Swirl Flow Reconstruction Using Neural Network

Saad A. Ahmed; Hany El Kadi

doi:10.15866/irephy.v10i3.10412

Swirl Flow Reconstruction Using Neural Network

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Abstract

The flowfield characteristics downstream of an axisymmetric sudden expansion dump combustor model is of basic importance to designers of gas turbine and solid- fuel ramjets. Many experimental techniques such as 2D 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 flows, usual numerical interpolating schemes appear to be unsuitable. Predictions using artificial neural networks (ANN) techniques are thus proposed and some of the results are presented in this paper and are compared with the experimental data. These techniques could be used for better designs and optimization of dump combustors.
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Keywords

Swirl Flow; Dump Combustors; Artificial Neural Networks

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