Numerical simulation of fluidization for application in oxyfuel combustion

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March 2021, 14(3): 769-783. doi: 10.3934/dcdss.2020232

Numerical simulation of fluidization for application in oxyfuel combustion

Michal Beneš , Pavel Eichler , Jakub Klinkovský , Miroslav Kolář , Jakub Solovský , Pavel Strachota ^, and Alexandr Žák

Department of Mathematics, FNSPE CTU in Prague, Trojanova 13, 120 00 Praha 2, Czech Republic

^* Corresponding author: Pavel Strachota

Received January 2019 Revised October 2019 Published March 2021 Early access December 2019

This paper is concerned with the simulation of multiphase flow hydrodynamics in an experimental oxyfuel fluidized bed combustor designed for biomass fuels. The aim is to perform cross-validation between several models and solvers that differ in the description of some phenomena in question. We focus on the influence of turbulence modeling, inter-phase drag force models, the presence of biomass in the mixture. Also the possibility to simplify the full 3D description to a quasi-1D model is tested. However, the results indicate that such simplification is not suitable for chaotic phenomena in considered scenarios. The models were developed using ANSYS Fluent and OpenFOAM CFD software packages as well as our in-house CFD code CFBSim. The quantities relevant for comparison (the densities of the dispersed solid phases and the phase velocities) are presented in the form of cross-section averaged vertical profiles.

Keywords: Granular flow, fluidized bed, oxyfuel combustion, inter-phase drag force, ANSYS Fluent, OpenFOAM.

Mathematics Subject Classification: Primary: 76T25, 76N15; Secondary: 70-08.

Citation: Michal Beneš, Pavel Eichler, Jakub Klinkovský, Miroslav Kolář, Jakub Solovský, Pavel Strachota, Alexandr Žák. Numerical simulation of fluidization for application in oxyfuel combustion. Discrete and Continuous Dynamical Systems - S, 2021, 14 (3) : 769-783. doi: 10.3934/dcdss.2020232

References:

[1]	ANSYS, Inc., ANSYS Fluent Theory Guide, Release 15.0, Canonsburg, Pennsylvania, 2013.
[2]	P. Basu, Combustion and Gasification in Fluidized Beds, CRC Press, 2006. doi: 10.1201/9781420005158.
[3]	P. Bauer, M. Beneš, R. Fučík, H. D. Hoang, V. Klement, R. Máca, J. Mach, T. Oberhuber, P. Strachota, V. Žabka and V. Havlena, Numerical simulation of flow in fluidized beds, Discrete. Cont. Dyn. S. S, 8 (2015), 833-846, URL http://www.aimsciences.org/journals/displayArticlesnew.jsp?paperID=11376.
[4]	M. Beneš, P. Strachota, R. Máca, V. Havlena and J. Mach, A quasi-1D model of biomass co-firing in a circulating fluidized bed boiler, in Finite Volumes for Complex Applications VII. Elliptic, Parabolic, and Hyperbolic Problems, Springer Proc. Math. Stat., Springer, Cham, 78 (2014), 791-799. doi: 10.1007/978-3-319-05591-6_79.
[5]	C. T. Bowman and D. J. Seery, Emissions from Continuous Combustion Systems, Plenum Press, 1972.
[6]	J. Christiansen, Handbook Series Numerical Integration: Numerical solution of ordinary simultaneous differential equations of the 1st order using a method for automatic step change, Numer. Math., 14 (1970), 317-324. doi: 10.1007/BF02165587.
[7]	D. Gidaspow, Multiphase Flow and Fluidization: Continuum and Kinetic Theory Description, Academic Press, Inc., Boston, MA, 1994.
[8]	B. E. Launder and D. B. Spalding, The numerical computation of turbulent flows, Numerical Prediction of Flow, Heat Transfer, Turbulence and Combustion, (1983), 96-116. doi: 10.1016/B978-0-08-030937-8.50016-7.
[9]	Y. F. Liu and O. Hinrichsen, CFD modeling of bubbling fluidized beds using OpenFOAM: Model validation and comparison of TVD differencing schemes, Computers and Chemical Engineering, 69 (2014), 75-88. doi: 10.1016/j.compchemeng.2014.07.002.
[10]	M. Mercedes Maroto-Valer (ed.), Developments and Innovation in Carbon Dioxide (CO₂) Capture and Storage Technology, Woodhead Publishing, 2010.
[11]	1 F. Sher, M. A. Pans, C. Sun, C. Snape and H. Liu, Oxy-fuel combustion study of biomass fuels in a 20 kw fluidized bed combustor, Fuel, 215 (2018), 778-786.
[12]	L. D. Smoot and P. J. Smith, Coal Combustion and Gasification, Plenum Press, 1985. doi: 10.1007/978-1-4757-9721-3.
[13]	M. Syamlal and T. J. O'Brien, Computer simulation of bubbles in a fluidized bed, AIChE Symp. Ser., 85 (1989), 22-31.
[14]	L. G. Zheng, Oxy-Fuel Combustion for Power Generation and Carbon Dioxide (CO₂) Capture, Woodhead Publishing, 2011.

