Numerical analysis of freight wagon rolling dynamics on a classification hump
Abstract
Accurate prediction of freely rolling freight-wagon speed is required to set retarder demand, maintain cut separation, and limit coupling energy in classification yards. This study develops a reproducible one-dimensional model of wagon motion along the first descending section of a classification hump. The governing equation combines the downslope gravitational component, equivalent mechanical resistance, aerodynamic drag based on signed relative air velocity, and rotating-mass inertia expressed through an effective mass. Three modelling levels are compared under identical initial conditions: gravity-only motion, constant mechanical resistance with rotating inertia, and the complete relative-wind formulation. Simulations cover a 60 m section, an initial speed of 0.50 m/s, wagon masses of 68 and 24 t, and track-aligned wind from a 12 m/s headwind to a 12 m/s tailwind. The gravity-only model overpredicts final speed by 7.8–19.7% for the loaded wagon and 3.2–41.3% for the empty wagon. For the loaded wagon, changing from headwind to tailwind raises final speed from 3.869 to 4.295 m/s and reduces travel time from 27.21 to 24.89 s. The time-stepping implementation agrees with the closed-form zero-drag solution to within 0.011%. A complete 24 factorial design identifies wind, gradient, and equivalent resistance as the dominant factors and reveals a substantial wind–mass interaction. A 3,500-run Monte Carlo analysis yields final-speed percentiles of 3.659, 4.074, and 4.403 m/s at P5, P50, and P95, respectively. The model is suitable for preliminary yard assessment and measurement planning; operational use requires yard-specific resistance calibration and independent field validation using separate calibration and validation datasets.
Copyright (c) 2026 Shuxrat Djabbarov, Bakhrom Abdullayev, Aziz Gayipov, Abdusaid Yuldashov, Nodir Botir o’g’li Adilov, Irina Soboleva

This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[1]Stock WA, Sakasita M, Elliott CV, et al. PROFILE: Gradient simulation for rail hump classification yards. Transportation Research Record. 1980; (744): 52–58.
[2]Petracek SJ, Savage NP. Freight Car Rollability: Task 1—Review and Requirements. US Department of Transportation; 1980.
[3]Wong PJ. Railroad Classification Yard Technology Manual. Vol. III: Freight Car Rolling Resistance. US Department of Transportation; 1981.
[4]Kozachenko D, Bobrovskyi V, Demchenko Y. A method for optimization of time intervals between rolling cuts on sorting humps. Journal of Modern Transportation. 2018; 26(3): 189–199. doi: 10.1007/s40534-018-0161-2
[5]Saidivaliev SU. Determining the kinematic parameters of railcar motion in hump yard retarder positions. AIP Conference Proceedings. 2023; 2612: 060016. doi: 10.1063/5.0115115
[6]Turanov K, Gordienko A, Saidivaliev S, et al. Designing the height of the first profile of the marshalling hump. E3S Web of Conferences. 2020; 164: 03038. doi: 10.1051/e3sconf/202016403038
[7]Michálek T, Kohout M, Šlapák J, et al. Curving and running resistance of freight trains: current experience with on-track measurements. Vehicle System Dynamics. 2025; 63: 1983–1997. doi: 10.1080/00423114.2024.2398003
[8]Rochard BP, Schmid F. A review of methods to measure and calculate train resistances. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. 2000; 214(4): 185–199. doi: 10.1243/0954409001531306
[9]Wu Q, Ahmad S, Spiryagin M, et al. A method to numerically assess rail vehicle rolling resistance on tangent and curved tracks. Vehicle System Dynamics. 2026. doi: 10.1080/00423114.2026.2615792
[10]Wu Q, Li T, Cole C, et al. A review of air drag related freight train aerodynamics. Part 1: Assessment methods. Heavy Rail. 2026; 2: 100002. doi: 10.1016/j.hrail.2026.100002
[11]Magelli M, Zampieri N. A novel approach for longitudinal train dynamics simulations with multibody codes. Vehicle System Dynamics. 2025; 63(5): 978–996. doi: 10.1080/00423114.2024.2362949
[12]Bosso N, Magelli M, Zampieri N. Validation of a new longitudinal train dynamics code for time-domain simulations and modal analyses. International Journal of Transport Development and Integration. 2021; 5(1): 41–56. doi: 10.2495/TDI-V5-N1-41-56
