Dynamic analysis for prediction of flow patterns in an oscillatory baffled reactor using machine learning

In the present paper, we come up with application of machine learning using data for flow visualization as a method for predicting unsteady flow patterns in oscillatory baffled reactors (OBRs). Application of the proper orthogonal decomposition (POD) is investigated for dynamic analysis of spatio-temporal data acquired by particle image velocimetry (PIV) to determine inputs and outputs for neural network model. It has demonstrated that three sets of modes and time-varying mode coefficients extracted by the POD could be useful for dynamic analysis and prediction of time-variant flow patterns in OBR. Also it is shown that decomposition of the time-series data for the mode coefficients by Fourier series expansion was effective for deriving reduced order model.