Articular cartilage has a limited ability to heal due to its avascular, aneural, and alymphatic nature. Currently, there is a need for alternative therapies for diseases that affect articular cartilage such as osteoarthritis. Recently, it has been shown that tissue constructs, which resemble cartilage in structure and function, can be cultured in vitro in a cell-polymer bioreactor system. Bioreactors provide a three dimensional environment that promotes cell proliferation and matrix production. The primary objective of this study is to accurately simulate fluid mechanics using the lattice Boltzmann method for application to a cell-polymer bioreactor system.
Lattice Boltzmann (LB) is a flexible computation technique that will allow for the simulation of a moving construct under various bioreactor conditions. The method predicts macroscopic hydrodynamics by considering virtual particle interactions. Derived from the Lattice Gas Automata, lattice Boltzmann allows for mass transfer, complex geometries, and particle dynamics. A primary goal is to characterize the accuracy of the LB implementation and eventually the shear stresses felt by a tissue construct in this dynamic environment. This information is important since recent studies show that chondrocytic function may depend on the mechanical stimuli produced by fluid flow. Hence, shear stress may affect the final mechanical properties of tissue constructs. In this study, numerical simulations are done first in 2D and then extended to 3D to test the LB implementation. Simulations of the rotating wall vessel (RWV) bioreactor are then undertaken. The results are benchmarked against computations done with a commercial CFD package, FLUENT, and compared with analytic solutions and experimental data.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/10546 |
| Date | 11 April 2006 |
| Creators | Deladisma, Marnico David |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 15366644 bytes, application/pdf |
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