Academic Paper

Pharmaceutical Blending: Understanding Miscible Fluids with a CFD Digital Twin

A CFD Digital Twin to Understand Miscible Fluid Blending

AAPS PharmSciTech (2021)

John Thomas, Kushal Sinha, Gayathri Shivkumar, Lei Cao, Marina Funck, Sherwin Shang, Nandkishor K. Nere

The pharmaceutical industry faces unique mixing challenges in the manufacturing process, causing significant processing and scale-up issues. In this work, M-Star and AbbVie build accelerated digital twins of a physical mixing tank to predict real-time fluid mechanics with a fidelity that rivals experimental data. 

predicted versus measured pharmaceutical blending over a range of Reynolds numbers

In the biopharmaceutical manufacturing process, it is common to mix two miscible liquids with differences between fluid density and viscosity. But due to the complexities of the fluids involved and engineering design limitations, manufacturers struggle to scale up production. 

To optimize pharmaceutical blending and mixing, manufacturers generally take three approaches:

  • Literature correlations
  • Experiments
  • Numerical modeling

However, these approaches are limited when considered by themselves. Few literature correlations are created for systems that involve two fluids with large differences in density and viscosity. Experimental approaches are limited by access to equipment, material expenses, and specialized labor costs. Lastly, the transport physics are three-dimensional, spatially varying, and evolve in time, limiting the applicability of numerical modeling approaches (including Reynold averages Navier-Stokes (RANS) equation-based turbulence models and other time-averaged fluid modeling approaches). 

That’s why in this study we combine Lattice Boltzmann transport algorithms with GPU-based hardware to build accelerated digital twins of a physical mixing tank. This essentially hybridizes the three approaches by developing a digital replica which can run real-time numerical experiments based on the appropriate mathematical models.

This method allowed us to simulate minutes/hours of fluid mechanics within just hours/days of computer wall time. Once developed, digital twins for pharmaceutical blending can be used to generate transient processing insights with a fidelity that rivals experimental measurement, but at a much lower cost.

In the full paper, we cover: 

  • modeling requirements, operating considerations, and numerical approach for building and validating the digital twins.
  • the impacts of density and gravity on pharmaceutical blending when the fluids are stratified. 
  • how to reduce blend time in two-fluid systems. 
  • best practices and guidance to other pharmaceutical manufacturers interested in developing digital twins to optimize blending processes. 

Read the full paper to learn more about the development of the digital twins and see the real results.