Direct numerical simulation of gas–solid suspensions at moderate Reynolds number: Quantifying the coupling between hydrodynamic forces and particle velocity fluctuations

Tenneti, Sudheer; Garg, Rahul; Hrenya, Christine M.; Fox, Rodney O.; Subramaniam, Shankar

doi:10.1016/j.powtec.2010.03.042

Cited by 78 publications

(91 citation statements)

References 33 publications

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“…The other relevant timescale ratio is the time that the particle configuration takes to change compared to d p /| U |, and this time scale ratio depends on Re T = d p T 1/2 /ν, which is the Reynolds number based on the particle fluctuating velocity that is characterized by the particle granular temperature T. In our simulations we have Re T = 0. While one expects finite granular temperature in risers, both direct numerical simulations of freely-evolving suspensions [58] and recent high-speed imaging of particles [12] show that this value of Re T is low. Hence, these numerical simulations of turbulence past fixed clusters of spheres can be considered a reasonable approximation to gas-solid riser flows where particles have high Stokes number (moderate Re p , high particle/fluid density ratio) and relatively low levels of particle velocity fluctuations.…”

Section: Introductionmentioning

confidence: 98%

Effect of Particle Clusters on Carrier Flow Turbulence: A Direct Numerical Simulation Study

Subramaniam

2010

Flow Turbulence Combust

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Experiments indicate that particle clusters that form in fluidized-bed risers can enhance gas-phase velocity fluctuations. Direct numerical simulations (DNS) of turbulent flow past uniform and clustered configurations of fixed particle assemblies at the same solid volume fraction are performed to gain insight into particle clustering effects on gas-phase turbulence, and to guide model development.The DNS approach is based on a discrete-time, direct-forcing immersed boundary method (IBM) that imposes no-slip and no-penetration boundary conditions on each particle's surface. Results are reported for mean flow Reynolds number Re p = 50 and the ratio of the particle diameter d p to Kolmogorov scale is 5.5. The DNS confirm experimental observations that the clustered configurations enhance the level of fluid-phase turbulent kinetic energy (TKE) more than the uniform configurations, and this increase is found to arise from a lower dissipation rate in the clustered particle configuration. The simulations also reveal that the particle-fluid interaction results in significantly anisotropic fluid-phase turbulence, the source of which is traced to the anisotropic nature of the interphase TKE transfer and dissipation tensors. This study indicates that when particles are larger than the Kolmogorov scale (d p > η), modeling the fluid-phase TKE alone may not be adequate to capture the underlying physics in multiphase turbulence because the Reynolds stress is anisotropic. It also shows that multiphase turbulence models should consider the effect of particle clustering in the dissipation model.

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Section: Introductionmentioning

confidence: 98%

Effect of Particle Clusters on Carrier Flow Turbulence: A Direct Numerical Simulation Study

Subramaniam

2010

Flow Turbulence Combust

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“…The turbulent multiphase flows, complicated by the presence of hard-to-quantify interactions between phases, are also a widely pursued research subject [29][30][31]. Due to the stochastic nature of turbulence in multiphase flows emanating from both the carrier-phase turbulence and from the interaction of dispersed phase particles with the carrier phase (also sometimes referred to as pseudo-turbulence) [32][33][34][35], the problem of turbulent multiphase flow is far more complex than its single-phase counterpart. Quantification of pseudoturbulence (carrier phase velocity fluctuations due to dispersed phase) has been mostly limited to sub-Kolmogorov [31,[36][37][38] scales and only recent studies have extended such analysis to large particles [32,35] using Particle-Resolved Direct Numerical Simulations (PR-DNS)…”

Section: Challenges and Technical Gapsmentioning

confidence: 99%

“…However, our understanding of key mechanisms responsible for turbulence attenuation, production, and cascading over scales in multiphase flows over a range of physical conditions is still lacking. PR-DNS of gas-solid suspensions resolving the flow around particles up to the smallest scales [32,41,61,69] presents itself as an excellent tool to study in-depth the above mechanisms and develop models that can be inputted into CFD simulations of PJM like applications. It should be noted that any activity relying on detailed numerical experiments (even PR-DNS) should be accompanied by detailed experiments for validation purposes.…”

Section: Proposed Solutionsmentioning

confidence: 99%

Technical Report on NETL's Non Newtonian Multiphase Slurry Workshop: A path forward to understanding non-Newtonian multiphase slurry flows

