Experimental data of the continuous evolution of fluid flow characteristics in a dump combustor is very useful and essential for better and optimum designs of gas turbine combustors and ramjet engines. Unfortunately, experimental techniques such as 2D LDV measurements provide only limited discrete information at given points; especially, for the cases of complex flows such as free vortex dump combustor swirling flows. For this type of flows, usual numerical interpolating schemes appear to be unsuitable. Recently, neural networks have emerged as viable means of expanding a finite data set of experimental measurements to enhance better understanding of a particular complex phenomenon. This study showed that artificial neural networks are suitable for the prediction of free vortex turbulent swirling flow characteristics in a model dump combustor. These techniques are proposed for optimum designs of dump combustors and ramjet engines.
- Fluids Engineering Division
Free Vortex Turbulent Swirling Flow Reconstruction Using a Neural Network
Ahmed, SA, & Raghavan, BV. "Free Vortex Turbulent Swirling Flow Reconstruction Using a Neural Network." Proceedings of the ASME 2012 Fluids Engineering Division Summer Meeting collocated with the ASME 2012 Heat Transfer Summer Conference and the ASME 2012 10th International Conference on Nanochannels, Microchannels, and Minichannels. Volume 1: Symposia, Parts A and B. Rio Grande, Puerto Rico, USA. July 8–12, 2012. pp. 783-788. ASME. https://doi.org/10.1115/FEDSM2012-72468
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