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• Explored sloshing in cryogenic hydrogen tanks for trains. • Simulations conducted at 4 vibration cases. • Used transient volume-of-fluid phase change model. • Increased frequency led to increased boil-off gas generation. • Highlighted temperature and pressure changes with vibrations. This study examines the effects of hydrogen sloshing on internal pressure, temperature, and fluid behavior liquefied-hydrogen storage tanks designed for train usage by applying the natural frequency and frequency conditions from train vibration test standards. Notably, it investigates the impact on BOG generation using a transient volume-of-fluid phase change model. Here, simulations were conducted at vibrations of 0, 0.53, 1.53, and 3 Hz, which were established using sine wave acceleration. The results demonstrated that sloshing increased with higher frequencies, thereby resulting in a more intense heat transfer between the wall of the tanks and free surface of hydrogen and an increase in the BOG generation. Compared to the 0 Hz baseline, BOG generation increased by 13, 44, and 66 % at 0.53, 1.53, and 3 Hz, respectively. [ABSTRACT FROM AUTHOR]

Quantitative microstructural interpretations were carried out without human involvement through an integrated combination of deep learning and focused ion beam-scanning electron microscopy (FIB-SEM) analytics on Ni/Y 2 O 3 -stabilized ZrO 2 (Ni/YSZ) cermets. The Ni/YSZ/pore composites were analyzed for the automated extraction of microstructural parameters to prevent the subjective analysis problems and unavoidable artifacts frequently encountered in lengthy image processing tasks and eliminate biased evaluations. Considering the high volume of image data and future expectations for electron microscopy usage, FIB-SEM was efficiently combined with semantic segmentation. Traditional image processing analysis tools are combined with phase separation predictions by semantic segmentation algorithms, leading to a quantitative evaluation of microstructural parameters. The combined strategy enables one to significantly enhance poor image quality originating from artifacts in electron microscopy, including charging effects, curtain effects, out-of-focus problems, and unclear phase boundaries encountered in searching for high-efficiency solid oxide fuel cells (SOFCs). • Semantic segmentation was applied to image-based phase identification in SOFCs. • Microstructural quantification was made using deep learning-predicted images. • The stereological approach was synergistically combined with semantic segmentation. [ABSTRACT FROM AUTHOR]