Adaptive chemistry reduction using Deep Neural Networks and Global Pathway Selection

Chemistry reduction is one of the main pillars of modern multi-dimensional combustion simulations. However, usually there is a tradeoff between the accuracy of the reduction model and the computational time. Many strategies have been developed in the past to avoid solving the complete set of reactions in a given kinetic mechanism. One of these strategies is the Global Pathway Selection (GPS). A novel adaptive GPS method is developed which creates multiple GPS skeletal mechanisms for short duration during combustion to better match the detailed chemistry prediction. The model not only predicts the combustion closer to detailed chemistry but also reduces the computational costs by removing species in regions of low activity. We also show how this technique can be used efficiently by implementing Deep Neural Networks (DNN) which avoids running GPS on the fly. The proposed scheme is validated with a 0D and 1D combustion simulation.