Abstract:
Knowledge of continuous evolution of fluid flow characteristics is very useful and essential for better designs of efficient combustors. Many experimental techniques such as Laser Doppler Velocimetry (LDV) measurements provide only limited discrete information at given points; especially, for the cases of complex flows such as swirling flows of dump combustors. For these types of flows, usual numerical interpolating schemes appear to be unsuitable. Artificial Neural Network (ANN) methods are thus proposed and their results are presented in this paper and are compared with the experimental data used for training purposes. This pilot study showed that ANN is an appropriate method for predicting swirl flow velocity in a model of a dump combustor. In summary, this detailed information is fundamental for better designs and optimization of dump combustors.