Species reaction rate modelling based on physics-guided machine learning
Ryota Nakazawa, Yuki Minamoto, Nakamasa Inoue, Mamoru Tanahashi
Abstract
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.