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AbstractRail models may be simplified at different levels in wind tunnel tests and numerical simulations. This work used delayed detached eddy simulations to analyze and compare the aerodynamic loads (drag, side force, lift, and rolling moment), surface pressure, and flow characteristics around a train model placed on subgrades with three different rail models: no-rail, simplified rail, and realistic rail. The simulations were conducted at a large yaw angle of 60°. The mean aerodynamic characteristics of the car bodies and bogies were similar with the simplified rail model and realistic rail model, while those were completely different without the rail model. The difference in aerodynamic loads between the no-rail model and the realistic rail model was as high as 53.1%, whereas the difference between the simplified and realistic rail models was only 7.7%. And for fluctuating aerodynamic characteristics of the train, there was still differences between the cases with the simplified and realistic models. Further, the rail model significantly impacted the flow (such as the velocity, turbulent kinetic energy, and vorticity) around the train, especially under the train, resulting in significant differences in the aerodynamic loads in time and frequency domains. For accurate and precise measurements of mean aerodynamic loads, incorporating a rail model (with the simplified model being sufficient) can improve accuracy by 48.0% and precision by 66.9%. When focusing on fluctuating aerodynamic loads, using a realistic rail model is essential to accurately capture amplitude-frequency characteristics. The rail model setup recommendations outlined in this work aim to enhance the accuracy and reliability of various wind tunnel tests and numerical simulations related to train aerodynamics. [ABSTRACT FROM AUTHOR]

Remote sensing based on satellites can provide long-term, consistent, and global coverage of NO2 (an important atmospheric air pollutant) as well as other trace gases. However, satellite data often miss data due to factors including but not limited to clouds, surface features, and aerosols. Moreover, one of the longest continuous observational platforms of NO2 observations from space, OMI, has suffered from missing data over certain rows since 2007, significantly reducing spatial coverage. This work uses the OMI based OMNO2 product, as well as an NO2 product from GOME-2 in combination with machine learning (XGBoost) and spatial interpolation (DINEOF) method to produce a 16-year global daily high spatial-temporal coverage merged tropospheric NO2 dataset (HSTCM-NO2, https://doi.org/10.5281/zenodo.10968462 , Qin et al., 2024), which increases the global spatial coverage of NO2 by ~60 % compared to the original OMINO2 data. The HSTCM-NO2 dataset is validated using upward looking observations of NO2 (MAX-DOAS), other satellites (TROPOMI), and reanalysis products. The comparisons show that HSTCM-NO2 maintains a good correlation with the magnitude of other observational datasets, except for under heavily polluted conditions (>6×1015 molec.cm-2). This work also introduces a new validation technique to validate coherent spatial and temporal signals (EOF) and validates that the HSTCM-NO2 are not only consistent with the original OMNO2 data, but in some parts of the world effectively fill in missing gaps and yield a superior result when analyzing long-range atmospheric transport of NO2. The few differences are also reported to be related to areas in which the original OMNO2 signal was very low, which has been shown elsewhere, but not from this perspective, further validating that applying a minimum cutoff to retrieved NO2 data is essential. The reconstructed data product can effectively extend the utilization value of the original OMNO2 data, and the data quality of HSTCM-NO2 can meet the needs of scientific research. [ABSTRACT FROM AUTHOR]

In this study, the effect of the freight train formation on the train aerodynamic performance was studied. Six cases involving a locomotive (LM) and three different freight vehicles – a boxcar (BC), a tanker (TK), and a double-container freight wagon (DW), were compared and analyzed. The results showed that the aerodynamic drag was significantly affected by the freight train formation type, and the total drag force in Case 2 (LM + BC + DW + TK) was the smallest. Furthermore, the flow field around the train was analyzed to reveal the influence mechanism of the different types of freight train formations on the aerodynamic drag. The car position and the adjacent cars had significant effects on the aerodynamic characteristics of the train. The DW had a greater effect on the pressure and velocity distributions around the train. In addition, the variations of the vortices in the near wake of the same tail car were inconsistent, although there were similar characteristics for the same tail car. [ABSTRACT FROM AUTHOR]