Laminar Burning Velocity Model Based on Deep Neural Network for Hydrogen and Propane with Air

Konrad Malik, Mateusz Żbikowski and Andrzej Teodorczyk
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Konrad Malik: Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 21/25, 00-665 Warsaw, Poland
Mateusz Żbikowski: Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 21/25, 00-665 Warsaw, Poland
Andrzej Teodorczyk: Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 21/25, 00-665 Warsaw, Poland

Energies, 2020, vol. 13, issue 13, 1-16

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.

Keywords: laminar flame speed; CFD; machine learning; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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