Progress Variable Variance and Filtered Rate Modelling Using Convolutional Neural Networks and Flamelet Methods
Progress Variable Variance and Filtered Rate Modelling Using Convolutional Neural Networks and Flamelet Methods
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DOI：
10.1007/s10494-019-00028-w
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发表时间：
2019-04
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影响因子：
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通讯作者：
Z. Nikolaou;C. Chrysostomou;L. Vervisch;S. Cant
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文献类型：
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作者：
Z. Nikolaou;C. Chrysostomou;L. Vervisch;S. Cant
一个纯粹的数据驱动的建模方法，使用深度卷积神经网络的大涡模拟（LES）的湍流预混火焰的背景下进行了讨论。该方法的评估进行了先验使用直接的数值模拟数据。该网络已经被训练为对滤波后的密度和滤波后的密度-进度变量乘积执行去卷积，并且通过这样做来获得未滤波的进度变量场的估计。然后可以使用去卷积场在LES网格上近似进度变量的滤波函数。这种新的策略，解决湍流燃烧建模成功的子网格尺度的进展变量方差和过滤的反应速率，使用小火焰的方法，预混湍流燃烧建模的两个基本成分。
A purely data-driven modelling approach using deep convolutional neural networks is discussed in the context of Large Eddy Simulation (LES) of turbulent premixed flames. The assessment of the method is conducted a priori using direct numerical simulation data. The network has been trained to perform deconvolution on the filtered density and the filtered density-progress variable product, and by doing so obtain estimates of the un-filtered progress variable field. A filtered function of the progress variable can then be approximated on the LES mesh using the deconvoluted field. This new strategy for tackling turbulent combustion modelling is demonstrated with success for both the sub-grid scale progress variable variance and the filtered reaction rate, using flamelet methods, two fundamental ingredients of premixed turbulent combustion modelling.