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DOI: 10.1615/ICHMT.2008.CHT.670
11 pages

Nasser Seraj Mehdizadeh
Aerospace Engineering Department, Amirkabir University of Technology, Center of Excellence in Computational Aerospace, Engineering, Tehran, Iran

Payam Sinaei
Aerospace Engineering Department, Amirkabir University of Technology, Center of Excellence in Computational Aerospace, Engineering, Tehran, Iran


The present paper reports a way of using an Artificial Neural Network (ANN) for modeling 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 Probability Density Function (PDF) method with twelve chemical species, and considered chemical equilibrium chemistry model to compute the flame characteristics for generating the examples of input-output data sets. The training algorithm is based on a back-propagation supervised learning procedure, and the feed-forward multi-layer 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 modeling of the combustion characteristics using ANN model are satisfactory, and the CPU-time and memory savings encouraging.

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