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Problem with determining the vehicle impact velocity for car bodies breaking apart

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Języki publikacji
EN
Abstrakty
EN
This article investigates the impact of a passenger car on a tree, which resulted in the car body breaking apart. A side impact of the car on a tree at high driving speeds is not a standard test in the provisions of the applicable Directives of the European Economic Community, even though the impact poses a serious threat to the driver and the passengers. The threat comes from a deep impaction of the barrier into the body which damages the safety cage. For such impacts, it is very difficult for the vehicle speed to be reconstructed. In practice, expert witnesses and appraisers usually disregard the bodybreaking-apart-related energy due to a difficulty in establishing the data for such calculations, which leads to simplifications and speed underestimates. Performing the right simulation of such impacts with accident reconstruction programs without determining the adequate input data for calculations is also impossible to calculate. This paper presents a range of studies and calculations for such incidents and for identifying the input parameters for collision simulations. The approach presented in this article should be used by expert witnesses and researchers. Therefore, this paper provides insights into theory and practice.
Czasopismo
Rocznik
Strony
75--86
Opis fizyczny
Bibliogr. 26 poz.
Twórcy
  • Bydgoszcz University of Science and Technology, Faculty of Mechanical Engineering; al. prof. S. Kaliskiego 7, 85-796, Bydgoszcz, Poland
  • Almot-Ekspert, Technical Department; Janusza Kusocińskiego 20, 86-032 Niemcz, Poland
  • Koszalin University of Technology, Department of Production Engineering; Racławicka 15, 75-620 Koszalin, Poland
  • Koszalin University of Technology, Department of Production Engineering; Śniadeckich 2, 75-620 Koszalin, Poland
  • Stanislaw Staszic University of Applied Sciences in Pila, Department of Transport; Podchorążych 10, 64-920 Piła, Poland
Bibliografia
  • 1. Stan bezpieczeństwa ruchu drogowego oraz działania realizowane w tym zakresie w 2020r. National Road Safety Council. [In Polish: The state of road traffic safety and activities carried out in this area in 2020]. Available at: https://www.krbrd.gov.pl/wp-content/uploads/2021/05/Stan-bezpieczenstwa-ruchu-drogowegooraz-dzialania-realizowane-w-tym-zakresie-w-2020-r..pdf.
  • 2. Baranowski, P. & Damaziak, K. Numerical simulation of vehicle–lighting pole crash tests: Parametric study of factors influencing predicted occupant safety levels. Materials. 2021. Vol. 14. Iss. 11. No. 2822. P. 1-19. DOI: 10.3390/ma14112822.
  • 3. Jedliński, T & Buśkiewicz, J. & Fritzkowski, P. Numerical and experimental analyses of a lighting pole in terms of passive safety of 100HE3 class. Vibrations in Physical Systems. 2020. Vol. 31. P. 1-8.
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  • 7. Kostek, R. & Aleksandrowicz, P. Identification of the parameters of a vehicle crashing into a round pillar. Journal of Theoretical and Applied Mechanics. 2020. Vol. 58. P. 233-245. DOI: 10.15632/jtam-pl/115550.
  • 8. Liu, Q. & Liu, J. & Wu, X. & Cao, L. & Guan, F. An inverse reconstruction approach considering uncertainty and correlation for vehicle-vehicle collision accidents. Structural and Multidisciplinary Optimization. 2019. Vol. 60. P. 681-698. DOI: 10.1007/s00158-019-02231-9.
  • 9. Gidlewski, M. & Prochowski, L. & Jemioł, L. & Żardecki, D. The process of front-to-side collision of motor vehicles in terms of energy balance. 2019. Nonlinear Dynamics. Vol. 97. P. 1877-1893. DOI: 10.1007/s11071-018-4688-x.
  • 10. Li, Q. & Wu, L. et al. Multi-objective optimization design of B-pillar and rocker sub-systems of battery electric vehicle. Structural and Multidisciplinary Optimization. 2021. Vol. 64. No. 6. P. 3999-4023. DOI: 10.1007/s00158-021-03073-0.
  • 11. Bułka, D.V. SIM4 – Instrukcja obsługi. CIBID. 2020. [In. Polish: V-SIM4 – User manual 2020]. 12. Polska norma: PN-EN 10002-1 (EN ISO 6892-1:2009). [In. Polish: Polish Standard: PN-EN 10002-1 (EN ISO 6892-1:2009)].
  • 13. ANSYS. Available at: https://www.ansys.com/.
  • 14. Aleksandrowicz, P. Modeling head-on collisions: The problem of identifying collision parameters. 2020. Applied Science. Vol. 10. No. 18. Paper No. 6212. DOI: 10.3390/app10186212.
  • 15. Vangi, D. & Cialdai, C. & Gulino, MS. Vehicle stiffness assessment for energy loss evaluation in vehicle impacts. 2019. Forensic Science International. Vol. 300. P. 136-144. DOI: 10.1016/j.forsciint.2019.04.031.
  • 16. Kučera, P. & Pištěk, V. Prototyping a system for truck differential lock control. 2019. Sensors. Vol. 19. No. 16. P. 1-18. DOI: 10.3390 / s19163619.
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  • 18. Zalewski, J. Selected problems of motor vehicle maintenance after side impact collision. In: MATEC Web Conf. 2019. Paper No. 01019 182. DOI: 10.1051/matecconf/201818201019.
  • 19. Smit, S. & Tomasch, E. & Kolk, H. & Plank, M. & Gugler, J. & Glaser, H. Evaluation of a momentum based impact model in frontal car collisions for the prospective assessment of ADAS. European Transport Research Review. 2019. Vol. 11. DOI: 10.1186/s12544-018-0343-3.
  • 20. Gulyaev, V. & Loginov, N. & Kozlov, A. Method of designing the superstructure of the car body based on the requirements of low-speed collisions. In: 9th International Scientific Practical Conference on Innovative Technologies in Engineering. Journal of Physics. Conference Series. 2018. Vol. 1059. DOI: 10.1088/1742-6596/1059/1/012021.
  • 21. Fomin, O. & Lovska, A. & Pištěk, V & Kučera, P. Research of stability of containers in the combined trains during transportation by railroad ferry. MM Science Journal. 2020. Vol. 1. P. 3728-3733. DOI: 10.17973/MMSJ.2020_03_2019043.
  • 22. Mrowicki, A. & Krukowski, M. & Turboś, F & Kubiak, P. Determining vehicle pre-crash speed in frontal barrier crashes using artificial neural network for intermediate car class. Forensic Science International. 2020. Vol. 308. P. 110179. DOI: 10.1016/j.forsciint.2020.110179.
  • 23. Vangi, D. & Begani, F. & Spitzhüttl, F & Gulino, MS. Vehicle accident reconstruction by a reduced order impact model. Forensic Science International. 2019. Vol. 298. P. 426-452. DOI: 10.1016/j.forsciint.2019.02.042.
  • 24. Cao, Y. & Xingwang, Y. & Geng, H. A method for calculating the velocity of corner-to-corner rear-end collisions of vehicles based on collision deformation analysis. Applied Science. 2021. Vol. 11. Iss. 22. No 10964. DOI: 10.3390/app112210964.
  • 25. Qijun, C. & Yuxi, X. & Yu, A. & Tiange, L. & Guorong, C. & Shaofei, R. & Chao, W. & Shaofan, L. A deep neural network inverse solution to recover pre-crash impact data of car collisions. Transportation Research Part C: Emerging Technologies. 2021. Vol. 126. Paper No. 103009. DOI: 10.1016/j.trc.2021.103009.
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Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-06c2fa92-0df1-492e-9005-b6d71bb32873
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