Although the phenomena related to the multiphase flow can be found in many kinds of industrial and engineering applications, the physical mechanism of the multiphase flow has not been investigated in detail. The major reason for the lack of data in the multiphase flow lies in the difficulties in measuring the flow quantities of the multiple phases simultaneously. Presently, the visualization and the PIV measurement have been carried out about the both phases of the liquid-liquid two-phase flow. The difference in the refractive indices makes the visualization in the vicinity of the boundary of the multiple phases very difficult. In this study, the refractive index of the aqueous phase has been equalized to that of the oil phase by adjusting the concentration of the aqueous solution. As for the surrounding fluid, silicon oil is chosen and as for the droplet, the aqueous solution of glycerol is prepared whose refractive index matches that of silicon oil. Both phases are seeded with neutrally buoyant particles. The droplet is slightly colored with Rhodamine B so that the position of the invisible droplet can be identified. The difference in the background brightness in both phases helps PIV algorithm in distinguishing the motions in each phases. The results show the details of the flow structures both around and inside of a falling droplet simultaneously.
It is only until this decade that several attempts are initiated to apply artificial neural networks (ANN) to problems in computat,ional fluid dyna.mics (CFD).The purpose of this study is to propose a new approach for flow prediction by using feedforward neural networks and fluid dynamics knowledge. In this paper, a representative hydraulic flow problem, the two-dimensional I(6rmhn vortex street around a static prism with an elongated rectangular cross section, is examined. Several precalculated flow solutions with different Reynolds numbers and phases of vortex generation are used to train the neural networks. As the result, flow patterns of new Reynolds numbers and phases are obtained. This study reveals the potential of using artificial neural networks for estimating flow patterns without doing the complicate and time-conwming CFD simulation. The computing time of this ANN approach is greatly saved c&paring with CFD simulation. Furthermore, the estimation accuracy is also very encouraging.
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