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

直接数值模拟（DNS）是目前公认的研究数值湍流的最佳方法。然而，高雷诺数的DNS需要数十亿个网格点，并且需要数千次迭代。在考虑涉及化学反应的更复杂的流动时，为了测试和开发简化模型，必须分析许多变量，并在空间和时间上进行关联。这导致产生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上，也不能在可接受的时间内使用任何现有的计算机网络传输。因此，有必要开发新的方法。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（实时分析）似乎在这方面特别有前途，因为这种方式需要存储和处理的数据要少得多。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在DNS生成数据的同时被并行提取，这样数据文件就不需要再存储了。然而，这涉及到数据分析、特征提取、并行化和相关数值技术方面的相当大的挑战。

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.