In nuclear engineering fields, gas-liquid bubbly flows exist in channels with various shape and size crosssections. Although many experiments have been carried out especially in circular pipes, those in a noncircular duct are very limited. To contribute to the development of gas-liquid bubbly flow model for a noncircular duct, detail measurements for the air-water bubbly flow in a square duct (side length: 0.136 m) were carried out by an X-type hot-film anemometry and a multi-sensor optical probe. Local flow parameters of the void fraction, bubble diameter, bubble frequency, axial liquid velocity and turbulent kinetic energy were measured in 11 two-phase flow conditions. These flow conditions covered bubbly flow with the area-averaged void fraction ranging from 0.069 to 0.172. A pronounced corner peak of the void fraction was observed in a quarter square area of a measuring cross-section. Due to a high bubble concentration in the corner, the maximum values of both axial liquid velocity and turbulent kinetic energy intensity were located in the corner region. It was pointed out that an effect of the corner on accumulating bubble in the corner region changed the distributions of axial liquid velocity and turbulent kinetic energy intensity significantly.

The rapid development of new generation radio interferometers such as the Square Kilometer Array (SKA) has opened up unprecedented opportunities for astronomical research. However, anthropogenic Radio Frequency Interference (RFI) from communication technologies and other human activities severely affects the fidelity of observational data. It also significantly reduces the sensitivity of the telescopes. We proposed a robust Convolutional Neural Network (CNN) model to identify RFI based on machine learning methods. We overlaid RFI on the simulation data of SKA1-LOW to construct three visibility function datasets. One dataset was used for modeling, and the other two were used for validating the model’s usability. The experimental results show that the Area Under the Curve (AUC) reaches 0.93, with satisfactory accuracy and precision. We then further investigated the effectiveness of the model by identifying the RFI in the actual observational data from LOFAR and MeerKAT. The results show that the model performs well. The overall effectiveness is comparable to AOFlagger software and provides an improvement over existing methods in some instances.

Because a pool scrubbing is important for reducing radioactive aerosols to the environment for a nuclear reactor in a severe accident situation, many researches have been performed. However, decontamination factor (DF) dependence on aerosol concentration was seldom considered in an aerosol number concentration with limited aerosol coagulation. To investigate an existence of DF dependence on the concentration, DF in a pool scrubbing with 2.4 m water submergence was derived from aerosol measurements by light scattering aerosol spectrometers. It was observed that DF increased monotonically with decreasing particle number concentration in a constant thermohydraulic condition: a gradual increase from 10 to 32 in the range of 1.3×1011 - 8.0×1011/m3 at the inlet and a significant increase from 32 to 77 in the range of 3.6×1010 - 1.3×1011/m3. Two validation experiments were conducted in the range with the gradual DF increase to confirm whether the DF dependence is a real pool scrubbing phenomenon. In addition, characteristics of the DF dependence in different water submergences were investigated experimentally. It was found that the DF dependence became more significant in higher water submergence. Significant DF dependence was observed in the condition of the water submergence higher than 1.6 m and the inlet particle number concentration less than around 1×1011 /m3. It is recommended to perform further analysis for the DF dependence mainly in such condition since it could make a difference to both experiment and model of the pool scrubbing.

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