Prediction of Particle Laden Turbulent Channel Flow Using One-Dimensional Turbulence

Schmidt, John R.; Wendt, Jost O.L.; Kerstein, Alan R.

doi:10.1007/1-4020-4977-3_42

Cited by 4 publications

(11 citation statements)

References 8 publications

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“…The model reproduced previously unexplained parameter dependences of droplet trajectory statistics [19]. For the channel-flow configuration considered here, model results for mean and fluctuation profiles of particle velocity components have been compared to DNS results [20,22].…”

Section: B4 Perspectivementioning

confidence: 61%

“…This approach has been implemented and results have been compared to measurements and simulations of various particle statistics in turbulent channel flow [23]. Although this approach is found to be successful in some respects, it is not pursued here because it violates a key property of particle advection: in the zero-inertia (marker-particle) limit, a particle should remain within the fluid element that contains it initially.…”

Section: B1 Particle Response To Turbulent Motionmentioning

confidence: 99%

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Non-equilibrium Wall Deposition of Inertial Particles in Turbulent Flow

2009

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Non-equilibrium effects resulting from the slow relaxation of inertial particles to statistical equilibrium with flow fluctuations in turbulence are known to have important consequences, but they are not readily incorporated into models. Here, a simple analysis of these effects predicts −2/3 power-law dependence of the particle deposition rate on Stokes number (normalized particle inertia) in the far field of a confined turbulent flow, and a weaker near-field dependence. Near-field measurements and numerical simulations exhibit this weaker dependence, as do models that are generally viewed as validated by this result, but the models fail to capture the newly identified far-field behavior due to their equilibrium assumptions. Quantification of these qualitative observations is obtained by incorporating particle response to fluid motion into 'one-dimensional turbulence' (ODT), a stochastic computational model of turbulence.

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Section: B4 Perspectivementioning

confidence: 61%

Section: B1 Particle Response To Turbulent Motionmentioning

confidence: 99%

Non-equilibrium Wall Deposition of Inertial Particles in Turbulent Flow

2009

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“…While molecular phenomena like viscous dissipation evolve along that single spatial dimension, the turbulent advection is modeled through a stochastic remapping of the velocity profiles, called eddy events. This and the previous study are a continuation of the work of Schmidt et al [9] and Sun et al [12], who extended the ODT model to predict particle-carrier phase interaction excluding particle collision. These models has still issues to convert temporal ODT output data to spatial results, which can be evaluated with spatial experimental results.…”

Section: Introductionmentioning

confidence: 71%

“…The PEI model in this study was developed by Schmidt et al [9,10] as a so-called instantaneous PEI model (noted as type-I) and governs the radial displacement due to an eddy event. Each particle obeys the model if they are located in the sampled eddy region.…”

Section: Particle-eddy Interaction Modelmentioning

confidence: 99%

Numerical studies of turbulent particle-laden jets using spatial approach of one-dimensional turbulence

Fistler¹,

Lignell²,

Kerstein³

et al. 2017

Proceedings ILASS–Europe 2017. 28th Conference on Liquid Atomization and Spray Systems

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To challenge one of the major problems for multiphase flow simulations, namely computational costs, a dimensionreduced model is used with the goal to predict these types of flow more efficiently. One-dimensional turbulence (ODT) is a stochastic model simulating turbulent flow evolution along a notional one-dimensional line of sight by applying instantaneous maps that represent the effect of individual turbulent eddies on property fields. As the particle volume fraction is in an intermediate range above 10 −5 for dilute flows and under 10 −2 for dense ones, turbulence modulation is important and can be sufficiently resolved with a two-way coupling approach, which means the particle phase influences the fluid phase and vice versa. For the coupling mechanism the ODT multiphase model is extended to consider momentum transfer and energy in the deterministic evolution and momentum transfer during the particle-eddy interaction. The changes of the streamwise velocity profiles caused by different solid particle loadings are compared with experimental data as a function of radial position. Additionally, streamwise developments of axial RMS and mean gas velocities along the centerline are evaluated as functions of axial position. To achieve comparable results, the spatial approach of ODT in cylindrical coordinates is used here. The investigated jet configuration features a nozzle diameter of 14.22 cm and a Reynolds number of 8400, which leads to a centerline inlet velocity of 11.7 m/s. The particles used are glass beads with a density of 2500 kg/m 3 . Two different particle diameters (25 and 70 µm) were tested for an evaluation of the models capability to capture the impact of a varying Stokes number and also two different particle solid loadings (0.5 and 1.0) were evaluated. It is shown that the model is capable of capturing turbulence modulation of particles in a round jet. KeywordsJet, particle-laden flows, turbulence modulation, one-dimensional turbulence Introduction Turbulent particle-laden jets play a major role in a wide range of industrial applications and natural phenomena. Especially the effect of particles on the gas-phase, named turbulence modulation, is of particular interest. Several experimental studies, e.g. Schreck and Kleis [11], Geiss et al. [4] and Budilarto [1], have shown the large influence of high particle concentrations on the turbulence level. To investigate this type of flow experimentally and to understand the physical fundamentals is still very challenging, because only a few advanced techniques to obtain spatially resolved unsteady data exist today. CFD (computational fluid dynamics) proved to be a powerful tool to investigate particle-laden flows and to acquire detailed flow information. Many CFD approaches for these flow types have limited predictive capabilities or rely on many assumptions, which affects the accuracy and restrict that generality. Even with access to a high-performance computational infrastructure direct numerical simulation (DNS) studies for particle-laden flow are...

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“…The model is based on that of Schmidt et al [22], who used ODT to study particle deposition in nonreacting flows. We have extended the model to include heat transfer properties, as well as capability for particle reactions.…”

Section: Particle Implementation In Odt Codementioning

confidence: 99%

Statistics of particle time-temperature histories : progress report for August 2012.

Sun

Lignell

Hewson³

2012

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Progress toward predictions of the statistics of particle time-temperature histories is presented. The joint statistics of a scalar like the temperature and an exposure time scale are of interest. We have related these quantities to statistics of conserved scalars, their gradients and their diffusive time scales. Future work will apply the one-dimensional turbulence (ODT) model to evaluate the suitability of these statistics to predicting particle time-temperature histories. To that end, we have reported on past performance of the ODT model and have implemented a Lagrangian particle tracking capability into the ODT code. The results of ODT-driven particle dispersion have been compared with classical experimental data, and preliminary statistics of particle evolution in reacting flows are also reported.4

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Prediction of Particle Laden Turbulent Channel Flow Using One-Dimensional Turbulence

Cited by 4 publications

References 8 publications

Non-equilibrium Wall Deposition of Inertial Particles in Turbulent Flow

Non-equilibrium Wall Deposition of Inertial Particles in Turbulent Flow

Numerical studies of turbulent particle-laden jets using spatial approach of one-dimensional turbulence

Statistics of particle time-temperature histories : progress report for August 2012.

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