Deep neural network (DNN) is applied to mean reaction rate modelling. Two DNN structures, species-dependent (SD) and species-independent (SI), are considered.¹ Due to the explicit inclusion of all species variables in the input layer for SD-DNN, this model may consider relationships of different chemical species. However, the prediction can be performed only for simulations with chemical mechanisms considering the same set of species as the one used in training data. SI-DNN circumvents this constraint, and can be used for any set of species appearing in the combustion. For the efficient learning and better prediction performance, two physics-guided loss functions are proposed and employed, which consider mass conservation of the mixture and elemental species in a specific formulation that yields a larger number of constraint conditions. These DNNs are trained and validated using direct numerical simulation (DNS) data of three different turbulent premixed planar flames, and tested using DNS results of a fourth turbulent premixed planar flame and turbulent premixed V-flame to assess the robustness of the present models for an unknown combustion configuration as well as unknown turbulent combustion conditions.

深度神经网络 (DNN) 应用于平均反应速率建模。考虑了两种 DNN 结构，即物种相关 (SD) 和物种独立 (SI)。¹由于在 SD-DNN 的输入层中明确包含所有物种变量，该模型可能会考虑不同化学物种的关系。然而，预测只能针对化学机制的模拟进行，考虑到与训练数据中使用的物种相同的一组物种。SI-DNN 绕过了这一限制，可用于燃烧中出现的任何一组物种。为了有效学习和更好的预测性能，提出并采用了两个物理引导的损失函数，它们考虑了混合物和元素物种在特定公式中的质量守恒，从而产生了大量的约束条件。这些 DNN 使用三种不同湍流预混合平面火焰的直接数值模拟 (DNS) 数据进行训练和验证，