• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.6
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• pp.1357-1363
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• 1994

A Monte-Carlo simulation model is developed to predict size distribution produced by the coalescence of air bubbles in turbulent shear f1ow. The simulation consists of generating a population of air bubbles into the initial positions at each time step and tracking them by simulating motions and checking collisions. The radial displacement of air bubbles in the simulation model is produced by numerically solving an advective diffusion equation. Longitudinal displacements are generated from the logarithmic flow velovity distribution and the bubble rise velocity. Collision of air bubbles for each time step is detected by a geometric test using their relative positions at the beginning of the time step and relative displacements during the time step. At the end of the time step, the total number of bubbles, their positions, and sizes are updated. The computer program is coded such that minimum storages for sizes and positions of bubbles are required.

• Journal of KIISE
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• v.42 no.7
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• pp.910-918
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• 2015

This paper presents a parameter visualization technique to overcome the limitation of the naked eye in contrast-enhanced ultrasonography. A method is also proposed to compensate for the distortion and noise in ultrasound image sequences. Meaningful parameters for diagnosing liver disease can be extracted from the dynamic patterns of the contrast enhancement in ultrasound images. The visualization technique can provide more accurate information by generating a parametric image from the dynamic data. Respiratory motions and noise from micro-bubble in ultrasound data may cause a degradation of the reliability of the diagnostic parameters. A multi-stage algorithm for respiratory motion tracking and an image enhancement technique based on the Markov Random Field are proposed. The usefulness of the proposed methods is empirically discussed through experiments by using a set of clinical data.