show all references

References:

[1]	ANSYS, Inc., ANSYS Fluent Theory Guide, Release 15.0, Canonsburg, Pennsylvania, 2013.
[2]	P. Basu, Combustion and Gasification in Fluidized Beds, CRC Press, 2006. doi: 10.1201/9781420005158.
[3]	P. Bauer, M. Beneš, R. Fučík, H. D. Hoang, V. Klement, R. Máca, J. Mach, T. Oberhuber, P. Strachota, V. Žabka and V. Havlena, Numerical simulation of flow in fluidized beds, Discrete. Cont. Dyn. S. S, 8 (2015), 833-846, URL http://www.aimsciences.org/journals/displayArticlesnew.jsp?paperID=11376.
[4]	M. Beneš, P. Strachota, R. Máca, V. Havlena and J. Mach, A quasi-1D model of biomass co-firing in a circulating fluidized bed boiler, in Finite Volumes for Complex Applications VII. Elliptic, Parabolic, and Hyperbolic Problems, Springer Proc. Math. Stat., Springer, Cham, 78 (2014), 791-799. doi: 10.1007/978-3-319-05591-6_79.
[5]	C. T. Bowman and D. J. Seery, Emissions from Continuous Combustion Systems, Plenum Press, 1972.
[6]	J. Christiansen, Handbook Series Numerical Integration: Numerical solution of ordinary simultaneous differential equations of the 1st order using a method for automatic step change, Numer. Math., 14 (1970), 317-324. doi: 10.1007/BF02165587.
[7]	D. Gidaspow, Multiphase Flow and Fluidization: Continuum and Kinetic Theory Description, Academic Press, Inc., Boston, MA, 1994.
[8]	B. E. Launder and D. B. Spalding, The numerical computation of turbulent flows, Numerical Prediction of Flow, Heat Transfer, Turbulence and Combustion, (1983), 96-116. doi: 10.1016/B978-0-08-030937-8.50016-7.
[9]	Y. F. Liu and O. Hinrichsen, CFD modeling of bubbling fluidized beds using OpenFOAM: Model validation and comparison of TVD differencing schemes, Computers and Chemical Engineering, 69 (2014), 75-88. doi: 10.1016/j.compchemeng.2014.07.002.
[10]	M. Mercedes Maroto-Valer (ed.), Developments and Innovation in Carbon Dioxide (CO₂) Capture and Storage Technology, Woodhead Publishing, 2010.
[11]	1 F. Sher, M. A. Pans, C. Sun, C. Snape and H. Liu, Oxy-fuel combustion study of biomass fuels in a 20 kw fluidized bed combustor, Fuel, 215 (2018), 778-786.
[12]	L. D. Smoot and P. J. Smith, Coal Combustion and Gasification, Plenum Press, 1985. doi: 10.1007/978-1-4757-9721-3.
[13]	M. Syamlal and T. J. O'Brien, Computer simulation of bubbles in a fluidized bed, AIChE Symp. Ser., 85 (1989), 22-31.
[14]	L. G. Zheng, Oxy-Fuel Combustion for Power Generation and Carbon Dioxide (CO₂) Capture, Woodhead Publishing, 2011.

Figure 1. Dimensions of the combustion chamber and meshes considered. (A) Dimensions of the cross-section of combustion chamber. (B) Coarse mesh generated by ANSYS Meshing. (C) Cross-section of the mesh in ANSYS. (D) Cross-section of the mesh in OpenFOAM generated by BlockMesh

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Figure 3. Profiles of the gas velocity averaged across the cross-section of the chamber obtained from the simulations for the circulating (upper curves) and bubbling (lower curves) fluidization regimes

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Figure 2. ANSYS Fluent simulation of the sand particles dynamics in the lower part of the combustion chamber in the bubbling regime shown by means of the sand volume fraction. Time sequence of snapshots of the longitudinal slice through the center of the 3D domain is displayed. The sequence shows a two-dimensional image of penetration dynamics of the gas phase (the lighter regions) through sand particles (their distribution is indicated by darker regions)

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Figure 4. Linear density of sand particles along the vertical axis of the combustion chamber. Circulating regime

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Figure 5. Velocity of sand particles averaged across the cross-section of the chamber. Circulating regime

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Figure 6. Comparison of biomass linear densities between 1D CFBSim code and ANSYS Fluent

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Figure 7. Comparison between the profiles of the biomass velocity averaged over the cross-section of the chamber obtained by 1D CFBSim code and ANSYS Fluent

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Figure 8. Velocity of sand particles averaged across the cross-section of the chamber in circulating regime for Gidaspow [7] and Syamlal [13] drag models

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Figure 9. Linear density of sand particles in circulating regime in dependence on drag models according to Gidaspow [7] and Syamlal [13]

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Figure 10. Comparison of linear densities of sand particles with RAS turbulence model and laminar flow available in the OpenFOAM package (solver twoPhaseEulerFoam)

