Machine learning techniques to predict the flame state, temperature and species concentrations in counter-flow diffusion flames operated with CH4/CO/H2-air mixtures

Abstract

The usage of artificial intelligence (AI) is increasing in many fields of research, since complex physical problems can be ‘learned’ and reproduced by AI methods. Thus, instead of numerically solving partial differential equations, describing the physical processes in detail, appropriate AI methods can be used to decrease the calculation time significantly. In the present study, artificial neural networks (ANNs) were used to predict temperature and species concentrations in a laminar counter-flow diffusion flame. To improve the accuracy of the ANNs, a support vector machine (SVM) was used to subdivide the wide range of operating conditions (air–fuel ratio, strain rate, fuel mixture) into ‘flame’ and ‘no flame’ cases. Due to classification with the SVM the prediction performance of the ANNs was optimized and an average error to the reference values (GRI3.0) below 10 K for all cases was detected, whereas the calculation time was decreased by a factor of about 4,800 (solving the transport equations with GRI3.0). • Machine learning methods to model laminar counter-flow diffusion flame. • Using artificial neural networks for temperature and species in a laminar flame. • Support vector machine for classification between ignition and non-ignition. • Reduction of the calculation time by a factor of 4,800 compared to GRI3.0. • Artificial neural networks had a higher accuracy than skeletal reaction mechanisms.