Abstract
A mechanistic in-silico approach for predicting mixing and mass transfer in a two-phase stirred tank bioreactor is presented. This fully transient approach, which is tailored to run on GPUs, makes a direct appeal to first principles turbulence theory. We investigate various approaches for predicting the convective mass transfer coefficient around bubbles. We then validate the free surface and bubble mass transfer coefficients against measured data over a range of microbioreactor operating conditions and then link these variations to the underlying fluid mechanics. The approach is designed to be general and requires no re-tuning between operating conditions. Additionally, the presented approach can be utilized as a digital scaleup and tech-transfer strategy for bioreactors used in the biopharmaceutical industry.
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