分子种类。为了充分理解燃烧的物理学，人们必须知道这些物种在给定的操作条件下如何表现和演变。 这是一个涂抹的任务，特别是如果操作涉及湍流混合。 化学反应和湍流之间的相互作用非常复杂，几十年来一直是深入研究的主题。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有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湍流运输的化学物质所需的。 这个项目将系统地解决这些限制，以使涉及化学动力学和非稳态动力学的过程建模。 这样的问题在生物学、大气学、气候学和医学中也很常见。本项目还将培养新一代的研究人员，他们将使用应用数学和计算机科学中最先进的工具对工程问题进行建模和模拟。本研究项目的目标是开发一种动态简化方案，该方案能够对涉及大量物种的反应性湍流进行建模和模拟。 本研究的论点是基于这样一个事实，即湍流反应流中的组成元素是高度相关的，这些相关性可以被利用来减少求解物种输运方程的计算成本。该方法提出了一个从根本上不同的工作流程，从目前的做法，在化学还原方案，在相关的结构中提取的飞行。 由于这个原因，它提供了以下优点：（i）减少不是先验的;因此，它是通用的，不限于特定的操作条件。(ii)它解释了所有重要反应的全部湍流化学相互作用。(iii)由此产生的流形是适合在所有火焰制度的湍流反应预测。(iv)与目前使用的方法相比，该方法有助于显著降低计算成本和存储。该研究跨越了多个学科：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。

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.