Abstract
We report the results of the first systematic Lagrangian experimental investigation in a previously unexplored regime of very light (air bubbles in water) and large (D/η ≫ 1) particles in turbulence. Using a traversing camera setup and particle tracking, we study the Lagrangian acceleration statistics of ∼3 mm diameter (D) bubbles in a water tunnel with nearly homogeneous and isotropic turbulence generated by an active grid. The Reynolds number (Reλ) is varied from 145 to 230, resulting in size ratios, D/η, in the range of 7.3–12.5, where η is the Kolmogorov length scale. The experiments reveal that gravity increases the acceleration variance and reduces the intermittency of the probability density function (PDF) in the vertical direction. Once the gravity offset has been subtracted, the variances of both the horizontal and vertical acceleration components are about 5 ± 2 times larger than those measured in the same flow for fluid tracers. Moreover, for these light particles, the experimental acceleration PDF shows a substantial reduction of intermittency at growing size ratios, in contrast with neutrally buoyant or heavy particles. All these results closely match numerical simulations of finite-sized bubbles with the Faxén corrections.
Highlights
We report the results of the first systematic Lagrangian experimental investigation in a previously unexplored regime of very light and large (D/η 1) particles in turbulence
We found that the acceleration variance a 2 y in the vertical direction is augmented by an offset that depends on g2
We carried out measurements of Lagrangian acceleration in the previously unexplored regime of large and very light particles in turbulence
Summary
Experimental setup In our experiments, air bubbles in water, ≈ 10−3, are dispersed in a turbulent flow in the 8 m high Twente Water Tunnel (TWT) facility (see figure 2(a)). The bubbles are generated by blowing air through capillary islands (diameter 500 μm) placed below the active grid. The bubbles rise through the measurement section along with the imposed water flow and escape through an open vent on top of the water tunnel. The circular Hough transform method [27] is successfully used to detect more than 90% of the bubbles (which are in focus) in the images. Edge-detection algorithms are used to extract attributes from images, and these are followed by linking procedures (such as the Hough transform) to assemble edge pixels into useable edges [28].
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