Unsteady effects in dense, high speed, particle laden flows

Regele, Jonathan D.; Rabinovitch, Jason; Colonius, Tim; Blanquart, Guillaume

doi:10.1016/j.ijmultiphaseflow.2013.12.007

Cited by 75 publications

(42 citation statements)

References 27 publications

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“…The lowest average Mach number within the cloud in our cases with M a = 2.6 happens for α = 0.1 and D p = 50 µm, where the average value at late time is 0.55. We expect that, in addition to differences caused by the two-dimensionality, the regularity of the particle configuration in Regele et al (2014) strengthens the reflected shock wave and results in a lower local Mach number than for a random configuration.…”

Section: Mean Flowmentioning

confidence: 99%

“…This causes the difference between the velocity in the separated flow regions behind the particles in this region and the mean flow to increase, and since the spanwise fluctuations are not increased similarly, the anisotropy increases. The importance of particle scale fluctuations in shock-wave particle-cloud interaction was previously examined in two-dimensional configurations using inviscid simulations by Regele et al (2014), and viscous simulations by Hosseinzadeh-Nik et al (2018), where the particle volume fraction was 0.15. Both studies featured a M a = 1.67 shock wave, which is significantly weaker than the shock waves studied here.…”

Section: Velocity Fluctuationsmentioning

confidence: 99%

See 1 more Smart Citation

Computational analysis of shock-induced flow through stationary particle clouds

Osnes

Vartdal

Omang

et al. 2019

International Journal of Multiphase Flow

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We investigate the shock-induced flow through random particle arrays using particle-resolved Large Eddy Simulations for different incident shock wave Mach numbers, particle volume fractions and particle sizes. We analyze trends in mean flow quantities and the unresolved terms in the volume averaged momentum equation, as we vary the three parameters. We find that the shock wave attenuation and certain mean flow trends can be predicted by the opacity of the particle cloud, which is a function of particle size and particle volume fraction. We show that the Reynolds stress field plays an important role in the momentum balance at the particle cloud edges, and therefore strongly affects the reflected shock wave strength. The Reynolds stress was found to be insensitive to particle size, but strongly dependent on particle volume fraction. It is in better agreement with results from simulations of flow through particle clouds at fixed mean slip Reynolds numbers in the incompressible regime, than with results from other shock wave particle cloud studies, which have utilized either inviscid or twodimensional approaches. We propose an algebraic model for the streamwise Reynolds stress based on the observation that the separated flow regions are the primary contributions to the Reynolds stress.

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Section: Mean Flowmentioning

confidence: 99%

Section: Velocity Fluctuationsmentioning

confidence: 99%

Computational analysis of shock-induced flow through stationary particle clouds

Osnes

Vartdal

Omang

et al. 2019

International Journal of Multiphase Flow

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“…For incompressible flows ( M p =0), the added‐mass coefficient is C M ,0 =0.5. Several recent numerical studies have either neglected the effect of compressibility, by assuming the incompressible value , or have considered flow only in the subcritical regime . For this work, the added‐mass coefficient is evaluated as

C_{M} (M_{p}) = ({, \begin{matrix} arrayleft \\ C_{M, 0} [1 + 1.8 {(M_{p})}^{2} + 7.6 {(M_{p})}^{4}] & for M_{p} \leq 0.5, \\ 1.0 & for M_{p} > 0.5 . \end{matrix})

The upper limit of C M =1.0 is based on the analysis by Parmar et al for subcritical Mach numbers.…”

Section: Governing Equations For Particlesmentioning

confidence: 99%

“…These unsteady forces can be an order of magnitude larger than the quasi‐steady drag force, which acts slowly on the particle because of the difference in particle and post‐shock gas velocities. Parmer et al proposed a model for the unsteady particle forces that has been applied successfully in some models from the literature .…”

Section: Introductionmentioning

confidence: 99%

An Euler–Lagrange particle approach for modeling fragments accelerated by explosive detonation

Price

Nguyen

Hassan

et al. 2015

Numerical Meth Engineering

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SUMMARYIn this paper, a method is proposed for modeling explosive-driven fragments as spherical particles with a point-particle approach. Lagrangian particles are coupled with a multimaterial Eulerian solver that uses a three-dimensional finite volume framework on unstructured grids. The Euler-Lagrange method provides a straightforward and inexpensive alternative to directly resolving particle surfaces or coupling with structural dynamics solvers. The importance of the drag and inviscid unsteady particle forces is shown through investigations of particles accelerated in shock tube experiments and in condensed phase explosive detonation. Numerical experiments are conducted to study the acceleration of isolated explosive-driven particles at various locations relative to the explosive surface. The point-particle method predicts fragment terminal velocities that are in good agreement with simulations where particles are fully resolved, while using a computational cell size that is eight times larger. It is determined that inviscid unsteady forces are dominating for particles sitting on, or embedded in, the explosive charge. The effect of explosive confinement, provided by multiple particles, is investigated through a numerical study with a cylindrical C4 charge. Decreasing particle spacing, until particles are touching, causes a 30-50% increase in particle terminal velocity and similar increase in gas impulse.

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“…However, due to the high volume fraction of particles within the particle curtain, the flow features are not directly observable. Regele et al (2014) performed numerical 20 simulations of a shock wave passing through a cloud of cylinders that are fixed in place to develop a better sense of how the flow unsteadiness behaves inside of the curtain and immediately downstream. However, it is still unclear how the particle motion and interaction between the fluid and moving particles affects the flow dynamics during this interaction.…”

Section: Introductionmentioning

confidence: 99%