Abstract
The mixing of stratified miscible fluids with widely different material properties is a common step in biopharmaceutical manufacturing processes. Differences between the fluid densities and viscosities, however, can lead to order-of-magnitude increase in blend times relative to the blending of single-fluid systems. Moreover, the mixing performance in two-fluid systems can be strongly dependent on the Richardson number defined as the ratio of fluid buoyancy to fluid inertia. In this work, we combine lattice Boltzmann transport algorithms with graphics card-based computing hardware to build accelerated digital twins of a physical mixing tanks. The digital twins are designed to predict real-time fluid mechanics with a fidelity that rivals experimental characterization at orders-of-magnitude less cost than physical testing. After validating the twins against measured single- and multi-fluid mixing data, we use them to explore the physics governing fluid blending in stratified two-fluid systems. We use output from the twins to provide general guidance on stratified two-fluid mixing processes, as well as guidance for building such models for other types of physical systems.
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Thomas, J., Sinha, K., Shivkumar, G. et al. A CFD Digital Twin to Understand Miscible Fluid Blending. AAPS PharmSciTech 22, 91 (2021). https://doi.org/10.1208/s12249-021-01972-5
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DOI: https://doi.org/10.1208/s12249-021-01972-5