Prediction of Inner Grooved Circular Jet Flow with Artificial Neural Networks

Abstract

In this study, an artificial neural network model was established by using experimental measurement values obtained from a low-speed subsonic wind tunnel, with the length of 75 cm and experiment test section of 32 x 32 cm(2). Model results were compared with experimental values and then, the prediction was made for the unmeasured tunnel stream values. In the wind tunnel, the jet velocity of 25 m/s and four tunnel velocities of 0, 5, 10 and 20 m/s were used. At four measurement stations x/D = 0.3, x/D = 12.5, x/D = 31.2 and x/D = 50, experimental measurements were made using a hot wire anemometer. This study is the continuation of the work done by Inan and Sisman [T. Inan, T. Sisman, Acta Phys. Pol. A 127, 1145 ( 2015)]. Inner grooved circular jet flows at x/D = 0.3 and x/D = 50 stations with average tunnel flow velocities of 7.5 m/s and 15 m/s were studied by using artificial neural networks.