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This study describes a robust bubble image recognition algorithm that detects the in-focus, ellipse-like bubble images from experimental images with heavily overlapping bubbles. The principle of the overlapping object recognition (OOR) algorithm is that it calculates the overall perimeter of a segment, finds the points at the perimeter that represent the connecting points of overlapping objects, clusters the perimeter arcs that belong to the same object and fits ellipses on the clustered arcs of the perimeter. The accuracy of the algorithm is studied with simulated images of overlapping ellipses, providing an RMS error of 0.9 pixels in size measurement. The algorithm is utilized in measurements of bubble size distributions with a direct imaging (DI) technique in which a digital camera and a pulsed back light are used to detect bubble outlines. The measurement system is calibrated with stagnant bubbles in a gel in order to define the bubble size dependent effective thickness of the measurement volume and the grey scale gradient threshold as a focus criterion. The described concept with a novel bubble recognition algorithm enables DI measurements in denser bubbly flows with increased reliability and accuracy of the measurement results. The measurement technique is applied to the study of the turbulent bubbly flow in a papermaking machine, in the outlet pipe of a centrifugal pump.

The application of particle image velocimetry to turbulence measurement is described. An analysis of physically necessary spatial resolution is presented by using a model spectrum function. A comparison is made with hot-wire anemometry. Some aspects of spatial filtering and two-dimensional sampling are presented with a comparison to large-eddy simulation. The estimation of time mean turbulence quantities from the measured vector fields in a laboratory mixer is used as an example. The measurement results for two different image sizes at the same position in the flow field are compared with different interrogation area sizes. The assumed dependence of the velocity field on the interrogation area size could not be confirmed. The image size seems to produce dependence in all estimated quantities. The measurement errors are critical for the achieved results.

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The effects of triboelectricity in a small-scale fluidized bed of polyethylene particles were investigated by imaging the particle layer in the vicinity of the column wall and by measuring the pressure drop across the bed. The average charge on the particles was altered by changing the relative humidity of the gas. A triboelectric charging model coupled with a computational fluid dynamics-discrete element method (CFD-DEM) model was utilized to simulate gas-particle flow in the bed. The electrostatic forces were evaluated based on a particle-particle particle-mesh method, accounting for the surface charge on the insulating walls. It was found that simulations with fixed and uniform charge distribution among the particles capture remarkably well both the agglomeration of the particles on the wall and the associated decrease in the pressure drop across the bed. With a dynamic tribocharging model, the charging rate had to be accelerated to render the computations affordable. Such simulations with an artificial acceleration significantly over-predict charge segregation and the wall becomes rapidly sheeted with a single layer of strongly charged particles.

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