Forecasting the evolution of chaotic dynamics of two-phase slug flow regime

Presenting a predictive model through handling of the dynamical system is critical for modern technologies and industrial application. Here, Hankel-based dynamic mode decomposition is used to generate a predictive model for the slug flow regime. Flow dynamics are split into linear and nonlinear parts; the latter of which is responsible for intermittency phenomena. The proposed model shows the ability to predicts the time evolution of phase fraction. Forecasting of slug flow systems is achieved with no a priori knowledge of the equations of motion. The percentage of the variation between the actual states and predicted states is approximately 20%.

Uncontrolled Keywords

Slug flow; Chaos; DMDc; Koopman operator; Prediction;