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Applications

Predict Two-Fluid Mixing and Droplet Dispersion

Two-fluid dispersion processes are crucial to many drug product and chemical manufacturing processes. M-Star CFD uses rigorous, first-principles modeling approaches for predicting droplet size distributions and dispersion physics.
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CFD Modeling Capabilities

M-Star CFD was built to run on millions and billions of lattice grid points. This allows you to model complex two-fluid models more accurately and in minutes.
  • Direct Two-Fluid LES/DNS Models
  • Particle/Parcel-Based Models
  • Droplet Size Distribution
  • Droplet Reaction Modeling

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Published in AiChE Journal

Predicting the Diameters of Droplets Produced in Turbulent Liquid-Liquid Dispersion

See how Boehringer Ingelheim uses M-Star CFD to understand the two-fluid mixing process and the relationship between droplet size distribution, fluid properties, and operating conditions.

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More academic papers

Predicting the break-up of oil droplets in the trailing vortex of an impeller

Case Study

How AbbVie Built Digital Twins to Understand Miscible Fluid Blending

With M-Star CFD software, AbbVie built accelerated digital twins of a physical mixing tank to predict real-time fluid mechanics with a fidelity that rivals experimental data.
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Case Study

Bristol Myers Squibb Predicts Mass & Oxygen Transfer in Bioreactors

BMS used M-Star CFD to build time dependent, bubble-resolved, two-phase models to investigate the real-time blending, and mass and oxygen transfer in stirred tank bioreactors.
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Case Study

How Pfizer Leveraged Digital Twins to Create a Process Scale-Up Roadmap

Pfizer built digital twins with M-Star CFD to perform lab experiments in silico, accelerating the speed of manufacturing innovation and minimizing the scope of required supporting experimental studies.
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Publication

Validating Lattice Boltzmann LES for Biopharma Equipment Characterization

Boehringer Ingelheim and the Institute of Multiphase Flows use M-Star CFD to apply transient LB LES to a 3L bioreactor system and compare results to a steady RANS simulation.
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