Applying Computational Fluid Dynamics Technology in Bioprocesses-Part 1 - Computational fluid dynamics is a powerful tool to optimize processes. - BioPharm International


Applying Computational Fluid Dynamics Technology in Bioprocesses-Part 1
Computational fluid dynamics is a powerful tool to optimize processes.

BioPharm International
Volume 23, Issue 4


Even though CFD has advanced remarkably, many challenging cases require CFD experts. Applying tools blindly without understanding the capabilities and limitations of the methods involved could lead to erroneous results. Besides good knowledge of numerical computation technologies (i.e., ensuring mesh quality and selecting time-steps for unsteady simulations), understanding the underlying physics, chemistry, and biology, and choosing the most appropriate physiochemical models are vital to successful CFD simulations. For example, a model taking interparticle and intraparticle mass transport and adsorption for chromatographic separations into account can be a powerful predictive tool. However, industrial process streams contain many proteins and an array of other materials such as lipids, nucleic acids, and cell debris, all of which may interact with the column matrix. Determining their competitive isotherms individually is not realistic. Simplified but reasonably accurate isotherm models based on theoretical study or through experimental data determination should be set up for any CFD modeling effort.

Making prediction with high confidence would require including not only fluid dynamic modeling but also modeling of other physical quantities. Areas that relate to bioprocesses that still need extensive research include interphase drag laws, bubble breakup, and coalescence mechanisms; constitutive models for the deformation of soft porous media; the dependence of cell damage on energy dissipation rate; cohesion; and multiple-particle interactions for spherical and nonspherical particles, just to name a few. With regard to the computation technologies, a substantial reduction in computational time for 3D unsteady flow simulations can further promote the application of CFD in the biopharmaceutical industry. CFD simulations produce huge amounts of raw data for unsteady flow problems, therefore, data storage and management will become a critical issue.

CFD model validation is necessary in any CFD modeling effort. Designing experimental verification requires the same level of understanding of the physiochemical mechanisms as for CFD model setup. Choosing parameters for comparison is a process of scientific reasoning, as well as artistic intuition.

Figure 2
When designing fermentation or cell culture bioreactors, one needs to address various issues. Among them are the simultaneous dispersion of gas, the homogenization (mixing) of the nutrients or base, the suspension of living cells by the impellers, and mass transfer between cells and media. Media and bubbles are of vital importance to the performance of a bioreactor. In the case of airlift bioreactors, air flowing upward in a column-shaped bioreactor vessel generates sufficient mixing of gases and cells simultaneously, thereby replacing the need for the conventional impellers of a stirred tank bioreactor. Aeration coupled with agitation in conventional bioreactors generates complex flow dynamics in the tank. Furthermore, foaming resulting from the high volume of airflow will adversely affect process performance. Bubble busting at the air–medium interface has been reported to cause cell damage. As indicated in Figure 2, considerations of simultaneously suspending cells (solids), dispersing gas completely, achieving a sufficient ratio of surface area to gas volume for mass transfer, and minimizing detrimental hydrodynamic forces leave a very small design and performance space. Finding the overlapped optimal space for each bioreactor and using it as the scale-up and scale-down and design criterion is a challenging but rewarding task.


The use of CFD has gone through many great developments, in terms of the computational technologies for robust, accurate, and efficient numerical analysis tools, as well as higher levels of sophistication of physical modeling in the areas such as turbulence and multiphase flow.

Although the semi-empirical correlations or the lack of sound physics principles in CFD models limit its predictive capabilities, undoubtedly, with current computing power progressing unrelentingly, multiscale modeling and simulations from the particle level to the continuum level will become more and more realistic and uncover more fundamental physics. It is conceivable that CFD will continue to provide explanations for more and more flow-related phenomena. Fueled by science-centered regulatory initiatives and cost and quality concerns, the use of CFD modeling technologies will provide significant opportunities for optimization and quality enhancement in the future.

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