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In order to study the fast filling problem of large-capacity on-board hydrogen storage tank for highway passenger cars, a computational fluid dynamics (CFD) simulation model of 134L large-capacity hydrogen storage tank was established. By simulating different pre-cooling temperatures and mass flow rates, the temperature distribution and thermal transmission in the tank were observed. Due to the large ratio of length to diameter of the hydrogen tank, the temperature distribution is extremely uneven during the whole filling process, and the high temperature area is mainly concentrated in the tank tail. And the heat transfer between the gas and the tank wall is not obvious under the low and constant mass flow rate. The temperature rise process during the whole filling process under different mass flow conditions was simulated to satisfy the highest safe temperature limit.

Recently, several datasets have become available for occupant monitoring algorithm development, including real and synthetic datasets. However, real data acquisition is expensive and labeling is complex, while virtual data may not accurately reflect actual human physiology. To address these issues and obtain high-fidelity data for training intelligent driving monitoring systems, we have constructed a hybrid dataset that combines real driving image data with corresponding virtual data generated from 3D driving scenarios. We have also taken into account individual anthropometric measures and driving postures. Our approach not only greatly enriches the dataset by using virtual data to augment the sample size, but it also saves the need for extensive annotation efforts. Besides, we can enhance the authenticity of the virtual data by applying ergonomics techniques based on RAMSIS, which is crucial in dataset construction.