Direct mapping from LES resolved scales to filtered-flame generated manifolds using convolutional neural networks
Direct mapping from LES resolved scales to filtered-flame generated manifolds using convolutional neural networks
复制标题
使用卷积神经网络从 LES 解析尺度直接映射到过滤火焰生成流形
DOI：
10.1016/j.combustflame.2019.08.014
复制
发表时间：
2019
影响因子：
4.4
通讯作者：
Z. Nikolaou
中科院分区：
文献类型：
--
作者：
Andrea Seltz;P. Domingo;L. Vervisch;Z. Nikolaou
提出了一个统一的建模框架中的所有未解决的条款过滤的进展变量传输方程的湍流预混火焰的大涡模拟，使用卷积神经网络。一个直接的数值模拟数据库的湍流预混化学计量的甲烷/空气射流火焰，以训练卷积神经网络预测过滤的进展变量源项和未解决的标量传输条款。为了计算网络的所有输入，需要一个从大涡模拟中容易得到的单一变量，即法弗尔滤波的进展变量c_（？）在火焰列表化学（预混小火焰）的背景下，训练的网络被证明在先验研究中产生定量良好的预测所有未解决的条款，尽管它们的性质不同，无论过滤器大小的变化，而不必诉诸于解决任何额外的传输方程。因此，本研究中提出的框架为深度学习应用于非线性空气热化学方程的建模开辟了前景，这些方程涉及未解决的源项和传输项。
A unified modelling framework for all unresolved terms in the filtered progress variable transport equation in large-eddy simulations of turbulent premixed flames is proposed, using convolutional neural networks. A direct numerical simulation database of a turbulent premixed stoichiometric methane/air jet flame is used in order to train convolutional neural networks to predict both the filtered progress variable source term and the unresolved scalar transport terms. A single variable readily available from the large-eddy simulation is required in order to calculate all inputs to networks, namely the Favre-filtered progress variable c˜. In the context of flame tabulated chemistry (premixed flamelet), the trained networks are shown to produce quantitatively good predictions of all unresolved terms in an a priori study, despite their different nature and irrespective of variations in filter size, without having to resort to solving any additional transport equations. The framework proposed in this study thus opens perspectives for the application of deep learning to the modelling of the non-linear aerothermochemistry equations which involve unresolved source and transport terms.