(751e) Spatially-Averaged Models for Dense Gas-Solid Flows

Fluidized beds are widely used in a variety of industrially important processes. During the last decades the analysis of the hydrodynamics or the efficiency of fluidized beds through numerical simulations has become increasingly common (Schneiderbauer et al. 2012; Schneiderbauer et al. 2013). During the last decades the analysis of the hydrodynamics or the efficiency of fluidized beds through numerical simulations has become increasingly common, where the two-fluid model (TFM) approach has proven to provide fairly good predictions of the hydrodynamics of gas-solid flows (Schneiderbauer et al. 2012).

However, due to computational limitations a fully resolved simulation of industrial scale reactors is still unfeasible. In our previous study (Schneiderbauer 2017), we have presented a spatially-averaged two-fluid model (SA-TFM), which enables the coarse grid simulation of dense large-scale gas-solid flows. However, these averaged TFM equations require constitutive models for the residual correlations appearing due to averaging. On the one hand, the unresolved part of the gas-solid drag force has been derived by employing a series expansion to the microscopic drag coefficient and on the other hand the Reynolds-stress like contributions are closed similar to Boussinesq-approximation in single phase flows. The subsequent averaging of this linearized drag force reveals that the unresolved part of the interphase momentum exchange is a function of the turbulent kinetic energies (TKE) of both, the gas and solid phase, and the variance of the solids volume fraction. Closure models for these quantities have been derived from first principles.

In contrast to TFM, parcel based approaches, such as MP-PIC (Snider 2001; O’Rourke & Snider 2010) and DDPM (ANSYS 2011; Klimanek et al. 2015; Cloete & Amini 2016), have become quite popular recently to access the numerical simulation of large scale gas-solid flows. However, similar to TFM computational limitations restrict such simulations of large scale gas-solid flows to coarse grids (for the continuous phase) as well (Li et al. 2012; Benyahia & Sundaresan 2012). Here, one has to distinguish between neglect the small unresolved scales due to grid coarsening and particle coarsening (Radl & Sundaresan 2014). In this paper, we show that the constitutive relation derived for the SA-TFM apply also to the parcel based approaches since the parcel approach represents a Lagrangian discretization of the solids transport and momentum equations. We refer this coarse grained parcel approach as SA-DDPM.

Finally, the SA-TFM and SA-DDPM models are validated for three different fluidization regimes: First, we apply these coarse grid models to the NETL challenging problem (Panday et al. 2014), where the flow of Geldart A and B particles in a riser section of a circulating fluidized bed has been studied. Second, the predictions of SA-TFM in the turbulent fluidization regime is examined (Zhu et al. 2008). Finally, a bubbling fluidized bed is studied (Zhu et al. 2008). The numerical results obtained on coarse grids demonstrates that the SA-TFM and SA-DDPM reveal nearly identical results and further fairly good agreement of pressure drop, solids volume fraction as well as velocity profiles. Thus, the results proof the SA-TFM and SA-DDPM are applicable to a wide range of particle diameters and different fluidization regimes.

References

ANSYS, 2011. ANSYS FLUENT Theory Guide, Canonsburg, PA: ANSYS, Inc.

Benyahia, S. & Sundaresan, S., 2012. Do we need sub-grid scale corrections for both continuum and discrete gas-particle flow models? Powder Technology, 220, pp.2–6. Available at: http://linkinghub.elsevier.com/retrieve/pii/S003259101100595X [Accessed April 17, 2012].

Cloete, S. & Amini, S., 2016. The dense discrete phase model for simulation of bubbling fluidized beds: Validation and verification. In A. Soldati & C. Marchioli, eds. Proceedings of the 9th International Conference on Multiphase Flow. Florence, Italy.

Klimanek, A. et al., 2015. Towards a hybrid Eulerian-Lagrangian CFD modeling of coal gasification in a circulating fluidized bed reactor. Fuel, 152, pp.131–137.

Li, F. et al., 2012. MP-PIC simulation of CFB riser with EMMS-based drag model. Chemical Engineering Science, 82, pp.104–113. Available at: http://dx.doi.org/10.1016/j.ces.2012.07.020.

O’Rourke, P.J. & Snider, D.M., 2010. An improved collision damping time for MP-PIC calculations of dense particle flows with applications to polydisperse sedimenting beds and colliding particle jets. Chemical Engineering Science, 65(22), pp.6014–6028. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0009250910005075 [Accessed March 17, 2012].

Panday, R. et al., 2014. Challenge problem: 1. Model validation of circulating fluidized beds. Powder Technology, 258, pp.370–391. Available at: http://dx.doi.org/10.1016/j.powtec.2014.02.010.

Radl, S. & Sundaresan, S., 2014. A drag model for filtered Euler-Lagrange simulations of clustered gas-particle suspensions. Chemical Engineering Science, 117, pp.416–425. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0009250914003467 [Accessed August 2, 2014].

Schneiderbauer, S., 2017. A spatially-averaged two-fluid model for dense large-scale gas-solid flows. AIChE Journal.

Schneiderbauer, S., Aigner, A. & Pirker, S., 2012. A comprehensive frictional-kinetic model for gas-particle flows: analysis of fluidized and moving bed regimes. Chemical Engineering Science, 80, pp.279–292.

Schneiderbauer, S., Puttinger, S. & Pirker, S., 2013. Comparative Analysis of Subgrid Drag Modifications for Dense Gas-Particle Flows in Bubbling Fluidized Beds. AIChE Journal, 59(11), pp.4077–4099.

Snider, D.M., 2001. An Incompressible Three-Dimensional Multiphase Particle-in-Cell Model for Dense Particle Flows. Journal of Computational Physics, 170(2), pp.523–549. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0021999101967476 [Accessed November 3, 2014].

Zhu, H. et al., 2008. Detailed measurements of flow structure inside a dense gas-solids fluidized bed. Powder Technology, 180(3), pp.339–349.