Reduced-order modeling of solid-liquid mixing in a stirred tank using data-driven singular value decomposition

Stirred tanks are widely used across the (bio)chemical and process industries for solid-liquid mixing. Predicting solid suspension behavior under varying agitation speeds is critical for process control and optimization. However, inherent turbulence and multiphase interactions challenges the simulation in terms of accuracy and speed. In response, increasing attention has been paid to machine learning algorithms to enhance fluid dynamics simulations. In this work, a reduced-order model (ROM) to simulate solid-liquid flows in a stirred tank was developed, which uses singular value decomposition (SVD) to learn the flow patterns from computational fluid dynamics (CFD) simulations. The impact of mode numbers and design points were further investigated. The results show that the use of the ROM can result in a reduction of computation time of up to three orders of magnitude with reasonable accuracy. This study contributes by offering an exploration into extending ROM to multiphase flows, with a particular focus on solid-liquid mixing processes.