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The clearance flow patterns and pressure distribution determine the clearance axial hydraulic thrust of a pump turbine, which has a substantial impact on the unit axial imbalance. However, due to the tiny size and complex shape of the clearance flow channel, there is no detailed description of the flow patterns and pressure characteristics. In this study, we conducted a model test with particle image velocimetry (PIV) measurements and CFD simulation of a rotating disk flow that was a simplified model of the pump-turbine clearance flow. It is shown that a typical Batchelor flow is formed in the clearance region, demonstrating a “core region + double-boundary layers” distribution for the circumferential velocity along the clearance height direction; the core region rotates at a speed of only around 41–42% of the rotating disk speed and is independent of the clearance inlet pressure and clearance height. Driven by centrifugal force, the flow is radially outward around the rotating disk, while inward around the stationary disk in the meridian section, showing secondary flow vortices. The pressure in the clearance region has a circumferentially symmetric and radially quadratic distribution. Based on the liquid differential equilibrium equation and core region circumferential velocity, the pressure and clearance axial hydraulic thrust in the clearance region can be expressed as a function of the clearance inlet pressure and the square of the runner rotating speed. These findings can be used to investigate axial force imbalance issues of the pump-turbine unit.

Measuring the perceptual quality of images automatically is an essential task in the area of computer vision, as degradations on image quality can exist in many processes from image acquisition, transmission to enhancing. Many Image Quality Assessment(IQA) algorithms have been designed to tackle this problem. However, it still remains un settled due to the various types of image distortions and the lack of large-scale human-rated datasets. In this paper, we propose a novel algorithm based on the Swin Transformer [31] with fused features from multiple stages, which aggregates information from both local and global features to better predict the quality. To address the issues of small-scale datasets, relative rankings of images have been taken into account together with regression loss to simultaneously optimize the model. Furthermore, effective data augmentation strategies are also used to improve the performance. In comparisons with previous works, experiments are carried out on two standard IQA datasets and a challenge dataset. The results demonstrate the effectiveness of our work. The proposed method outperforms other methods on standard datasets and ranks 2nd in the no-reference track of NTIRE 2022 Perceptual Image Quality Assessment Challenge [53]. It verifies that our method is promising in solving diverse IQA problems and thus can be used to real-word applications., Comment: 8 pages, 4 figures

The experiment of hot dip aluminizing had been carried out in order to study the interaction between die casting die and aluminum alloy as well as how Si affects the soldering tendency in die and aluminum alloy casting. The experimental results show that intermetallic alloy layer is formed when non-inoculation samples of die steel are dipped in A380 melt. However, no inactive diffusion layers appear when inoculation samples are dipped in A380 melt. The existence of Si decreases the activity coefficient of Al in the phase of Fe-Si-Al and prevents aluminum atoms moving from molten Al to the steel substrate. As a result, Si reduces the interaction between die steel and molten aluminum alloy and then effectively impedes the soldering tendency of aluminum die casting.