Utilization of basic multi-layer perceptron artificial neural networks to resolve turbulent fine structure chemical kinetics applied to a CFD model of a methane/air piloted jet flame
CITATION: Laubscher, R. & Hoffmann, J. H. 2018. Utilization of basic multi-layer perceptron artificial neural networks to resolve turbulent fine structure chemical kinetics applied to a CFD model of a methane/air piloted jet flame. Journal of Thermal Engineering, 4(2):1828-1846.
The original publication is available at http://dergipark.org.tr/en/download/article-file/408300
ENGLISH ABSTRACT: This work investigates and proposes an alternative chemistry integration approach to be used with the eddy dissipation concept (EDC) advanced combustion model. The approach uses basic multi-layer perceptron (MLP) artificial neural networks (ANNs) as a chemistry integrator for the reactions that take place in the fine structure regions created by the turbulence field. The ANNs are therefore utilised to predict the incremental species changes that occur in these fine structure regions as a function of the initial species composition, temperature and the residence time of the mixture in the fine structure regions. The chemistry integration approach for the EDC model was implemented to model a piloted methane/air turbulent jet diffusion flame (Sandia Flame D) at a Reynolds number of 22400. To prove the concept, a five-step methane combustion mechanism was used to model the chemical reactions of the experimental flame. The results of the new approach were benchmarked against experimental data and the simulation results using the standard integration approaches in Fluent. It was shown that once the ANNs are well-trained (in-sample error minimised as best possible), it can predict the species mass fractions with relative accuracy in a manner that is both time and computer-memory efficient compared with using traditional integration procedures.