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Figure 11. Comparison of the profiles of the velocity of sand particles averaged over the cross-section of the chamber obtained using the RAS turbulence model and laminar flow model available in the OpenFOAM package (solver twoPhaseEulerFoam). The profiles are also compared with the velocity profile obtained by the 1D CFBSim model

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Figure 12. Comparison of linear densities in the two-phase laminar model (OpenFOAM solver twoPhaseEulerFoam) and three-phase laminar model (OpenFOAM solver reactingMultiphaseEulerFoam)

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Figure 13. Comparison of particle velocities averaged over the cross-section of the chamber in the two-phase laminar model (OpenFOAM solver twoPhaseEulerFoam) and three-phase laminar model (OpenFOAM solver reactingMultiphaseEulerFoam)

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Figure 14. Comparison of the gas velocity averaged over the cross-section of the chamber under the oxyfuel setting and the air setting. Computed in ANSYS Fluent

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Figure 15. Comparison of the velocity of sand particles averaged over the cross-section of the chamber under the oxyfuel setting and the air setting. Computed in ANSYS Fluent

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Figure 16. Comparison of the linear density of sand particles under the oxyfuel setting and the air setting. Computed in ANSYS Fluent

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Table 1. Phase properties corresponding to given temperature and gas composition (air, oxyfuel). Diameter $ d_b $ is calculated so that the volume of a particle corresponds to the average volume of biomass pellets used in [11]. Note that the kinematic viscosity values of all the phases have the same order of magnitude ($ \propto 10^{-4}\:\mathrm{m}^2\;\mathrm{s}^{-1} $)

Temperature	$ T = 1000 \: \mathrm{K} $
Air - density	$ \rho_g = 0.3564 \: \mathrm{kg}\;\mathrm{m}^{-3} $
Air - dynamic viscosity	$ \mu_g = 4.1923 \cdot 10^{-5} \: \mathrm{kg} \; \mathrm{m}^{-1} \; \mathrm{s}^{-1} $
Oxyfuel gas - density	$ \rho_g = 0.495 \: \mathrm{kg} \; \mathrm{m}^{-3} $
Oxyfuel gas - dynamic viscosity	$ \mu_g = 5.8227 \cdot 10^{-5} \: \mathrm{kg} \; \mathrm{m}^{-1} \; \mathrm{s}^{-1} $
Sand - particle diameter	$ d_s = 0.78 \: \mathrm{mm} $
Sand - density	$ \rho_s = 2655 \: \mathrm{kg} \; \mathrm{m}^{-3} $
Sand - dynamic viscosity [7]	$ \mu_s = 0.5 \: \mathrm{kg} \; \mathrm{m}^{-1} \; \mathrm{s}^{-1} $
Sand - packing limit	$ 0.59 $
Biomass (Miscanthus) - particle diameter	$ d_b = 10.36 \: \mathrm{mm} $
Biomass (Miscanthus) - density	$ \rho_b = 603 \: \mathrm{kg} \; \mathrm{m}^{-3} $
Biomass (Miscanthus) - dynamic viscosity [7]	$ \mu_b = 0.5 \: \mathrm{kg} \; \mathrm{m}^{-1} \; \mathrm{s}^{-1} $
Biomass (Miscanthus) - packing limit	$ 0.5 $

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Temperature	$ T = 1000 \: \mathrm{K} $
Air - density	$ \rho_g = 0.3564 \: \mathrm{kg}\;\mathrm{m}^{-3} $
Air - dynamic viscosity	$ \mu_g = 4.1923 \cdot 10^{-5} \: \mathrm{kg} \; \mathrm{m}^{-1} \; \mathrm{s}^{-1} $
Oxyfuel gas - density	$ \rho_g = 0.495 \: \mathrm{kg} \; \mathrm{m}^{-3} $
Oxyfuel gas - dynamic viscosity	$ \mu_g = 5.8227 \cdot 10^{-5} \: \mathrm{kg} \; \mathrm{m}^{-1} \; \mathrm{s}^{-1} $
Sand - particle diameter	$ d_s = 0.78 \: \mathrm{mm} $
Sand - density	$ \rho_s = 2655 \: \mathrm{kg} \; \mathrm{m}^{-3} $
Sand - dynamic viscosity [7]	$ \mu_s = 0.5 \: \mathrm{kg} \; \mathrm{m}^{-1} \; \mathrm{s}^{-1} $
Sand - packing limit	$ 0.59 $
Biomass (Miscanthus) - particle diameter	$ d_b = 10.36 \: \mathrm{mm} $
Biomass (Miscanthus) - density	$ \rho_b = 603 \: \mathrm{kg} \; \mathrm{m}^{-3} $
Biomass (Miscanthus) - dynamic viscosity [7]	$ \mu_b = 0.5 \: \mathrm{kg} \; \mathrm{m}^{-1} \; \mathrm{s}^{-1} $
Biomass (Miscanthus) - packing limit	$ 0.5 $

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2020 Impact Factor: 2.425

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