(696c) Enhancing Filtration and Chromatography Processes Using GPU-Accelerated Lattice-Boltzmann Simulations

This presentation introduces a multi-scale approach to predicting two-phase flow through porous media, integrating lattice-Boltzmann (LB) simulations with fully resolved and semi-empirical Ergun models. By leveraging the LB method's suitability for complex topologies, the work provides insights into transport phenomena governing single and multiphase flow systems, with applications to filtration and chromatography processes. At small scales, this methodology involves creating packed beds using fully resolved discrete element method (DEM) simulations, enabling a broad exploration of flow behaviors crucial for optimizing filtration efficiency and chromatography separation processes. At larger scales, we consider Ergun models. We also address numerical considerations such as grid spacing optimization and simulation time step refinement to ensure the accuracy and reliability of predictions, facilitating comparison with experimental data. A key component of this analysis will focus on process scale-up, in terms of understanding physics across a range of operating volumes and scales.

One of the primary challenges in conducting high-resolution simulations lies in the computational burden associated with intricate models. To overcome this challenge and make our simulations industrially practical, we explore the utilization of graphics processing units (GPUs) for accelerating computations. GPUs offer parallel processing capabilities that significantly reduce computation time, enhancing the feasibility of our research and making it accessible for real-world industrial implementations.