A Neural Network-Based Model for Hydrogen–Air Combustion

Chemistry evaluation is a bottleneck to computational fluid dynamics (CFD) simulations of many real-life problems such as propulsion system design, engine diagnostics, and atmospheric modeling. In this work, we study approach for accelerating chemical kinetics calculations using artificial neural networks (ANNs) on the example of combustion of a hydrogen–air mixture. This work carries out a detailed exploratory study of the optimal design of a fully connected neural network, including the number of network parameters, number of layers as well as used activation function. Part of the work is also dedicated to investigation and optimization of network training process itself. Comparison with the results of other works, bringing some unification to the widely disparate reported results, is also performed. Links to the used datasets and the resulting neural network are provided.