EAGER: An Experiment-Based Framework for Turbulent Combustion Modeling

湍流燃烧过程的预测与燃烧装置（如火花点火和柴油发动机，飞机和火箭发动机）的设计和优化有关，提出了关键的挑战。这些挑战可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与化学之间的耦合。使用基于激光的非侵入性方法获得的实验数据越来越多，这为预测湍流燃烧过程提供了新的范例。这些范例是基于构建湍流燃烧模型，从实验数据出发，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传统湍流燃烧模型相关的最有限的假设，从而克服了湍流燃烧过程的复杂性和多尺度性质。目前的努力利用高保真实验数据的出现来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，该方法收集温度和代表燃烧问题复杂性的关键化学物质的测量数据。这种测量以相对较高的频率在小体积或点体积上进行，从而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)开发一种模型还原策略，使用主成分分析生成化学系统的简化描述；2)恢复未测量化学物质的方法，这是完整描述燃料化学所必需的；3)结合统计分布来构建评估火焰结构和燃烧性能所需的数量手段的方法。主成分分析产生化学系统的简化描述，转化为框架的有效计算实现，特别是对于实际的燃烧装置。由于测量涉及对化学系统的部分解释（仅测量了化学物种的一小部分），因此主要的挑战与未测量物种的恢复有关。这种恢复是通过一个随机模拟模型来实现的，该模型混合并反应了单个测量结果，以进化出对缺失物种的估计。该项目将包括对框架要素的验证，以及利用现有实验和计算数据在已开发框架的基础上进行数值研究。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。

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.