Hydrodynamics characterization of a choanoid fluidized bed bioreactor used in the bioartificial liver system: Fully resolved simulation with a fictitious domain method
Graphical abstract
Introduction
Although significant development in support therapies has been made in recent years, fulminant hepatic failure still leads to a high mortality worldwide. Currently, the only effective long-term treatment is orthotropic liver transplantation. However, the shortage of organ donations makes this treatment difficult for widespread adoption (Fruhauf et al., 2004; Lee, Squires, Nyberg, Doo, & Hoofnagle, 2008; Yu et al., 2014). In this situation, bioartificial liver systems offer an alternative hope. They can serve as an efficient tool for bridge-to-transplantation use or to treat end-stage liver failure after injury. In these support systems, the bioreactor is the core device in which substances such as albumin and other macromolecules are exchanged between the microencapsulated artificial liver cells and the plasma of a patient. Traditionally, the microcapsules were hosted in a fixed or fluidized bed, where the high shear stress of the perfusion fluid and the existence of channel flow could result in microcapsule damage, stagnant zones, and even invalid perfusion. To improve fluidization quality and mass transfer efficiency between microcapsules and fluid, a novel choanoid fluidized bed bioreactor (CFBB) was proposed by Li et al. (Li, Yu, Chen, Zhang, & Du, 2008; Yu et al., 2014). Several biomedical experiments have been conducted to evaluate the functionality of the CFBB (Li, Li, & Yu, 2013; Yu et al., 2014). Nonetheless, the hydrodynamics of the two-phase flow in the CFBB, which plays an important role in reactor design and operation optimization, is poorly understood.
Because useful and detailed information about the flow field can be obtained, computational fluid dynamics (CFD) simulation has emerged as a desirable tool for the characterization of fluidization behavior. Generally, the CFD approaches for multiphase flows are classified as a two-fluid model (TFM), a discrete element method (DEM) in combination with a CFD method for fluid phase (DEM–CFD or so-called unresolved discrete particle method) or a direct numerical simulation (DNS or so-called resolved discrete particle method) (Feng & Musong, 2014). Based on a continuum description of the fluid and solid phases, TFM is suitable for simulations of fluidization systems at both laboratory and industrial scales because the expense of computations is less. However, the details of the phase interactions cannot be adequately modelled through TFM (van der Hoef, Annaland, Deen, & Kuipers, 2008). DEM–CFD and DNS both take a Lagrangian viewpoint to track the particle motion. Specifically, DNS employs finer grids to resolve the flow field around an individual particle and to compute the drag and torque by integrating the pressure and viscous forces on the particle surface. Therefore, DNS enables a more accurate and detailed prediction compared with DEM–CFD, and supplies drag models for the CFD strategies at coarser scales. The first application of DNS to the investigation of particle fluidization was conducted by Glowinski, Pan, Hesla, Joseph, and Periaux (2001) in a pseudo-two-dimensional fluidized bed, employing a distributed Lagrange multiplier/fictitious domain (DLM/FD) method. Pan, Joseph, Bai, Glowinski, and Sarin (2002) observed that the relationship between the simulated particle fluidization velocity and solids fraction followed the Richardson–Zaki power law (Richardson & Zaki, 1997). The DLM/FD method was also used to study the self-fluidization phenomenon in particulate flows, where heat transfer caused by an exothermic catalyst reaction occurs on particle surfaces (Wachs, 2011). Based on a similar computation principle embedded in the DLM/FD method, the immersed boundary method (IBM) has been applied to the DNS of fluidized beds. From a comparison between the particle–fluid interaction forces obtained from IBM and DEM, Kriebitzsch, van der Hoef, and Kuipers (2013) suggested a modification of the computing strategy for the gas–solid force. Feng et al. (Feng & Musong, 2014; Feng, Ponton, Michaelides, & Mao, 2014) studied the slip condition at the wall, and the heat transfer characteristics of 225 fluidized particles in a narrow channel using IBM. Another important DNS method used in the fluidization computation is the lattice-Boltzmann method. These approaches have been applied to simulate travelling waves (Derksen & Sundaresan, 2007), flow structures (Wang, Zhou, Wang, Xiong, & Ge, 2010), stability criterion verification (Wei, Wang, & Li, 2013), as well asscalar dispersion (Derksen, 2014) in two- or three-dimensional fluidized beds. Apart from these conventional DNS methods, an improved version of the macro-scale pseudo-particle modelling method was developed to simulate gas–solid systems containing hundreds to thousands of particles with high particle/fluid density ratios (Ma, Ge, Wang, Wang, & Li, 2006; Ma, Ge, Xiong, Wang, & Li, 2009; Tang et al., 2004). In this method, the finite difference technique is combined with weighted averaging techniques to upgrade particle interactions at the fluid element level (Ge & Li, 2003). Using this method, Xiong et al. (2010) also simulated a two-dimensional fluidized bed at moderate and high solid/gas density ratios. With up to 30,000 particles simulated, particle clusters were observed to form.
