### 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.

Originalsprache | englisch |
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Titel | Pharmaceutical Discovery, Development and Manufacturing Forum 2013 - Core Programming Area at the 2013 AIChE Annual Meeting |

Untertitel | Global Challenges for Engineering a Sustainable Future |

Herausgeber (Verlag) | American Institute of Chemical Engineers |

Seiten | 67-68 |

Seitenumfang | 2 |

ISBN (elektronisch) | 9781634390514 |

Publikationsstatus | Veröffentlicht - 1 Jan 2013 |

Veranstaltung | Pharmaceutical Discovery, Development and Manufacturing Forum 2013 - Core Programming Area at the 2013 AIChE Annual Meeting: Global Challenges for Engineering a Sustainable Future - San Francisco, USA / Vereinigte Staaten Dauer: 3 Nov 2013 → 8 Nov 2013 |

### Konferenz

Konferenz | Pharmaceutical Discovery, Development and Manufacturing Forum 2013 - Core Programming Area at the 2013 AIChE Annual Meeting: Global Challenges for Engineering a Sustainable Future |
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Land | USA / Vereinigte Staaten |

Ort | San Francisco |

Zeitraum | 3/11/13 → 8/11/13 |

### Fingerprint

### ASJC Scopus subject areas

- Organische Chemie
- !!Pharmaceutical Science

### Dies zitieren

*Pharmaceutical Discovery, Development and Manufacturing Forum 2013 - Core Programming Area at the 2013 AIChE Annual Meeting: Global Challenges for Engineering a Sustainable Future*(S. 67-68). American Institute of Chemical Engineers.

**Bioreactor simulation with CUDA.** / Witz, Christian; Tantikul, Tawan; Khinast, Johannes.

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Forschung › Begutachtung

*Pharmaceutical Discovery, Development and Manufacturing Forum 2013 - Core Programming Area at the 2013 AIChE Annual Meeting: Global Challenges for Engineering a Sustainable Future.*American Institute of Chemical Engineers, S. 67-68, San Francisco, USA / Vereinigte Staaten, 3/11/13.

}

TY - GEN

T1 - Bioreactor simulation with CUDA

AU - Witz, Christian

AU - Tantikul, Tawan

AU - Khinast, Johannes

PY - 2013/1/1

Y1 - 2013/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84911473400&partnerID=8YFLogxK

M3 - Conference contribution

SP - 67

EP - 68

BT - Pharmaceutical Discovery, Development and Manufacturing Forum 2013 - Core Programming Area at the 2013 AIChE Annual Meeting

PB - American Institute of Chemical Engineers

ER -