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

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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

ABSTRACT

The capabilities of computational fluid dynamics (CFD) as a tool for trouble-shooting and Quality by Design have been demonstrated through case studies in bioprocesses from upstream, to downstream, to fill–finish. The applications involve, for example, multiple physics, fluid dynamics, transport phenomenon, heat transfer, phase transition, and rheology. Flow types include laminar flow in emulsification, turbulent flow in bioreactor mixing, and porous media flow in chromatographic columns, filtration, and centrifuge machine. Part 1 of this article series is an overview of the technology, followed by a discussion of the model setup. Part 2 of this series will discuss how CFD is used to perform flow characterization, mixing, resuspension, fermentation, and cell culture.


(AMGEN, INC.)
Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical methods and algorithms to solve and analyze problems that involve fluid flows and associated phenomena. CFD is a highly sophisticated integration of applied computer science, physics, chemistry, and engineering science. Existing commercial CFD codes are capable of simulating a very wide variety of physical processes besides fluid flow. To date, there are more than a dozen commercially available codes (e.g., Ansys, Fluent, CFX, Star-CD, Flow3D, and Phoenics) and open-source codes (e.g., Open-Foam) for CFD modeling and simulations. Significant improvements are continually being made in both CFD software and the hardware used to run it. The software is becoming more user friendly and the hardware is providing more computing power for less money.

Besides being a powerful instrument to analyze "what if" scenarios to improve the efficiency of existing operating systems, CFD also can be used as a Quality by Design (QbD) tool for the design of new systems. It can help shorten product and process development cycles; optimize processes to improve robustness, efficiency, and productivity; and solve problems as they arise in unit operations. CFD technology is well established and has been used successfully in the aerospace and automotive industries in optimizing control strategies and equipment, and shortening the time from design to production. It entered the chemical industries over the past two decades and has been applied to various chemical processes.1,2 Recently, CFD has seen increased interest in the pharmaceutical arena, where insights into fluid flow and related phenomena can help mitigate risks associated with scaling-up process equipment and aid in troubleshooting.3,4 Furthermore, it has demonstrated its potential as a QbD tool.

Pharmaceutical and biopharmaceutical processes that can benefit from CFD analysis include turbulent flow in bioreactors; multiphase flow, heat, and mass transfer; chemical reactions; phase transitions; porous media flow; and granular particle material dynamics. These processes cover upstream, downstream, and fill–finish, and involve unit operations such as fermentation and cell culture, mixing, chromatography, filtration, centrifuge separation, product freezing and thawing, lyophilization, emulsification, spray drying, product packing, and transportation. With growing capabilities and flexibility, CFD has been the most widely applicable tool available to simulate the physical processes involved in process development, manufacturing, and drug delivery.

There have many reviews of the application of CFD to various industrial processes, only a few of which have covered the role of CFD in the pharmaceutical industry.3,5,6 The biotechnology industry has its own unique processes, in addition to shared common unit operation processes with the pharmaceutical industry. This review will focus on applying CFD to bioreactor (fementer) operations.

OVERVIEW OF CFD

The fundamental basis of any CFD problem is the Navier-Stokes equations that describe the motion of fluids. Additional physiochemical processes are coupled with fluid flow, such as multiphase (gas–liquid, or solid–liquid, or solid–liquid–gas) interaction, species transport, heat transfer, mass diffusion, and chemical reactions. Numerical algorithms are used to solve conservation equations for mass, momentum, and energy, and provide solutions on flow and physical variables such as velocity, pressure, temperature, density, concentration, and volume or mass fraction, from which other derived variables such as shear stress, pH, height equivalent to a theoretical plate (HETP), etc., are obtained.

CFD involves two procedures of discretization—domain discretization and equation discretization—followed by the application of a suitable algorithm to solve the resulting ordinary differential equations for unsteady problems and an algebraic equation for steady problems, subject to appropriate boundary conditions and initial conditions, to get solutions on the grid.7 Either an iterative solver or a direct solver can be used, depending on the size of the problem, available computer memory, and CPU speed. In the step of equation discretization, partial differential equations are discretized into a finite difference equation, whereas in domain discretization, the continuous spatial domain is discretized into small control volumes to form a volume mesh or grid. A postprocessor is used for the analysis and visualization of the resulting solution.

Mesh-based methods include the finite difference method (FDM), the finite volume method (FVM), the finite element method (FEM), the boundary element method (BEM), and some high-resolution schemes that treat shocks and discontinuities. Non-mesh–based methods include smoothed particle hydrodynamics (SPH)—a Lagrangian method of solving fluid problems, Spectral methods—a technique where the equations are projected onto basis functions like the spherical harmonics and Chebyshev polynomials, and Lattice Boltzmann methods (LBM), which simulate an equivalent mesoscopic on a Cartesian grid instead of solving the macroscopic system (or the real microscopic physics).


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