Based on the extensive computational works described above, DNS has been validated as a promising tool for hydrodynamics characterization of fluidized beds. However, no DNS or even other CFD methods have been used to evaluate the hydrodynamic performance of CFBBs. In our study, we employed the direct-forcing/fictitious domain (DF/FD) method proposed by Yu and Shao (2007) to conduct a fully resolved simulation of a CFBB under different operating conditions. This method is an improved version of a previous DLM/FD method (Yu, Shao, & Wachs, 2006; Yu & Wachs, 2007; Yu, Wachs, & Peysson, 2006), in which the calculation of the particle velocity and body-force was more computationally intensive compared with the DF/IBM. In the DF/FD method, this shortcoming was overcome using a discrete δ-function in the form of a bi-(tri-)linear function to explicitly transfer quantities between the Eulerian and Lagrangian nodes (Yu & Shao, 2007).
This paper is organized as follows: first, the principle and numerical schemes of the DF/FD method are described briefly. Second, the DF/FD method is verified by comparing the drag and particle settling velocity from simulations with data from the literature. The results and discussion on two- and three-dimensional CFBBs are then presented. Finally, conclusions from the numerical simulations are drawn.
Section snippets
Direct-forcing/fictitious domain method
The concept behind the DF/FD method is that the interior of particles is filled with a fictitious fluid subject to a distributed pseudo-body force (i.e., mathematically a Lagrange multiplier) to satisfy the rigid body motion constraint (Glowinski et al., 2001). Here we only briefly introduce the DF/FD method, referring the interested reader to Yu and Shao (2007) for details.
Validation
Because accurate predictions of drag on a single particle are essential in fluidization simulations, we first validate the accuracy of our code against benchmark tests in regard to this aspect. Moreover, particle velocity is an extremely important parameter that influences the behaviors inside fluidized beds, such as solid dispersion, conversion, mixing, and staging (Tebianian et al., 2015). Exploiting the similarity between particle fluidization and settling processes, we thus validate the
Results and discussion
For the bioartificial liver-support system, the liver cells are microencapsulated to form porous rigid spherical particles roughly 1 mm in diameter. Because the surface porosity is low, we simply model the microcapsules as impermeable rigid particles to simulate their motion. Fig. 4 shows a schematic of the CFBB studied, set in a rectangular computational domain. Two filter screens (sieves marked as horizontal dashed lines) are positioned at the top and bottom of the CFBB, to prevent the
Conclusions
The hydrodynamic performance of a CFBB used in the bioartificial liver-support system has been numerically studied through a DF/FD method. The algorithm was verified by comparing the simulated dragon a particle and the particle settling velocity in a periodic cubic cell with that from an analytical solution and data from the literature. The effects of particle–fluid density ratio, particle number, and the presence of filter screens on the fluidization behaviors were investigated. Depending on
Acknowledgements
The authors gratefully acknowledge the supports from China Postdoctoral Science Foundation (Grant No. 2014M550327), the opening foundation of the State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, and the National Natural Science Foundation of China (Grant No. 11372275). The authors are also grateful to Chengbo Yu and Liang Yu for their introduction of the choanoid fluidized bed bioreactor and helpful discussions.
References (37)
A critical review of the complex pressure fluctuation phenomenon in gas-solids fluidized beds
Chemical Engineering Science
(2007)The sedimentation velocity of dilute suspensions of nearly monosized spheres
International Journal of Multiphase Flow
(1999)- et al.
Direct numerical simulation of heat and mass transfer of spheres in a fluidized bed
Powder Technology
(2014) - et al.
Using the direct numerical simulation to compute the slip boundary condition of the solid phase in two-fluid model simulations
Powder Technology
(2014) - et al.
A bioartificial liver support system using primary hepatocytes: A preclinical study in a new porcine hepatectomy model
Surgery
(2004) - et al.
Simulation of particle-fluid systems with macro-scale pseudo-particle modeling
Powder Technology
(2003) - et al.
A fictitious domain approach to the direct numerical simulation of incompressible viscous flow past moving rigid bodies: Application to particulate flow
Journal of Computational Physics
(2001) - et al.
Fully resolved simulation of a gas-fluidized bed: A critical test of DEM models
Chemical Engineering Science
(2013) - et al.
Characterization of regimes and regime transitions in bubble columns by chaos analysis of pressure signals
Chemical Engineering Science
(1997) - et al.
Artificial liver for liver failure patient
International Journal of Antimicrobial Agents
(2013)
High-resolution simulation of gas-solid suspension using macro-scale particle methods
Chemical Engineering Science
Direct numerical simulation of particle clustering in gas-solid flow with a macro-scale particle method
Chemical Engineering Science
Sedimentation and fluidisation: Part I
Chemical Engineering Research & Design
Investigation of particle velocity in FCC gas-fluidized beds based on different measurement techniques
Chemical Engineering Science
Rising of 3D catalyst particles in a natural convection dominated flow by a parallel DNS method
Computers & Chemical Engineering
Direct numerical simulation of particle-fluid systems by combining time-driven hard-sphere model and lattice Boltzmann method
Particuology
Unified stability condition for particulate and aggregative fluidization—Exploring energy dissipation with direct numerical simulation
Particuology
Direct numerical simulation of sub-grid structures in gas-solid flow-GPU implementation of macro-scale pseudo-particle modeling
Chemical Engineering Science
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Present address: School of Engineering and Digital Arts, University of Kent, Kent CT2 7NT, UK.