High speed imaging of particle flow fields in CFB risers

Shaffer, Frank; Gopalan, Balaji; Breault, Ronald W.; Cocco, Ray; Karri, S.B. Reddy; Hays, Roy; Knowlton, T.M.

doi:10.1016/j.powtec.2013.01.012

Cited by 110 publications

(52 citation statements)

References 21 publications

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“…Agrawal et al 2001;Capecelatro, Pepiot & Desjardins 2014). In fluidized bed reactors, clusters have been observed to reduce mixing and interaction of particles with the transport gas (Shaffer et al 2013), and may therefore inhibit reaction rates and heat transfer, potentially lowering operating efficiencies significantly. Meanwhile, a fundamental understanding of cluster characteristics and their effect on the carrier phase remains elusive.…”

Section: Introductionmentioning

confidence: 99%

Numerical study of collisional particle dynamics in cluster-induced turbulence

2014

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We present a computational study of cluster-induced turbulence (CIT), where the production of fluid-phase kinetic energy results entirely from momentum coupling with finite-size inertial particles. A separation of length scales must be established when evaluating the particle dynamics in order to distinguish between the continuous mesoscopic velocity field and the uncorrelated particle motion. To accomplish this, an adaptive spatial filter is employed on the Lagrangian data with an averaging volume that varies with the local particle-phase volume fraction. This filtering approach ensures sufficient particle sample sizes in order to obtain meaningful statistics while remaining small enough to avoid capturing variations in the mesoscopic particle field. Two-point spatial correlations are computed to assess the validity of the filter in extracting meaningful statistics. The method is used to investigate, for the first time, the properties of a statistically stationary gravity-driven particle-laden flow, where particle-particle and fluid-particle interactions control the multiphase dynamics. Results from fully developed CIT show a strong correlation between the local volume fraction and the granular temperature, with maximum values located at the upstream boundary of clusters (i.e. where maximum compressibility of the particle velocity field exists), while negligible particle agitation is observed within clusters.

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Section: Introductionmentioning

confidence: 99%

Numerical study of collisional particle dynamics in cluster-induced turbulence

2014

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“…Moreover, intimate coupling between the phases can often lead to a high degree of segregation from an initially homogeneous distribution of particles that reorganizes the underlying structure of the turbulent flow. In a recent experimental study, Shaffer et al (2013) observed high-speed jets of gas in risers of circulating fluidized bed reactors that were produced by particle clusters, a phenomenon referred to as jet bypassing. The interaction between clusters and the gas phase was shown to destroy the mixing behaviour of fluidized beds that makes them attractive for enhanced chemical reaction and heat transfer.…”

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confidence: 99%

On fluid–particle dynamics in fully developed cluster-induced turbulence

2015

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At sufficient mass loading and in the presence of a mean body force (e.g. gravity), an initially random distribution of particles may organize into dense clusters as a result of momentum coupling with the carrier phase. In statistically stationary flows, fluctuations in particle concentration can generate and sustain fluid-phase turbulence, which we refer to as cluster-induced turbulence (CIT). This work aims to explore such flows in order to better understand the fundamental modelling aspects related to multiphase turbulence, including the mechanisms responsible for generating volume-fraction fluctuations, how energy is transferred between the phases, and how the cluster size distribution scales with various flow parameters. To this end, a complete description of the two-phase flow is presented in terms of the exact Reynolds-average (RA) equations, and the relevant unclosed terms that are retained in the context of homogeneous gravity-driven flows are investigated numerically. An Eulerian-Lagrangian computational strategy is used to simulate fully developed CIT for a range of Reynolds numbers, where the production of fluid-phase kinetic energy results entirely from momentum coupling with finite-size inertial particles. The adaptive filtering technique recently introduced in our previous work (Capecelatro et al., J. Fluid Mech., vol. 747, 2014, R2) is used to evaluate the Lagrangian data as Eulerian fields that are consistent with the terms appearing in the RA equations. Results from gravity-driven CIT show that momentum coupling between the two phases leads to significant differences from the behaviour observed in very dilute systems with one-way coupling. In particular, entrainment of the fluid phase by clusters results in an increased mean particle velocity that generates a drag production term for fluid-phase turbulent kinetic energy that is highly anisotropic. Moreover, owing to the compressibility of the particle phase, the uncorrelated components of the particle-phase velocity statistics are highly non-Gaussian, as opposed to systems with one-way coupling, where, in the homogeneous limit, all of the velocity statistics are nearly Gaussian. We also observe that the particle pressure tensor is highly anisotropic, and thus additional transport equations for the separate contributions † Email address for correspondence: jsc359@cornell.edu On fluid-particle dynamics in cluster-induced turbulence 579 to the pressure tensor (as opposed to a single transport equation for the granular temperature) are necessary in formulating a predictive multiphase turbulence model.

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“…The high flow rates encountered in risers often lead to the spontaneous generation of densely packed particles, referred to as clusters. Clusters have been observed to reduce mixing and interaction of particles with the transport gas (Shaffer et al, 2013), and therefore may inhibit the chemical conversion process, potentially lowering operating efficiencies significantly. Simultaneously accounting for both the microscale processes and particle dynamics poses significant challenges in developing predictive models.…”

Section: Introductionmentioning

confidence: 99%