Modelling methane-air turbulent diffusion flame in a gas turbine combustor with artifical neural network

The present paper reports a way of using an artificial neural network (ANN) for modelling methane-air jet diffusion turbulent flame characteristics, such as temperature and chemical species mass fractions in a gas turbine combustion chamber. Since the neural network needs sets of examples to adapt its synaptic weights in the training phase, we used pre-assumed probability density function (PDF) method and considered chemical equilibrium chemistry model to compute the flame characteristics for generating the examples of input-output data sets. In this approach, flow and mixing field results are presented with a non-linear first order k-ε model. The turbulence model is applied in combination with preassumed β-PDF modelling for turbulence-chemistry interaction. The training algorithm for the neural network is based on a back-propagation supervised learning procedure, and the feed-forward multilayer network is incorporated as neural network architecture. The ability of ANN model to represent a highly non-linear system, such as a turbulent non-premixed flame is illustrated, and it can be summarized that the results of modelling of the combustion characteristics using ANN model are satisfactory, and the CPU-time and memory savings encouraging.