years. The main question is the validation of these studies because they mainly
concern with real scale greenhouses, whereas the measurements and characterizations have merely been done on scale models (Fig. 12.7).
CFD modeling is an area of knowledge that in recent years has developed
enormously through the development of software and hardware, which has contributed to research on natural ventilation a greater understanding of the interactions
between the variables that make up the climate inside greenhouses. In the past 5
years, CFD simulation has become increasingly realistic and detailed, obtaining
more accurate solutions. However, their use requires depth and extensive knowledge of climatic variables, fluid dynamics, and turbulence. Simulating more
accurately requires more processing power, so research tends to use CFD in conjunction with other tools. Further studies are required to incorporate more realistic
crops beyond a porous medium, taking into account the role of gas exchange, which
is necessary for an understanding of the physiology and phenology of crops. There
is still a need to develop high-precision systems in greenhouses, and CFD is a
powerful tool for defining parameters with high precision.
12.6 CFD in Bioreactors
CFD provides the ability to determine the circulation time based on position of the
particle, therefore eliminating circulation times due to multiple triggering. When
this was taken into account, CFD simulations resulted in similar unimodal CTDs
for bioreactor tanks with similar geometries (Davidson et al. 2003).
Bioreactor designing is too complex but is a fundamental count on computer
simulations which aid to direct the way in which to lessen the time spent on the
developments. A generalized approach to predict oxygen transfer for bioreactors
was developed by Dhanasekharan et al. (2005). The model predictions show good
agreement with experimental data. The developed methodology was applied to
stirred tank and airlift bioreactors at different scales of operation. Thus the
approach was used for scale-up of bioprocesses. The model was improved further
by solving for more number of discrete bubble size equations to resolve the bubble
size distribution more accurately. Different bubble breakup and coalescence
mechanisms have been investigated to account for non-Newtonian media and the
presence of surfactants and impurities.
Most of the models proposed so far deviate from ideal mixing behavior, without
considering the mixing mechanism within the bioreactor. This could lead to severe
loss in yield and changes in microbial physiology. Thus, a kinetics multiscale
model was proposed by Wan-Teng et al. (2011) in order to describe the nonideally mixing mechanism of the bioreactor. Aeration rate and stirrer speed are
implemented into the proposed model to study the effect of both parameters in the
mixing mechanism of the bioreactor. Results suggested that yield predictions from
CFD simulations gave rise to approximately 5.00 % error compared to yield
results obtained from experiment. On the other hand, around 14.00 % error is
12 Advances in Computational Fluid Dynamics Applied to Biosystems
357
concern with real scale greenhouses, whereas the measurements and characterizations have merely been done on scale models (Fig. 12.7).
CFD modeling is an area of knowledge that in recent years has developed
enormously through the development of software and hardware, which has contributed to research on natural ventilation a greater understanding of the interactions
between the variables that make up the climate inside greenhouses. In the past 5
years, CFD simulation has become increasingly realistic and detailed, obtaining
more accurate solutions. However, their use requires depth and extensive knowledge of climatic variables, fluid dynamics, and turbulence. Simulating more
accurately requires more processing power, so research tends to use CFD in conjunction with other tools. Further studies are required to incorporate more realistic
crops beyond a porous medium, taking into account the role of gas exchange, which
is necessary for an understanding of the physiology and phenology of crops. There
is still a need to develop high-precision systems in greenhouses, and CFD is a
powerful tool for defining parameters with high precision.
12.6 CFD in Bioreactors
CFD provides the ability to determine the circulation time based on position of the
particle, therefore eliminating circulation times due to multiple triggering. When
this was taken into account, CFD simulations resulted in similar unimodal CTDs
for bioreactor tanks with similar geometries (Davidson et al. 2003).
Bioreactor designing is too complex but is a fundamental count on computer
simulations which aid to direct the way in which to lessen the time spent on the
developments. A generalized approach to predict oxygen transfer for bioreactors
was developed by Dhanasekharan et al. (2005). The model predictions show good
agreement with experimental data. The developed methodology was applied to
stirred tank and airlift bioreactors at different scales of operation. Thus the
approach was used for scale-up of bioprocesses. The model was improved further
by solving for more number of discrete bubble size equations to resolve the bubble
size distribution more accurately. Different bubble breakup and coalescence
mechanisms have been investigated to account for non-Newtonian media and the
presence of surfactants and impurities.
Most of the models proposed so far deviate from ideal mixing behavior, without
considering the mixing mechanism within the bioreactor. This could lead to severe
loss in yield and changes in microbial physiology. Thus, a kinetics multiscale
model was proposed by Wan-Teng et al. (2011) in order to describe the nonideally mixing mechanism of the bioreactor. Aeration rate and stirrer speed are
implemented into the proposed model to study the effect of both parameters in the
mixing mechanism of the bioreactor. Results suggested that yield predictions from
CFD simulations gave rise to approximately 5.00 % error compared to yield
results obtained from experiment. On the other hand, around 14.00 % error is
12 Advances in Computational Fluid Dynamics Applied to Biosystems
357