Evaluation of multiphase rotation models in grid turbulence via Particle Image Velocimetry
Industrial processes involving multi-phase flows such as flotation require understanding of the relationships between bubbles, solid particles and the flow. Modern experimental tools are employed in this effort to measure with great accuracy the basic features of the motion of all three phases in turbulent flow. We employed a unique Digital Particle Image Velocimeter (DPIV) that can record with great accuracy and kHz temporal resolution, velocity vectors of all three phases, namely the fluid, the solid particles and the air bubbles. The interaction of these three phases was studied in homogeneous isotropic turbulence generated by cylindrical grids. Particles and bubbles were released into the turbulence and the motions of the three phases were monitored. The experimental results obtained in the present work were compared with the predictions of the models published in the literature.
Available from: Jun Meng
- "They have the advantage over intrusive measurement techniques in that they do not disturb the fluid flows when measuring. With the help of modern signal processing techniques, they can achieve highspeed , high temporal and spatial resolution in measurements (Brady et al., 2006; Sad Chemloul and Benmedjedi, 2010). But the disadvantages of these techniques is that they can only apply to transparent or semi-transparent laboratory systems (Wikipedia, 2013), which is not the case in industrial flotation cells processing slurries which are not optically clear. "
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ABSTRACT: Turbulence and its distribution are of great importance in flotation processing and has been the subject of much research. However, there is no mature technique to measure turbulence in three phase (liquid–solid–gas) systems. In this research, the Piezoelectric Vibration Sensor (PVS) was developed, based on previous research, as a promising tool for turbulence measurements in industrial flotation environments. A frequency response model was established to calculate force applied to the sensor. Experimental results and comparison with Laser Doppler Anemometry (LDA) measurement data showed that the PVS can measure intensity of kinetic energy fluctuation (σv2σv2), which has been found in experiments to correlate with turbulent kinetic energy (TKE), a parameter often related to flotation performance in the literature. The sensor was then applied to a 60 l laboratory batch cell running at different impeller speeds and air flow rates to obtain turbulence profiles. Results showed that the piezoelectric sensor is fully capable of measuring turbulence in a multi-phase environment.
Minerals Engineering 11/2014; s 66–68:84–93. DOI:10.1016/j.mineng.2014.06.007 · 1.60 Impact Factor
Available from: inderscience.com
- "PIV measurement research results of turbulent flow can be found in such literatures as Li et al. (2009), Shah et al. (2008), Guida et al. (2010), Zhou and Cheng (2009), Sakakibara et al. (2007) and Rasouli et al. (2009). Although turbulence PIV measuring have been widely used in traditional engineering area, we can also found that turbulence image tracing involved such new subjects as homogeneous isotropic turbulent flow (de Jong et al., 2010), POD (Bouhoubeiny et al., 2011), three phased turbulent (Brady et al., 2006), flow patterns in the lower plenum of gas cooled reactors (Amini and Hassan, 2009) and flow-adaptive data validation (Liu et al., 2008b). When studying turbulence characteristics with PIV images, Milenkovic et al. (2005) studied the interactions between large vortices and bubbles using phase-averaging techniques; Mahendra and Olsen (2009) have studied flow characteristics at the outlet of an automotive supercharger; and Murzyn and Bélorgey (2005) investigated the features of a grid-generated turbulence occurring in a current flow with a free surface flow. "
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ABSTRACT: For the purpose of obtaining the accurate characteristics of turbulence motion locus in strengthen grinding, we quantitative studied the influence of image features on selecting fluid models when computing flow kinetic energy and its spatial distribution simulation. Through the turbulence image signal is sampled in a grinding flow field, we realised the multi-precision expression of image signal group for quantifying its mathematical characteristics, then established typical fluid models and deduced the computing formulas for kinetic energy. In an experiment, we use turbulence from strengthen grinding as an example, with fuzzy relational degree and central-limit variance were used for analysing and comparing the variation tendencies of those obtained kinetic energy. Finally, the computer simulation of kinetic energy spatial distribution is realised in Fluent 6.2.23, which helps to establish the influence mechanism and mutual relationship between turbulence image features and the computation of flow kinetic energy, the technical reference for turbulence monitoring can be provided.
International Journal of Simulation and Process Modelling 07/2012; 7(1/2):3 - 15. DOI:10.1504/IJSPM.2012.047858
Available from: Geoffrey M Evans
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ABSTRACT: Mineral flotation in mechanically agitated vessels (cells) involves complex interaction between bubbles, particles and the liquid phase. Ideally, just enough power input from the impeller is needed to so that the frequency of particle–bubble collision and attachment is maximised, while at the same time detachment events are minimised. This paper firstly investigated how the slip velocity of 2–10mm diameter bubbles, a size commonly encountered in flotation devices, was influenced by turbulence intensity. The measurements confirmed the earlier correlation by [Lane, G.L., 2005, Numerical modelling of gas–liquid flow in stirred tanks, Ph.D. Thesis, University of Newcastle, Australia], which was then inputted into a computational fluid dynamic model to describe the gas dispersion in a mechanically agitated tank. The model provided turbulence intensity values that were then coupled with both slip velocity and critical Weber number models to generate both bubble size and gas holdup profiles for the entire vessel. Moreover, a simple equation was introduced to allow prediction of cavity formation behind the rotating impeller blades, which is a common occurrence in most flotation cells they normally operate at high gas loadings. This inclusion allowed the model to predict power reduction resulting from the presence of the cavities. Finally, extension of the computational model to include flotation hydrodynamics, such as probabilities of collision, adhesion and stabilisation of the particles at the bubble surface, is also described. The model is able to compute net attachment rates, and hence the particle flux entering the froth recovery phase, as a function of bubble and particle diameter, gas flowrate and power input.
Chemical Engineering Research and Design 12/2008; 86(12):1350-1362. DOI:10.1016/j.cherd.2008.07.006 · 2.28 Impact Factor
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