A Priori Analysis on Deep Learning of Filtered Reaction Rate
A Priori Analysis on Deep Learning of Filtered Reaction Rate
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DOI：
10.1007/s10494-022-00330-0
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Junsu Shin;M. Hansinger;M. Pfitzner;M. Klein
中科院分区：
文献类型：
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作者：
Junsu Shin;M. Hansinger;M. Pfitzner;M. Klein
提出了一种由深度学习驱动的过滤反应速率模型，并在大涡模拟（LES）的背景下进行了先验分析。一个深度人工神经网络（ANN）的显式过滤的反应速率源项提取的数据库包括湍流预混平面火焰直接数值模拟（DNSes）采用单步化学训练。用于人工神经网络训练的过滤DNS数据库涵盖了广泛的湍流强度和LES滤波器宽度。采用深度学习的解释技术来搜索高维数据库中的主要输入参数，以减轻模型的复杂性。然后在具有未经训练的湍流强度和LES过滤器宽度的看不见的过滤平面火焰上测试深度学习过滤反应速率模型，深度学习过滤的反应速率模型与过滤的DNS结果实现了良好的一致性，并且与现有的代数模型相比，还提供了定量准确的代理模型以及文献中的其他燃烧模型。
A filtered reaction rate model driven by deep learning is proposed and analyzed a priori in the context of large eddy simulation (LES). A deep artificial neural network (ANN) is trained on the explicitly filtered reaction rate source term extracted from a database comprised of turbulent premixed planar flame direct numerical simulations (DNSes) employing single-step chemistry. The filtered DNS database to be used for the training of the ANN covers a wide range of turbulence intensities and LES filter widths. An interpretation technique of deep learning is employed to search the principal input parameters in the high dimensional database to alleviate the model complexity. The deep learning filtered reaction rate model is then tested on the unseen filtered planar flames featuring untrained turbulence intensities and LES filter widths, in conjunction with another canonical type of flame configuration that it has not been trained on. The deep learning filtered reaction rate model achieves good agreement with the filtered DNS results and also provides a quantitatively accurate surrogate model when compared to existing algebraic models and other combustion models from the literature.