A data-driven machine learning framework for modeling of turbulent mixing flows

Abstract

A novel computationally efficient machine learning (ML) framework has been developed for constructing the turbulent flow field of single-phase or two-phase particle-liquid flows in a mechanically agitated vessel by feeding a very short-term experimental Lagrangian trajectory. Using a supervised k-nearest neighbors regressor learning algorithm coupled with a Gaussian process, the framework predicts the mean flow and turbulent fluctuations by sharing the statistical features learned from experimental data. The capability of the ML framework is evaluated by comparing the flow dynamics of predicted trajectories to extensive Lagrangian particle tracking measurements under various flow conditions. Local velocity distributions, Lagrangian statistical analysis, solid concentration distributions, and phase flow numbers show very good agreement between ML-predictions and experiments. Being accurate, efficient, and robust, the ML framework is a powerful tool for analyzing and modeling multiphase flow systems using a minimal amount of driver data input, which can equally be provided from any reliable numerical simulation, thus avoiding costly experimental measurements.