Print Email Facebook Twitter Efficiency analysis and optimisation of DEM for railway ballast track simulations Title Efficiency analysis and optimisation of DEM for railway ballast track simulations: Multi-layer shape model of lateral resistance Author Jia, W. (TU Delft Railway Engineering) Markine, V.L. (TU Delft Railway Engineering) Guo, Y. (TU Delft Railway Engineering) Date 2023 Abstract The railway ballast layer provides the function of bearing loading, resisting geometry degradation, and drainage. In those related research, the behaviour of ballast assembly can be obtained by laboratory (or in-situ) tests. Limited simulation methods can be used to analyse the behaviour of ballast particles at the mesoscopic level. The numerical simulations based on the Discrete Element Method (DEM) are employed, which treat every ballast particle as a calculation component. However, the efficiency of DEM simulation is very low due to the algorithm and a very large number of elements. This paper analysed the efficiency-related questions of the DEM modelling. The influence of particle shape and contact properties on the force behaviour is studied. Further, an optimised multi-layer ballast track model is introduced based on the most influential ballast areas. In such areas, particles are generated with an irregular shape to ensure the reliability of results, and particles except that area are generated with a rolling resisted ball shape to decrease the number of elements. A series of lateral resistance simulations are conducted to show and validate the accuracy and efficiency of this method in the dimension of the single sleeper section. Results show that this optimised multi-layer model building method largely improves efficiency, and it can provide accurate data. Subject DEMModel optimisationRailway ballastRailway trackSimulation efficiency To reference this document use: http://resolver.tudelft.nl/uuid:d982950b-8d5e-4c2e-af00-19f1d5c67087 DOI https://doi.org/10.1016/j.trgeo.2023.100977 ISSN 2214-3912 Source Transportation Geotechnics, 40 Part of collection Institutional Repository Document type journal article Rights © 2023 W. Jia, V.L. Markine, Y. Guo Files PDF 1_s2.0_S2214391223000508_main.pdf 17.44 MB Close viewer /islandora/object/uuid:d982950b-8d5e-4c2e-af00-19f1d5c67087/datastream/OBJ/view