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On-the-fly postprocessing and feature extraction of flame and flow properties obtained by Direct Numerical Simulations

通过直接数值模拟获得的火焰和流动特性的动态后处理和特征提取

基本信息

批准号：
250921653
负责人：
Professor Dr.-Ing. Holger Theisel
金额：
--
依托单位：
Institut für Simulation und Graphik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2018-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/250921653?language=en
关键词：
fly postprocessing feature extraction flame

项目摘要

Direct Numerical Simulations (DNS) are presently recognized as the best possible solution to investigate numerically turbulent flows. However, DNS at high Reynolds numbers require billions of grid points and are pursued during thousands of time iterations. When considering even more complex flows involving chemical reactions, many variables have to be analyzed and correlated in space and time in order to test and develop reduced models. This leads to Terabytes or even Petabytes of raw data, which cannot be stored any more on existing disk space, nor transferred within an acceptable time using any existing computer network. It is therefore necessary to develop new approaches. A simultaneous analysis and postprocessing of the generated flow data during the corresponding DNS simulations (on-the-fly analysis) appears to be particularly promising in this regard, since significantly less data has to be stored and handled this way. This analysis is realized by on-the-fly feature extraction: features of the flow and scalar fields are extracted in parallel to data generation by DNS, such that the data files do not need to be stored any more. However, this is associated with considerable chal-lenges concerning data analysis, feature extraction, parallelization and relevant numerical techniques.

直接数值模拟（DNS）是目前公认的最好的解决方案，研究数值湍流。然而，在高雷诺数下的DNS需要数十亿个网格点，并且在数千次时间迭代期间进行。当考虑涉及化学反应的更复杂的流动时，必须在空间和时间上分析和关联许多变量，以便测试和开发简化模型。这导致了TB甚至PB的原始数据，这些数据不能再存储在现有的磁盘空间中，也不能使用任何现有的计算机网络在可接受的时间内传输。因此，有必要制定新的办法。在相应的DNS模拟过程中对生成的流数据进行同步分析和后处理（动态分析）似乎在这方面特别有前途，因为必须以这种方式存储和处理的数据显著减少。这种分析是通过实时特征提取来实现的：流场和标量场的特征与DNS的数据生成并行提取，这样就不需要再存储数据文件。然而，这与相当大的挑战，涉及数据分析，特征提取，并行化和相关的数值技术。