Prediction of drag reduction effect caused by pulsating pipe flows is examined using machine learning. First, a large set of flow field data is obtained experimentally by measuring turbulent pipe flows with various pulsation patterns. Consequently, more than 7000 waveforms are applied, obtaining a maximum drag reduction rate and maximum energy saving rate of 38.6% and 31.4%, respectively. The results indicate that the pulsating flow effect can be characterized by the pulsation period and pressure gradient during acceleration and deceleration. Subsequently, two machine learning models are tested to predict the drag reduction rate. The results confirm that the machine learning model developed for predicting the time variation of the flow velocity and differential pressure with respect to the pump voltage can accurately predict the nonlinearity of pressure gradients. Therefore, using this model, the drag reduction effect can be estimated with high accuracy.

使用机器学习检查了由脉动管流引起的减阻效果的预测。首先，通过测量具有各种脉动模式的湍流管道流，通过实验获得了大量流场数据。因此，应用了7000多个波形，分别获得了最大减阻率和最大节能率分别为38.6％和31.4％。结果表明，在加速和减速过程中，脉动流效应可以通过脉动周期和压力梯度来表征。随后，测试了两个机器学习模型以预测减阻率。结果证实，为预测流速和压差相对于泵电压的时间变化而开发的机器学习模型可以准确地预测压力梯度的非线性。因此，使用该模型，可以高精度地估计减阻效果。