[13]Spiryagin M, Wu Q, Cole C. International benchmarking of longitudinal train dynamics simulators: Benchmarking questions. Vehicle System Dynamics. 2017; 55(4): 450–463. doi: 10.1080/00423114.2016.1270457
[14]Wu Q, Spiryagin M, Cole C, et al. International benchmarking of longitudinal train dynamics simulators: Results. Vehicle System Dynamics. 2018; 56(3): 343–365. doi: 10.1080/00423114.2017.1377840
[15]Wang J, Rakha HA. Longitudinal train dynamics model for a rail transit simulation system. Transportation Research Part C: Emerging Technologies. 2018; 86: 111–123. doi: 10.1016/j.trc.2017.10.011
[16]Bosso N, Magelli M, Trinchero R, et al. Application of machine learning techniques to build digital twins for long train dynamics simulations. Vehicle System Dynamics. 2024; 62(1): 21–40. doi: 10.1080/00423114.2023.2174885
[17]Zhang S, Huang P, Yan W. A data-driven approach for railway in-train forces monitoring. Advances in Engineering Informatics. 2024; 59: 102258. doi: 10.1016/j.aei.2023.102258
[18]Zhai W, Stichel S, Ling L. Train–track coupled dynamics problems in heavy-haul rail transportation. Vehicle System Dynamics. 2025; 63(7): 1187–1240. doi: 10.1080/00423114.2025.2494834
[19]Magelli M, Corrêa PHA, Santos AA. Assessing the effect of modelling approaches for longitudinal train dynamics simulations with a multibody code. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics. 2025; 239(4): 326–338. doi: 10.1177/14644193251368540
[20]Magelli M, Zampieri N, Wu Q. Integration of brake-block thermal equations within a railway-vehicle multibody model: a multiphysics approach. International Journal of Rail Transportation. 2025; 13(1): 69–84. doi: 10.1080/23248378.2023.2301618
[21]Bosso N, Gugliotta A, Magelli M, et al. Integrating longitudinal train dynamics simulations within multibody models. CIVIL-COMP Conference. 2024; 7: 1–11. doi: 10.4203/ccc.7.5.12
[22]Bernal E, Wu Q, Spiryagin M, et al. Augmented digital twin for railway systems. Vehicle System Dynamics. 2024; 62(1): 67–83. doi: 10.1080/00423114.2023.2194543
[23]Tang Z, Ling L, Zhang T, et al. Towards digital twin trains: implementing a cloud-based framework for railway vehicle dynamics simulation. International Journal of Rail Transportation. 2025; 13(3): 444–467. doi: 10.1080/23248378.2024.2355578
[24]Hofmeier T, Cichon M. Introducing scaled model development to on-sight automatic train operation. In: Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems; 2–4 May 2024; Angers, France. doi: 10.5220/0012691500003702
[25]Djabbarov S, Saidivaliev S, Abdullaev B, et al. Mathematical model of wagon wheels rolling along the hump profile. E3S Web of Conferences. 2023; 460: 06007. doi: 10.1051/e3sconf/202346006007
[26]Djabbarov S, Abdullaev B. Updating the parameters of the “calculated runners” used in the design of sorting slides. AIP Conference Proceedings. 2026; 3374: 050024. doi: 10.1063/5.0317222
[27]Turanov K, Gordienko A, Saidivaliev S, et al. Kinematic characteristics of the car movement from the top to the calculation point of the marshalling hump. In: Murgul V, Pukhkal V (editors). Advances in Intelligent Systems and Computing. Springer; 2021. doi: 10.1007/978-3-030-57450-5_29
[28]Aredah AS, Fadhloun K, Rakha HA. NeTrainSim: A network-level simulator for modeling freight train longitudinal motion and energy consumption. Railway Engineering Science. 2024; 32: 480–498. doi: 10.1007/s40534-024-00331-x
[29]Corniani L, Schito P, Bell J, et al. A review of freight train aerodynamics. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. 2026; 240(2): 133–150. doi: 10.1177/09544097251382265
[30]Wu Q, Li T, Cole C, et al. A review of air drag related freight train aerodynamics. Part 2: Energy efficient designs. Heavy Rail. 2026; 2: 100003. doi: 10.1016/j.hrail.2026.100003
[31]Bosso N, Cantone L, Gugliotta A, et al. Introduction of digital twins in the longitudinal train dynamics simulation of freight train air brake operations. In: Huang W, Ahmadian M (editors). Advances in Dynamics of Vehicles on Roads and Tracks III. Springer; 2025. pp. 297–306. doi: 10.1007/978-3-031-66971-2_32




