Bioreactor Simulation With Cuda

Bioreactor simulation with CUDA

Christian Witz¹, Tawan Tantikul¹, Johannes Khinast^1,2

¹Graz University of Technology, Institute for Process and Particle Engineering

²Research Center Pharmaceutical Engineering

Abstract

The effectiveness of a biopharmaceutical manufacturing process depends to a large extent on the efficiency of the bioreactor, especially in the field of generic drugs. So far, the engineering process of the reactor design has been mostly driven by empirical knowledge, as the simulation of this complex multiphase and multiscale process was impossible for many years. However, despite the recent improvement of computational capabilities, the simulation of an industrial scale reactor takes months for only a few seconds of real operation time.

Thus, the goal of this study is to use graphic cards to speed up this simulation. The Compute Unified Device Architecture (CUDA) technology of nVidia has made the computational power of graphic processing units (GPUs) available for scientific calculations [1]. In the multiphase simulation, the large number of computing units in the GPU leads to a significant reduction of calculation time. To archive this acceleration, an efficiently parallelizable simulation method is needed.

The lattice Boltzmann method (LBM) which was developed based on the lattice gas automata [2], represents an efficient way to numerically capture the liquid phase flow dynamics on the GPU computing machine. It uses a regular grid with evenly distributed nodes. To model the geometry without changing the regular grid the immersed boundary method is used. The air bubbles are simulated with the Lagrangian particle tracking (LPT) method. The sum of the forces acting on each bubble, i.e. the drag, the buoyancy, the lift force, the history force, the added mass effect and gravity is used to determine the acceleration of the particle. The acceleration and the time step length give the velocity and the position change at the end of the time step. For coalescence and breakup stochastic models are used. The phases are coupled with a two way approach.

As the calculation results are computed on the graphic card, simulation data can be visualized real time during the calculation. Furthermore to simulate large reactors, the code has a multi GPU functionality, hence, it can distribute the workload of the simulation on several graphic processors. This multi GPU code is able to handle the large scale, memory demanding simulation such as the simulation of the industrial scale reactor.

References

[1]        http://www.nvidia.com/cuda

[2]        Sukop, M.: Lattice Boltzmann Modeling, Springer, Berlin, 2007.

 

 

Submission details

Session - 26013 Mixing Issues and Scale-Up/Scale-Down Strategies for the Production of Pharmaceutical and Biopharmaceuticals

Emails:

christian.witz@tugraz.at
tawan.tantikul@tugraz.at
khinast@tugraz.at