RESEARCH PAPER
Precrash vehicle velocity determination using inverse system and tensor product of Legendre polynomials - subcompact car class
| 1 | Faculty of Automotive and Construction Machinery Engineering, Warsaw University of Technology, Polska |
| 2 | Harbin Institute of Technology, School of Transportation Science and Engineering, China |
CORRESPONDING AUTHOR
Adam Mrowicki
Faculty of Automotive and Construction Machinery Engineering, Warsaw University of Technology, Polska
Faculty of Automotive and Construction Machinery Engineering, Warsaw University of Technology, Polska
Submission date: 2022-09-14
Final revision date: 2022-09-27
Acceptance date: 2022-09-28
Publication date: 2022-10-05
The Archives of Automotive Engineering – Archiwum Motoryzacji 2022;97(3):14–24
KEYWORDS
TOPICS
ABSTRACT
Presented paper discusses new approach to EES parameter determination in frontal car crash based on the tensor product of Legendre polynomials. In this paper Subcompact Car Class was analyzed using that method. Data that was used to perform analyses introduced in this paper was taken from National Highway Traffic Safety Administration (NHTSA) database. Such database consists of considerate number of test cases along with various information including vehicle mass, crash velocity, chassis deformation etc. New approach to the problem of determining the EES parameter was necessary due to the low accuracy of the currently used methods. Linear models used up till now for accident reconstruction show significant error as the relationship between mass, velocity and deformation cannot be well approximated with a flat plane. Proposed model produces better results, because of the nonlinear dependence of said parameters. This paper also includes a calculation example presenting a comparison of linear and nonlinear method on an actual crash test.
REFERENCES (39)
2.
Campbell B.J.: The Traffic Accident Data Project Scale. Proceedings of Collision Investigation Methodology Symposium. 1969.
3.
Cao Y., Luo Y.F.: The Synthesized Method Based on Classical Mechanics and Finite Element for Vehicle Collision Accident Reconstruction Analysis. International Journal of Crashworthiness. 2021, 1–8, DOI:10.1080/13588265.2021.2008741.
4.
Cheney W., Kincaid D.: Numerical Analysis: Mathematics of Scientific Computing. Wadsworth Group, 2002.
5.
Faraj R., Holnicki-Szulc J., Knap L., Seńko J.: Adaptive Inertial Shock-Absorber. Smart Materials and Structures. 2016, 25, DOI:10.1088/0964-1726/25/3/035031.
6.
Geigl B.C., Hoschopf H., Steffan H., Moser A.: Reconstruction of Occupant Kinematics and Kinetics for Real World Accidents. International Journal of Crashworthiness. 2003, 8, 17–27, DOI:10.1533/ijcr.2003.0217.
7.
Gidlewski M., Żardecki D.: Simulation-Based Sensitivity Studies of a Vehicle Motion Model. Transport Means – Proceedings of the International Conference. 2016, 135091, 236–240.
8.
Grolleau V., Galpin B., Penin A., Rio G.: Modelling the Effect of Forming History in Impact Simulations: Evaluation of the Effect of Thickness Change and Strain Hardening Based on Experiments. International Journal of Crashworthiness. 2008, 13, 363–373, DOI:10.1080/13588260801976120.
9.
Han I., Kang H., Park J.C., Ha Y.: Three-Dimensional Crush Measurement Methodologies Using Two-Dimensional Data. Transactions of the Korean Society of Automotive Engineers. 2015, 23, 254–262, DOI: 10.7467/ksae.2015.23.3.254.
10.
Han I.: Vehicle Collision Analysis from Estimated Crush Volume for Accident Reconstruction. International Journal of Crashworthiness. 2019, 24(1), 100–105, DOI: 10.1080/13588265.2018.1440499.
11.
Han I.: Analysis of Vehicle Collision Accidents Based on Qualitative Mechanics. Forensic Science International. 2018, 291, 53–61, DOI: 10.1016/j.forsciint.2018.08.004.
12.
Hight P.V., Fugger T.F., Marcosky J.: Automobile Damage Scales and the Effect on Injury Analysis. SAE Technical Paper. 1992, 90092, DOI: 10.4271/920602.
13.
Iraeus J., Lindquist M.: Pulse Shape Analysis and Data Reduction of Real-Life Frontal Crashes with Modern Passenger Cars. International Journal of Crashworthiness. 2015, 20, 535–546, DOI: 10.1080/13588265.2015.1057005.
14.
Krukowski M., Kubiak P., Mrowicki A., Siczek K., Gralewski J.: Non-Linear Method of Determining Vehicle Pre-Crash Speed Based on Tensor B-Spline Products with Probabilistic Weights - Intermediate Car Class. Forensic Science International. 2018, 293, 7–16, DOI: 10.1016/j.forsciint.2018.10.011.
15.
Kubiak P.: Work of Non-Elastic Deformation against the Deformation Ratio of the Subcompact Car Class Using the Variable Correlation Method. Forensic Science International. 2018, 287, 47–53, DOI: 10.1016/j.forsciint.2018.03.033.
16.
Lindquist M., Hall A., Björnstig U.: Real World Car Crash Investigations - A New Approach. International Journal of Crashworthiness. 2003, 8, 375–384, DOI: 10.1533/ijcr.2003.0245.
17.
Mackay G.M., Hill J., Parkin S., Munns J.A.: Restrained Occupants on the Nonstruck Side in Lateral Collisions. Accident Analysis and Prevention. 1993, 25, 147–152, DOI: 10.1016/0001-4575(93)90054-z.
