Prediction of Drag Reduction Effect by Machine Learning toward Optimization of Pulsating Control in Turbulent Pipe Flow

Abstract

Machine learning based on experimental data was performed to predict the drag reduction effects of various pulsating pipe flows. In the experiment, the voltages input to the pumps were changed over time to generate 2448 different pulsating flows. Pulsating flows with higher pressure gradients than in previous study was investigated with high responsive pumps. Using machine learning model specialized for time series prediction based on experimental data, time series data of flow velocity and differential pressure from the voltages applied to the pumps to calculate the drag reduction rates. It was confirmed that the model could predict the drag reduction effect of complex pulsating flows with the almost same accuracy as in the previous study. Optimization of the voltage waveforms for pulsating control was also performed using the learned model. As a result, the drag reduction rate of optimal voltage waveform within the conditions equivalent to those of the experiment is predicted to be 42%. This is a larger value than the maximum drag reduction rate of 39% which is measured in the experiment.