CDS&E: Appraisal of Subgrid Scale Closures in Reacting Turbulence via DNS Big Data

湍流燃烧数据的直接数值模拟（DNS）有助于工业和政府应用的先进燃烧系统的设计和制造。 这样的“大数据”集是如此之大，以至于数据必须在源处被积极地过滤，或者在短时间内被丢弃。 该项目采用了一系列的策略和计算工具，利用DNS数据来评估湍流燃烧中大涡模拟（LES）预测的性能。该研究将为LES成为未来燃烧系统设计和制造的主要预测手段铺平道路，同时建立数据共享基础设施，并为各级学生提供教育和推广计划。拟议的研究是建立在一个协调的5元素的策略，用于处理湍流燃烧直接数值模拟（DNS）的数据集的顺序为几十到几百TB的大小。 这些要素包括：（1）在各种火焰状态下使用DNS数据评估当前LES策略;（2）评估LES中SGS闭合的置信区间;（3）开发用于有效计算过滤DNS数据的计算框架;（4）开发用于广泛共享DNS数据和注释的基础设施，其可用于评估未来SGS闭合和LES预测;以及（5）建议未来DNS在其他（缺失的）制度中进行火焰。 DNS大数据将从多个来源收集，并将涉及非预混和预混（完全或部分）火焰。LES将在适用于DNS中考虑的每种火焰配置的亚网格尺度（SGS）闭合的帮助下进行。将尝试涵盖所有的制度，湍流燃烧文献中确定的，并有助于进一步了解LES预测将更好地工作在不同的制度。 通过DNS数据评估SGS关闭对于评估关闭的信任和信心水平将是非常宝贵的。通过整合工程师，计算机科学家和数学家团队的专业知识，该研究有可能对最先进的湍流燃烧高保真预测产生重大影响。 这项研究的成功将对燃气轮机工业和政府（DoD，DOE，NASA）的燃烧产生重大影响。LES成为未来燃烧系统设计和制造的主要预测工具的潜力将得到增强的基础设施的帮助，这将促进未来SGS关闭的整合。该研究还将为研究生和本科生提供研究机会，K-12推广，并从少数民族和代表性不足的群体中招募学生。该项目由计算数据支持的科学与工程（CDS E）计划共同资助。

Design and manufacture of advanced combustion systems for both industrial and government applications is aided by direct numerical simulation (DNS) of turbulent combustion data. Such "big data" sets are so large that the data has to be either aggressively filtered at the source or discarded after a short period of time. The project employs a range of strategies and computational tools for utilizing DNS data to appraise the performance of large eddy simulation (LES) predictions in turbulent combustion. The study will pave the way for LES to become the primary means of predictions for future design and manufacturing of combustion systems, while building a data sharing infrastructure, and providing educational and outreach programs to students at all levels. The proposed research is built around a coordinated 5-element strategy for handling turbulent combustion direct numerical simulation (DNS) data sets of the order of tens to hundreds of terabytes in size. The elements include: (1) Appraisal of current LES strategies using DNS data in various flame regimes; (2) Assessment of confidence intervals of SGS closures in LES; (3) Development of a computational framework for efficient computation of filtered DNS data; (4) Development of infrastructure for broad sharing of DNS data and annotations which can be employed to appraise future SGS closures and LES predictions; and (5) Suggestion for future DNS to be conducted of flames in other (missing) regimes. The DNS big data will be collected from multiple sources and will pertain to both non-premixed and premixed (fully or partially) flames. The LES will be conducted with the aid of subgrid scale (SGS) closures that are applicable for each of the flame configurations considered in DNS. An attempt will be made to cover all of the regimes of turbulent combustion as identified in the literature and contribute further insight as to which LES prediction would work better in the different regimes. Appraisal of the SGS closures via DNS data will be invaluable for assessing the level of trust and confidence that can be placed on the closure. By integrating expertise from a team of engineers, computer scientists, and mathematicians, the study has the potential to make a significant impact in state-of-the-art high-fidelity predictions of turbulent combustion. Success of this research will have a significant impact in combustion, both in the gas-turbine industry and in government (DoD, DOE, NASA). The potential for LES to become the primary predictive tool for future design and manufacturing of combustion systems will be aided by the enhanced infrastructure, which will facilitate incorporation of future SGS closures. The study will also provide research opportunities for both graduate and undergraduate students, K-12 outreach, and recruitment of students from minority and under-represented groups.The project is co-funded by the Computational Data-Enabled Science and Engineering (CDS&E) Program.