White Paper

Enabling Digital Twins With Computational Fluid Dynamics Modeling

By Sangmin Paik, Lead Engineer in Digital Excellence, Samsung Biologics

Machine Learning digital twin-GettyImages-1220294243

The integration of predictive modeling and bioprocess digital twins (BPDTs) is revolutionizing biopharmaceutical manufacturing, enhancing precision, efficiency, and scalability. By combining mechanistic modeling, data-driven insights, and computational fluid dynamics (CFD), BPDTs enable real-time simulation of critical bioprocess variables such as cell growth, nutrient consumption, and hydrodynamics. Samsung Biologics is leveraging these technologies to optimize process development, streamline scale-up operations, and improve quality management.

CFD plays a central role in this innovation, offering detailed analyses of fluid dynamics and mass transfer phenomena within bioreactors. These insights enable rigorous facility-fit evaluations, risk assessments, and process optimizations, reducing the need for extensive physical experimentation. Furthermore, hybrid BPDTs integrate CFD with advanced modeling techniques, delivering highly accurate predictions of cell performance and product quality.

Samsung Biologics is advancing its capabilities through significant investments in computational infrastructure and adherence to emerging regulatory guidelines, ensuring cutting-edge solutions for clients while maintaining compliance. This transformation underscores the company’s commitment to innovation and excellence in biomanufacturing.

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