A data-driven stochastic model for velocity field and phase distribution in stirred particle-liquid suspensions

A computationally efficient Lagrangian stochastic model driven by short 3D experimental trajectories determined by a technique of positron emission particle tracking, has been developed to study two-phase particle-liquid flow in a mechanically agitated vessel and unravel the complex behaviour of both phases. Using a small set of tra-jectory driver data, the stochastic model is used in conjunction with a particle-wall collision model to simulate the full velocity field and spatial distribution of particles. The performance of a first and a second order model is evaluated in particle suspensions of various concentrations. Both models are able to predict local phase velocities to a high degree of accuracy. Predictions of spatial particle distribution are reasonable by the first order model but very accurate by the second order model. Furthermore, the latter is able to accurately predict the two-phase velocity field and spatial phase distribution under flow conditions outside the experimental range.

Automated summary

This study presents a computationally efficient Lagrangian stochastic model for investigating two-phase particle-liquid flow in a mechanically agitated vessel. By using experimental data, the model accurately predicts local phase velocities and particle distribution, and it can also predict the two-phase velocity field and phase distribution under conditions beyond the experimental range.