Guenther¹,

Garg²

2013

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“…Recent findings by Tenneti et al (2010b), however, indicate that such treatments are not appropriate. Figure 2 shows a plot of the streamwise component of fluctuations in particle acceleration A ′′ versus the streamwise component of fluctuations in particle velocity V. The fluctuations in the particle acceleration and velocity are defined with respect to their corresponding mean values.…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, for the system of Figure 1c, the presence of numerous particles moving in different directions will lead to continually-changing fluid-dynamic interactions between particles (i.e., fluctuations in the fluid velocity and pressure fields) even at low Reynolds number. Finally, and perhaps more importantly, a common assumption in works that incorporate gas-and/or solid-phase fluctuations is that the basic form of the mean fluid force [F fluid = β(U g − U)] also holds for its instantaneous counterpart by simply replacing the mean hydrodynamic fields with instantaneous ones [e.g.,Recent findings by Tenneti et al (2010b), however, indicate that such treatments are not appropriate. Figure 2 shows a plot of the streamwise component of fluctuations in particle acceleration A ′′ versus the streamwise component of fluctuations in particle velocity V. The fluctuations in the particle acceleration and velocity are defined with respect to their corresponding mean values.…”

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confidence: 99%

Development, Verification, and Validation of Multiphase Models for Polydisperse Flows

Hrenya¹,

Cocco²,

Fox³

et al. 2011

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This report describes in detail the technical findings of the DOE Award entitled "Development, Verification, and Validation of Multiphase Models for Polydisperse Flows." The focus was on high-velocity, gas-solid flows with a range of particle sizes. A complete mathematical model was developed based on first principles and incorporated into MFIX. The solid-phase description took two forms: the Kinetic Theory of Granular Flows (KTGF) and Discrete Quadrature Method of Moments (DQMOM). The gas-solid drag law for polydisperse flows was developed over a range of flow conditions using Discrete Numerical Simulations (DNS). These models were verified via examination of a range of limiting cases and comparison with Discrete Element Method (DEM) data. Validation took the form of comparison with both DEM and experimental data. Experiments were conducted in three separate circulating fluidized beds (CFB's), with emphasis on the riser section. Measurements included bulk quantities like pressure drop and elutriation, as well as axial and radial measurements of bubble characteristics, cluster characteristics, solids flux, and differential pressure drops (axial only). Monodisperse systems were compared to their binary and continuous particle size distribution (PSD) counterparts. The continuous distributions examined included Gaussian, lognormal, and NETLprovided data for a coal gasifier.FINAL TECHNICAL 4 DE-FC26-07NT43098 5 EXECUTIVE SUMMARYThis report is the final technical report for the award DE-FC26-07NT43098 entitled "Development, Verification, and Validation of Multiphase Models for Polydisperse Flows." Below is a summary of work completed under the grant for each of the major goals. Goal I: Continuum Theory for Solid PhaseTwo separate and complementary continuum theories were derived to model polydisperse solids: the kinetic theory of granular flow (KTGF) and the discrete quadrature method of moments (DQMOM). Both theories are based on first principles with no adjustable parameters. The polydisperse KTGF and DQMOM were then encoded in MFIX, and underwent a wide range of verification testing to ensure, to the best possible extent, that no coding errors were present. These verification tests included both granular and gas-solid flows, including cases where an analytical solution and/or DEM (discrete element method) data and/or simple test cases. For the case of KTGF, another set of constitutive equations were derived which rigorously incorporated the gas phase for a simplified case of monodisperse systems at low Reynolds numbers. This derivation used the acceleration model developed in Task 2 from direct numerical simulations (DNS) in the starting kinetic equation, and indicated the effect of the fluid phase on the resulting constitutive relations. Goal II: Improved Gas-Particle Drag Laws -effect of particle size distributionIn this portion of the effort, two types of direct numerical simulations (DNS) were used to extract the drag force experienced by particles in a polydisperse suspension. These two methods, na...

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Direct numerical simulation of gas–solid suspensions at moderate Reynolds number: Quantifying the coupling between hydrodynamic forces and particle velocity fluctuations

Cited by 78 publications

References 33 publications

Effect of Particle Clusters on Carrier Flow Turbulence: A Direct Numerical Simulation Study

Effect of Particle Clusters on Carrier Flow Turbulence: A Direct Numerical Simulation Study

Technical Report on NETL's Non Newtonian Multiphase Slurry Workshop: A path forward to understanding non-Newtonian multiphase slurry flows

Development, Verification, and Validation of Multiphase Models for Polydisperse Flows

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