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

CDS

基本信息

批准号：
1609120
负责人：
Peyman Givi
金额：
$ 36.27万
依托单位：
University of Pittsburgh
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609120&HistoricalAwards=false
关键词：
CDS&amp Appraisal Subgrid Scale Closures

项目摘要

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.

湍流燃烧数据的直接数值模拟（DNS）帮助了工业和政府应用的高级燃烧系统的设计和制造。这样的“大数据”集是如此之大，以至于必须在源头上积极过滤数据，或在短时间后丢弃。该项目采用了一系列策略和计算工具来利用DNS数据来评估动荡燃烧中大型涡模拟（LES）预测的性能。这项研究将为LES成为燃烧系统未来设计和制造的预测的主要手段，同时建立数据共享基础架构，并向各级学生提供教育和外展计划。拟议的研究围绕着协调的5元素策略来处理湍流燃烧直接数值模拟（DNS）数据集，该数据集的大小为数百吨。这些要素包括：（1）使用各种火焰制度中的DNS数据评估当前LES策略；（2）评估LES中SGS关闭的置信区间；（3）开发用于有效计算过滤DNS数据的计算框架；（4）开发基础设施，用于广泛共享DNS数据和注释，可用于评估未来的SGS关闭和LES预测；（5）建议未来的DNS在其他（缺失）制度中进行火焰。 DNS的大数据将从多个来源收集，并将其与未经原始的（完全或部分）的火焰有关。 LES将借助适用于DNS中考虑的每种火焰配置的子网格量表（SGS）封闭。将尝试涵盖文献中确定的所有动荡燃烧制度，并进一步见解LES预测在不同的制度中更好地发挥作用。通过DNS数据对SGS关闭的评估对于评估可以放置在关闭上的信任和信心水平将是无价的。通过整合来自工程师，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高保真预测对动荡的燃烧产生重大影响。这项研究的成功将对燃烧行业和政府（国防部，DOE，NASA）产生重大影响。增强的基础设施将有助于LES成为燃烧系统未来设计和制造的主要预测工具，这将有助于纳入未来的SG封闭。这项研究还将为研究生和本科生，K-12外展活动以及少数群体和代表性不足的群体的招聘。