An Artificial Neural Network for the Prediction of Penetration Height of Aerated Elliptical Liquid Jets in Gaseous Crossflows

Abstract

The breakup of a liquid jet by a high-velocity gaseous crossflow has many applications in industry. Penetration height of the liquid jets in crossflows is considered as the main parameter of interest, and several empirical correlations for it have been developed by many researchers. However, recent studies show that significant differences between the predictions of the available correlations exist since the liquid jet in crossflow is a complex process and the penetration height depends on many parameters and variables. In the present study, it is shown that, although developing an accurate explicit equation or model is difficult, an Artificial Neural Network (ANN) is able to estimate the penetration height precisely. To train and test the network, input and output data have been obtained from experiments conducted in a wind tunnel. Overall, 48 different experiments have been performed, and 45 cases have been selected and partitioned into two parts: 80% for training and 20% for testing. Afterward, the remaining three cases have been used independently to test the network performance rigorously. In summary, it is revealed that ANNs enable the accurate prediction of penetration heights and have great potential to be used for more complicated operating conditions.