EAGER: An Experiment-Based Framework for Turbulent Combustion Modeling

与燃烧装置（例如火花点火和柴油发动机、飞机和火箭发动机）的设计和优化相关的湍流燃烧过程的预测提出了关键挑战。这些挑战可以归因于预测湍流的复杂性，涉及数千种化学物质的化学反应以及湍流和化学之间的耦合。使用基于激光的非侵入式方法获得的实验数据的日益增加的可用性使得能够预测湍流燃烧过程的新范例。这些范例基于从实验数据开始构建湍流燃烧模型，所述实验数据可以通过光学访问在燃烧装置中进行。这种基于数据的范例克服了与湍流燃烧的传统模型相关联的最有限的假设，以克服复杂的，多尺度的湍流燃烧processes.The目前的努力利用高保真实验数据的出现，开发一种新的基于数据的湍流燃烧建模框架。该方法专门针对一种实验方法，该方法收集代表燃烧问题复杂性的温度和关键化学物质的测量值。这种测量是在小体积或点体积上以相对高的频率进行的，这使得能够充分评估测量量的统计矩和分布。新的框架要素包括：1）开发模型简化策略，以使用主成分分析生成化学系统的简化描述，2）用于恢复未测量的化学物质的方法，这是燃料化学的完整描述所需的，和3）结合统计分布的方法来构造评估火焰结构和燃烧性能所需的量的手段。主成分分析产生的化学系统，这转化为一个有效的计算实现的框架，特别是对于实际的燃烧装置的减少的描述。由于测量涉及化学系统的部分帐户（只有一小部分的化学物种被测量），一个主要的挑战是有关的恢复未测量的物种。这种恢复是通过一个随机模拟模型，混合和反应的个别测量，以发展的估计失踪的物种。该项目将涉及两个验证的框架元素，以及数值研究的基础上开发的框架，使用现有的实验和计算数据。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

The prediction of turbulent combustion processes that is relevant to the design and optimization of combustion devices (e.g. spark ignition and diesel engines, aircraft and rocket engines) presents critical challenges. These challenges can be attributed to the complexity of predicting turbulent flows, chemical reactions involving thousands of chemical species and the coupling between turbulence and chemistry. The increasing availability of experimental data obtained using laser-based non-intrusive methods has enabled new paradigms for predicting turbulent combustion processes. These paradigms are based on constructing turbulent combustion models starting from experimental data that can be carried out in combustion devices through optical access. Such data-based paradigms overcome the most limiting assumptions associated with traditional models for turbulent combustion to overcome the complex, multiscale nature of turbulent combustion processes.The present effort exploits the emergence of high-fidelity experimental data to develop a novel data-based modeling framework for turbulent combustion. The approach targets specifically an experimental approach that gathers measurements for temperature and key chemical species that represent the complexity of the combustion problem. Such measurements are carried out on small or point volumes at relatively high frequencies that enable an adequate assessment of the statistical moments and distributions of the measured quantities. The novel framework elements include: 1) the development of a model reduction strategy to generate a reduced description of the chemical system using principal component analysis, 2) methods for recovering unmeasured chemical species, which are needed for a complete description of the fuel chemistry, and 3) methods for combining statistical distributions to construct means of quantities needed to assess the flame structure and the combustion performance. Principal component analysis produces a reduced description of the chemical system, which translates into an efficient computational implementation of the framework, especially for practical combustion devices. Since the measurements involve a partial account for the chemical system (only a fraction of the chemical species is measured), a principal challenge is related to the recovery of the unmeasured species. This recovery is achieved through a stochastic simulation model that mixes and reacts the individual measurements to evolve an estimate of the missing species. The project will involve both validation of the framework elements as well as numerical studies based on the developed framework using available experimental and computational data.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.