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A new cavity model that can explain the interaction between viscous effects including vortices and cavitation bubbles is presented in this study. This model, which is named a bubble two-phase flow (BTF) model, treats the inside and outside of a cavity as one continuum by regarding the cavity as a compressible viscous fluid whose density changes greatly. Navier–Stokes equations including cavitation bubble clusters are solved in finite-difference form by a time-marching scheme, where the growth and collapse of a bubble cluster is given by a modified Rayleigh's equation. Computation was made on a two-dimensional flow field around a hydrofoil NACA0015 at angles of attack of 8° and 20°. The Reynolds number was 3 × 105. The experiments were also performed at the same Reynolds number for comparison. The computed results by the BTF cavity model can express the feature of cloud-type cavitation shed from the trailing edge of the attached cavities when the angle of attack is 8°. It shows the mechanism of cavitation cloud generation and large-scale vortices. The boundary layer separates at the cavity leading edge. Then it rolls up and produces the cavitation cloud. In other words, the instability of the shear layer may produce the cavitation cloud. When the angle of attack is 20°, the flow was fully separated from the leading edge of the hydrofoil and vortex cavitation occurs in the separated region. The BTF cavity model can also express the generation of such vortex cavitation and the effect of cavitation nuclei in the uniform flow.

The cross section, the deuteron vector A(d)(y) and tensor analyzing powers A(ij), the polarization transfer coefficients K(y('))(ij), and the induced polarization P(y(')) were measured for the dp elastic scattering at 270 MeV. The cross section and A(d)(y) are well reproduced by Faddeev calculations with modern data-equivalent nucleon-nucleon forces plus the Tucson-Melbourne three-nucleon force. In contrast, A(ij), K(y('))(ij), or P(y(')) are not described by such calculations. These facts indicate the deficiencies in the spin dependence of the Tucson-Melbourne force and call for extended three-nucleon force models.

Generation mechanism of cloud cavitation on a hydrofoil section was investigated in a sequence of experiments through observation of cloud cavitation by high-speed video and high-speed photo as well as pressure measurements by pressure pick-ups and a hydrophone. The mechanism was also investigated by controlling cloud cavitation with an obstacle fitted on the foil surface. From the results of these experiments, it was found that the collapse of a sheet cavity is triggered by a re-entrant jet rushing from the trailing edge to the leading edge of the sheet cavity, and consequently, the sheet cavity is shed in the vicinity of its leading edge and thrown downstream as a cluster of bubbles called cloud cavity. In other words, the re-entrant jet gives rise to cloud cavitation. Moreover, cloud cavitation could be controlled effectively by a small obstacle placed on the foil. It resulted in reduction of foil drag and cavitation noise.

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The structure of flow around unsteady cloud cavitation on a stationary two-dimensional hydrofoil was investigated experimentally using a conditional sampling technique. The unsteady flow velocity around the cloud cavitation was measured by a Laser Doppler Anemometry (LDA) and matched with the unsteady cavitation appearance photographed by a high-speed camera. This matching procedure was performed using data from pressure fluctuation measurements on the foil surface. The velocities were divided into two components using a digital filter, i.e., large-scale (low-frequency) and small-scale (high frequency) ones. The large-scale component corresponds with the large-scale unsteady cloud cavitation motion. In this manner, the unsteady structure of the cloud cavitation was successfully measured. The experimental result showed that the cloud cavitation observed at the present experiment had a vorticity extremum at its center and a cluster containing many small cavitation bubbles. The convection velocity of the cavitation cloud was much lower than the uniform velocity. The small-scale velocity fluctuation was not distributed uniformly in the cavitation cloud, but was concentrated near its boundary.

There is some ambiguity in the 3D shape of an object when the number of observed views is small. Because of this ambiguity, although a 3D object reconstructor can be trained using a single view or a few views per object, reconstructed shapes only fit the observed views and appear incorrect from the unobserved viewpoints. To reconstruct shapes that look reasonable from any viewpoint, we propose to train a discriminator that learns prior knowledge regarding possible views. The discriminator is trained to distinguish the reconstructed views of the observed viewpoints from those of the unobserved viewpoints. The reconstructor is trained to correct unobserved views by fooling the discriminator. Our method outperforms current state-of-theart methods on both synthetic and natural image datasets; this validates the effectiveness of our method.