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

Abstract

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 trajectory 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. • Deterministic multiphase flow models are challenging and computationally costly • A stochastic model accurately predicts 3D two-phase flow field in a stirred vessel • The stochastic model is driven by a small set of Lagrangian input data • The stochastic model is capable of extrapolating flow predictions • The stochastic model is highly computationally efficient