18.
Mannering F.L., Bhat C.R.: Analytic Methods in Accident Research: Methodological Frontier and Future Directions. Analytic Methods in Accident Research. 2014, 1, 1–22, DOI: 10.1016/j.amar.2013.09.001.
19.
Mchenry B.G.: The Algorithms of CRASH. Southeast Coast Collision Conference. 2001.
20.
Mchenry R.R.: Computer Program for Reconstruction of Highway Accidents. SAE Technical Paper. 1973, 90232, DOI: 10.4271/730980.
21.
Nelson W.D.: The History and Evolution of the Collision Deformation Classification SAE J224 MAR80. SAE Technical Paper. 1981, 810213, DOI: 10.4271/810213.
22.
Neptune J.A.: Crush Stiffness Coefficients, Restitution Constants, and a Revision of CRASH3 & Amp; SMAC. SAE Technical Paper. 1998, 90116, DOI: 10.4271/980029.
23.
Norros I., Kuusela P., Innamaa S., Pilli-Sihvola E., Rajamäki R.: The Palm Distribution of Traffic Conditions and Its Application to Accident Risk Assessment. Analytic Methods in Accident Research. 2016, 12, 48–65, DOI: 10.1016/j.amar.2016.10.002.
24.
Prochowski L., Ziubinski M., Gidlewski M.: Experimental and Analytic Determining of Changes in Motor Cars’ Positions in Relation to Each Other during a Crash Test Carried out to the FMVSS 214 Procedure. Proceedings of the 2018 XI International Science-Technical Conference Automotive Safety, IEEE. 2018, DOI: 10.1109/AUTOSAFE.2018.8373302.
25.
Prochowski L., Gidlewski M., Ziubiński M., Dziewiecki K.: Kinematics of the Motorcar Body Side Deformation Process during Front-to-Side Vehicle Collision and the Emergence of a Hazard to Car Occupants. Meccanica. 2021, 56, 901–922, DOI:10.1007/s11012-020-01274-3.
26.
Ptak M., Wilhelm J., Klimas O., Reclik G., Garbaciak L.: Numerical Simulation of a Motorcycle to Road Barrier Impact. Lecture Notes in Mechanical Engineering. 2019, 565–573, DOI: 10.1007/978-3-030-04975-1_65.
28.
Sharma D., Stern S., Brophy J., Choi E.: An Overview of NHTSA’s Crash Reconstruction Software WinSMASH. Proceedings of the 20th International Technical Conference on Enhanced Safety of Vehicles. 2007, France, Lyon.
29.
Siddall D.E., Day T.D.: Updating the Vehicle Class Categories. SAE Technical Paper. 1996, DOI: 10.4271/960897.
30.
Syad B.A., Salmani E., Ez-Zahraouy H., Benyoussef A.: Computational Method of the Stiffness Coefficients A and B in the Case of Frontal Impact from the Results of the Crash Tests. International Journal of Intelligent Transportation Systems Research. 2021, 19, 587–593, DOI: 10.1007/s13177-021-00266-1.
31.
Vangi D., Cialdai C.: Evaluation of Energy Loss in Motorcycle-to-Car Collisions. International Journal of Crashworthiness. 2014, 19, 361–370, DOI: 10.1080/13588265.2014.899072.
32.
Vangi D.: Simplified Method for Evaluating Energy Loss in Vehicle Collisions. Accident Analysis and Prevention. 2009, 41, 633–641, DOI: 10.1016/j.aap.2009.02.012.
33.
Vangi D.: Vehicle Collision Dynamics: Analysis and Reconstruction, Butterworth-Heinemann. Elsevier-Inc. 2020, 157–191, DOI: 10.1016/B978-0-12-812750-6.00005-6.
34.
Vangi D., Cialdai C., Gulino M.S.: Vehicle Stiffness Assessment for Energy Loss Evaluation in Vehicle Impacts. Forensic Science International. 2019, 300, 136–144, DOI: 10.1016/j.forsciint.2019.04.031.
35.
Vangi D., Begani F., Spitzhüttl F., Gulino M.S.: Vehicle Accident Reconstruction by a Reduced Order Impact Model. Forensic Science International. 2019, 298, 426.e1–426.e11, DOI: 10.1016/j.forsciint.2019.02.042.
36.
Wach W.: Reconstruction of Vehicle Kinematics by Transformations of Raw Measurement Data. Proceedings of the 2018 XI International Science-Technical Conference Automotive Safety, IEEE. 2018, DOI: 10.1109/AUTOSAFE.2018.8373324.
37.
Wach W.: Spatial Impulse-Momentum Collision Model in Programs for Simulation of Vehicle Accidents. Proceedings of the 2020 XII International Science-Technical Conference Automotive Safety, IEEE. 2020, DOI: 10.1109/AUTOMOTIVESAFETY47494.2020.9293494.
38.
Wood D.P., Simms C.K.: Car Size and Injury Risk: A Model for Injury Risk in Frontal Collisions. Accident Analysis and Prevention. 2002, 34, 93–99, DOI: 10.1016/s0001-4575(01)00003-3.
39.
Żuchowski A.: The Use of Energy Methods at the Calculation of Vehicle Impact Velocity. The Archives of Automotive Engineering – Archiwum Motoryzacji. 2015, 68(2), 85–111.
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