Predicting complex multicomponent particle–liquid flow in a mechanically agitated vessel via machine learning

Abstract

Machine learning (ML) is used to build a new computationally efficient data-driven dynamical model for single-phase and complex multicomponent particle–liquid turbulent flows in a stirred vessel. By feeding short-term trajectories of flow phases or components acquired experimentally for a given flow condition via a positron emission particle tracking (PEPT) technique, the ML model learns primary flow dynamics from the input driver data and predicts new long-term trajectories pertaining to new flow conditions. The model performance is evaluated over a wide range of flow conditions by comparing ML-predicted flow fields with extensive long-term experimental PEPT data. The ML model predicts the local velocities and spatial distribution of each flow phase and component to a high degree of accuracy, including conditions of impeller speeds, particle loadings and sizes within and without the range of the input driver datasets. A new flow analysis and modeling strategy is thus developed, whereby only short-term experiments (or alternatively high-fidelity simulations) covering a few typical flow situations are sufficient to enable the prediction of complex multiphase flows, significantly reducing experimental and/or simulation costs.