RANS Simulation of Turbulent Flames Under Different Operating Conditions Using Artificial Neural Networks for Accelerating Chemistry Modelling

Combustion modelling using computational fluid dynamics offers detailed insights on the flame structure and thermo-chemical processes. Furthermore, CFD is extensively used in the past to optimize industrial furnaces. Despite the increasing computational power, the prediction of the reaction kinetics in flames is still related to high calculation times, which is a major drawback for large scale combustion systems. To speed-up the simulation, artificial neural networks (ANNs) were applied in this study to calculate the chemical source terms in the flame instead of using a chemistry solver. Since one ANN may lack of accuracy for the entire input feature space (temperature, species concentrations), the space is subdivided in 4 regions/ANNs. The ANNs were tested for different fuel mixtures, degrees of turbulence and air-fuel/oxy-fuel combustion. It was found that the shape of the flame and its position was well predicted in all cases with regard to the temperature and CO. However, at low temperature levels (< 800 K) in some cases the ANNs under-predicted the source terms. Additionally, in oxy-fuel combustion the temperature was too high. Nevertheless, an overall high accuracy and a speed-up factor for all simulations of 12 was observed, which makes the approach highly suitable for large scale furnaces.