Predictions of turbulence intensity and continuous evolution of fluid flow characteristics in a combustor model are useful and essential for better and optimum design of gas turbine 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 dump combustor swirling flows. For this type of flow, usual numerical interpolating schemes appear to be unsuitable. Neural Network Analysis (ANN) is proposed and the results are presented in this paper and are compared with the experimental data used for training purposes. This pilot study showed that artificial neural network is an appropriate method for predicting swirl flow characteristics in a model of a dump combustor. These techniques are proposed for better designs and optimization of dump combustors.
- Fluids Engineering Division
Predictions of Turbulence Intensity in a Combustor Model Using Neural Network Analysis
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Ahmed, SA, & El Kadi, H. "Predictions of Turbulence Intensity in a Combustor Model Using Neural Network Analysis." Proceedings of the ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting: Volume 1, Symposia – Parts A, B, and C. Montreal, Quebec, Canada. August 1–5, 2010. pp. 893-898. ASME. https://doi.org/10.1115/FEDSM-ICNMM2010-30834
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