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.

在大涡模拟（LES）的背景下，提出了一种由深度学习驱动的过滤反应速率模型并进行了先验分析。深度人工神经网络 (ANN) 对从数据库中提取的显式过滤反应速率源项进行训练，该数据库由采用单步化学的湍流预混合平面火焰直接数值模拟 (DNSes) 组成。用于训练 ANN 的过滤 DNS 数据库涵盖了广泛的湍流强度和 LES 过滤器宽度。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 过滤器宽度的未见过滤平面火焰上测试深度学习过滤反应速率模型，结合另一种尚未训练过的规范类型的火焰配置。与现有的代数模型和文献中的其他燃烧模型相比，深度学习过滤反应速率模型与过滤后的 DNS 结果取得了很好的一致性，并且还提供了一个定量准确的替代模型。