Laminar burning velocity model based on deep neural network for hydrogen and propane with air

Abstract

The aim of the study was to develop deep neural network models for laminar burning velocity (LBV) calculations. The present study resulted in models for hydrogen-air and propane-air mixtures. An original data-preparation/data-generation algorithm was also developed in order to obtain the datasets sufficient in quality and quantity for models training. The discussion about the current analytical models highlighted issues with both experimental data and methodology of creating those analytical models. It was concluded that there is a need for models that can capture data from multiple experimental techniques with ease and automate the model design and training process. We presented a full machine learning based approach that fulfills these requirements. Not only model development, but also data preparation was described in detail as it is crucial in obtaining good results. Resulting models calculations were compared with popular analytical models and experimental data gathered from literature. The calculations comparison showed that the models developed were characterized by the smallest error with regards to the experiments and behaved equally well for variable pressure, temperature, and equivalence ratio. The source code of ready-to-use models has been provided and can be easily integrated in, for example, CFD software.