Numerical analysis of freight wagon rolling dynamics on a classification hump

  • Shuxrat Djabbarov orcid

    Department of Wagons and Wagon Facilities, Tashkent State Transport University, Tashkent 100069, Uzbekistan

  • Bakhrom Abdullayev orcid

    Department of Wagons and Wagon Facilities, Tashkent State Transport University, Tashkent 100069, Uzbekistan

  • Aziz Gayipov orcid

    Department of Wagons and Wagon Facilities, Tashkent State Transport University, Tashkent 100069, Uzbekistan

  • Abdusaid Yuldashov orcid

    Department of Wagons and Wagon Facilities, Tashkent State Transport University, Tashkent 100069, Uzbekistan

  • Nodir Botir o’g’li Adilov orcid

    Department of Wagons and Wagon Facilities, Tashkent State Transport University, Tashkent 100069, Uzbekistan

  • Irina Soboleva orcid

    Department of Wagons and Wagon Facilities, Tashkent State Transport University, Tashkent 100069, Uzbekistan

Article ID: 4108
Keywords: classification hump; freight wagon; longitudinal dynamics; rolling resistance; relative wind; factorial design; uncertainty quantificatio

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.

Published
2026-08-18
How to Cite
Djabbarov, S., Abdullayev, B., Gayipov, A., Yuldashov, A., Botir o’g’li Adilov, N., & Soboleva, I. (2026). Numerical analysis of freight wagon rolling dynamics on a classification hump. Sound & Vibration, 60(5). https://doi.org/10.59400/sv4108
Section
Article

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