Comprehensive computational model for coupled fluid flow, mass transfer and light supply in tubular photobioreactors equipped with glass sponges

Abstract

The design and optimization of photobioreactors (PBR) can benefit from the development of robust and yet quantitatively accurate computational models, that incorporate the complex interplay of fundamental phenomena. At a minimum, the simulation model requires at least three submodels for hydrodynamic, light supply and biomass kinetics as pointed out by various review articles on computational fluid flow models for PBR design. By modeling the hydrodynamics, the light-dark-cycles can be detected and the mixing characteristic of the flow besides its mass transport is analyzed. The radiative transport model is deployed to predict the local light intensities according to wavelength of the light and scattering characteristic of the culture. The third submodel implements the biomass growth kinetic, by coupling the local light intensities to hydrodynamic information of CO2 concentration, to predict the algal growth.The developed mesoscopic simulation model is applied to a tubular PBR with transparent walls and internal sponge structure. The complex hydrodynamics and the homogeneous illumination in such reactors is very promising for CFD based optimization.

Limit Consistency of Lattice Boltzmann Equations

“…Primarily out of this reason, meanwhile the LBM has become an established alternative to conventional approximation tools for the incompressible Navier-Stokes equations (NSE) [25] where optimized scalability to high performance computers (HPC) is crucial. As such, LBM has been used with various extensions for applicative scenarios [22,23,35,29,7,13], such as transient computer simulations of turbulent fluid flow with the help of assistive numerical diffusion [14,34] or large eddy simulation in space [37] and in time [36]. Nonetheless, the LBM's relaxation principle does come at the price of inducing a bottom-up method, which stands in contrast to conventional top-down discretization techniques such as finite difference methods.…”