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CDS&E: Data Management and Visualization in Petascale Turbulent Combustion Simulation

CDS

基本信息

批准号：
1250171
负责人：
Peyman Givi
金额：
$ 50万
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：
2012
资助国家：
美国
起止时间：
2012-09-15 至 2017-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1250171&HistoricalAwards=false
关键词：
CDS&amp Data Management Visualization Petascale

项目摘要

CBET-1250171PI: Peyman Givi, Univ. of PittsburghThis award provides funding for developments of (1) a robust computational method for petascale simulationof turbulent combustion, (2) a scalable data management system for such simulations and (3) a systematicvisual analysis of the generated data. The computational method will be based on the filtered density function(FDF) methodology for large eddy simulation (LES) of complex turbulent flames. The simulation willbe based on a new algorithm, termed ?irregularly portioned Lagrangian Monte Carlo-Finite Difference?(IPLMCFD) which facilitate FDF simulations on massively parallel, up to petascale platforms. Data managementwill be provided by addressing research challenges in management of annotations, managementof workflows and data archiving. Effective data visualization and analysis will be conducted through amachine learning ?feature-extraction? approach. This work crosses the disciplines of engineering and computerscience and expands the state-of-the-art in high fidelity predictions of turbulent reacting flows. At theconclusion of the work, an open source LES code will be provided for use by the public.If successful, the results of this research will have a significant impact in combustion, both in gas-turbineindustry and in government. It is firmly believed that LES will constitute the primary means of predictionsfor future design and manufacturing of combustion systems. Having it coupled with robust and versatile datamanagement and visualization capabilities will be useful for both basic and applied research purposes. Someof the other broader impacts are through involvement of undergraduate students in research and attractingthem to graduate school, K-12 outreach, involvement of high school students in research, and recruitmentof students from minority and under-represented groups.

CBET-1250171 PI：Peyman Givi，University of Bennburg该奖项为以下方面的开发提供资金：（1）用于湍流燃烧的千万亿次模拟的稳健计算方法，（2）用于此类模拟的可扩展数据管理系统以及（3）生成数据的系统可视化分析。计算方法将基于复杂湍流火焰的大涡模拟（LES）的过滤密度函数（FDF）方法。模拟将基于一种新的算法，称为？不规则拉格朗日蒙特卡罗有限差分法（IPLMCFD），可促进大规模并行（高达千万亿次平台）上的FDF模拟。通过解决注释管理、工作流程管理和数据存档方面的研究挑战，可以提供数据管理。有效的数据可视化和分析将通过机器学习进行？特征提取approach.这项工作跨越了工程和计算机科学的学科，并扩大了最先进的高保真预测湍流反应流。在工作结束时，一个开放源代码LES代码将提供给公众使用。如果成功，这项研究的结果将有重大影响的燃烧，无论是在燃气轮机行业和政府。人们坚信大涡模拟将成为未来燃烧系统设计和制造的主要预测手段。将其与强大的和通用的数据管理和可视化功能相结合，将有助于基础和应用研究的目的。其他一些更广泛的影响是通过本科生参与研究并吸引他们进入研究生院，K-12推广，高中生参与研究，以及从少数民族和代表性不足的群体中招募学生。