PIV for granular flows

Lueptow, Richard M.; Akonur, Alp; Shinbrot, Troy

doi:10.1007/s003480050023

Cited by 95 publications

(41 citation statements)

References 22 publications

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“…Different works can be found in the literature using PIV in granular flows [32], silo discharge [33][34][35] and fluidized beds [26,12]. The same PIV software used by Müller et al [26](MATPIV 1.6.1 [36]) and the same iterative process and filters of this work were followed.…”

Section: Jet Spike Mechanismmentioning

confidence: 99%

Experimental observations on the different mechanisms for solid ejection in gas-fluidized beds

Almendros-Ibáñez

Sánchez-Delgado

Sobrino

et al. 2009

Chemical Engineering and Processing: Process Intensification

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a b s t r a c tThis work presents an experimental study of the ejection velocity for different mechanisms of solid ejection in fluidized beds. The experiments were carried out in a 2D fluidized bed, where the bubble eruptions were recorded with a frequency of 250 frames per second using a high speed video camera with a resolution of 1.3 Megapixels.The results show that in isolated bubble eruption, the dome velocity is significantly reduced by the effect of a group of raining particles in the form of stalactites within the bubble. Higher velocities are observed when bubble coalescence takes place. If bubbles coalesce before the leading bubble breaks, the momentum of the trailing bubble together with the increase in the throughflow accelerate the dome of the leading bubble. In contrast, when coalescence occurs after the breakage of the leading bubble, the wake of the trailing bubble is projected into the freeboard with a very high velocity (wake spike mechanism). The last observed mechanism, the jet spike mechanism, occurs when a stream of bubbles reaches the bed surface following the path opened by the previous bubbles. A cloud of particles moving upward is observed, although their velocities are not as high as in the wake spike mechanism due to the interchange of momentum during the collisions with other particles.Finally, an explanation for some of the patterns of gas release from erupting bubbles recently observed by Hartung et al.

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Section: Jet Spike Mechanismmentioning

confidence: 99%

Experimental observations on the different mechanisms for solid ejection in gas-fluidized beds

Almendros-Ibáñez

Sánchez-Delgado

Sobrino

et al. 2009

Chemical Engineering and Processing: Process Intensification

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“…The evaluation of the velocities is done with a computer, where a mean velocity with respect to time is always calculated. Particle Image Velocimetry has been used to analyse the velocity profile [13], and a detailed description of the use of Particle Image Velocimetry with granular flows can be found in [14].…”

Section: Introductionmentioning

confidence: 99%

Silo Discharge: Measurement and Simulation of Dynamic Behavior in Bulk Solids

Böhrnsen¹,

Antes²,

Ostendorf³

et al. 2004

Chem Eng & Technol

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This paper deals with the experimental investigation and the numerical simulation of silo discharge processes, including dynamic interactions between the silo filling and the elastic silo walls. The discharge process is described by a system of nonlinear differential equations. Via the Finite Element Method (FEM) based on an EULERian reference frame, the deformation rate, the velocity field, the porosity, and the stress distribution can be calculated without the need of re-meshing the FE-grid. To compare simulation results with measured data, the numerical simulation examples are chosen to be similar to an experimental test-silo of the Institute of Mechanical Process Engineering at the Technical University of Braunschweig. Optical measurement techniques are applied to investigate the flow profile, and load cells on the silo walls register the stress evolution, e.g., a stress peak (switch) moving from the outlet to the transition from hopper to shaft.

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“…For example, the particle image velocimetry (PIV) method of flow visualization [13] allows identifying individual tracer particles in each image, but not to track these between images. In biology, flow cytometry is routinely used to measure the expression of membrane proteins of individual cells in a population [14].…”

mentioning

confidence: 99%

Reconstruction of one-dimensional chaotic maps from sequences of probability density functions

Nie

Coca

2015

Nonlinear Dyn

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In many practical situations, it is impossible to measure the individual trajectories generated by an unknown chaotic system, but we can observe the evolution of probability density functions generated by such a system. The paper proposes for the first time a matrix-based approach to solve the generalized inverse Frobenius-Perron problem, that is, to reconstruct an unknown one-dimensional chaotic transformation, based on a temporal sequence of probability density functions generated by the transformation. Numerical examples are used to demonstrate the applicability of the proposed approach and evaluate its robustness with respect to constantly applied stochastic perturbations.

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PIV for granular flows

Cited by 95 publications

References 22 publications

Experimental observations on the different mechanisms for solid ejection in gas-fluidized beds

Experimental observations on the different mechanisms for solid ejection in gas-fluidized beds

Silo Discharge: Measurement and Simulation of Dynamic Behavior in Bulk Solids

Reconstruction of one-dimensional chaotic maps from sequences of probability density functions

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