부산대학교 도서관

Pulsation control is one of the promising methods to drastically reduce friction drag of turbulent flows. In the present report, two approaches of machine learning of pulsating turbulent pipe flows are introduced. One is a prediction of temporal change of turbulent pipe flow using the flow fields calculated by direct numerical simulation (DNS) using Long Short-Term Memory (LSTM) coupled with Convolutional Auto Encoder (CAE). The periodic changes of flow velocities and friction drag coefficient were qualitatively reproduced by the model. The other is a prediction of pulsating flow by using thousands of experimental data. Two models are constructed using different units; Multi-Layer Perceptron (MLP) and LSTM. Both models well reproduced drag reduction rates for various pulsation patterns. Especially, the model based on LSTM showed good reproducibility of temporal changes in flow characteristics. Thus, it was shown that machine learning can be a reliable tool to predict characteristics of pulsating flows, which could be also utilized to optimize pulsation patterns in the future.