PRACA ORYGINALNA
Nonlinear method of precrash vehicle velocity determination based on tensor product of Legendre polynomials - luxury class
1
Institute of Vehicles and Construction Machinery Engineering,, Warsaw University of Technology,
02-524 Warsaw, Poland, Poland
2
Institute of Mathematics, Lodz University of Technology, Poland
3
School of Transportation Science and Engineering, Harbin Institute of Technology, China
4
Institute of Marketing and Sustainable Development, Lodz University of Technology, Poland
Data nadesłania: 08-03-2022
Data ostatniej rewizji: 29-03-2022
Data akceptacji: 31-03-2022
Data publikacji: 31-03-2022
Autor do korespondencji
Adam Mrowicki
Institute of Vehicles and Construction Machinery Engineering,, Warsaw University of Technology, 02-524 Warsaw, Poland, Narbutta, 84, 02-524 Warsaw, Warsaw, Poland
Institute of Vehicles and Construction Machinery Engineering,, Warsaw University of Technology, 02-524 Warsaw, Poland, Narbutta, 84, 02-524 Warsaw, Warsaw, Poland
The Archives of Automotive Engineering – Archiwum Motoryzacji 2022;95(1):53-64
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE
Presented paper discusses a new, nonlinear approach to EES (Equivalent Energy Speed) parameter determination in frontal car collisions. This method is based on tensor product of Legendre polynomials and in this case considers Luxury car class.
Methods that are used up till now are based on a linear dependency between mass, velocity and deformation. This is of course a simplification that was necessary, due to limitation in computation power of computers when this method was introduced decades ago. The contemporary resources allowed Authors to develop a much more sophisticated method. The mathematical model was developed using data shared by National Highway Traffic Safety Administration (NHTSA). This database covers a large number of test cases along with various information including vehicle mass, crash velocity, chassis deformation etc.
New method proves to be more accurate than the currently used approach utilizing linear dependency of deformation force and deformation of the vehicle.
REFERENCJE (39)
1.
Axler S.J.: Linear Algebra Done Right; 2nd ed.; Springer: New York, NY, 1997; ISBN 9780387225951.
2.
Campbell B.J.: The Traffic Accident Data Project Scale. Proceedings of Collision Investigation Methodology Symposium. 1969, 675–681.
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. American Mathematical Society. Pure and Applied Undergraduate Texts. 2002, 2.
5.
Faraj R., Holnicki-Szulc J., Knap L., Seńko J.: Adaptive Inertial Shock-Absorber. Smart Materials and Structures. 2016, 25(3), 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(1), 17–27, DOI: 10.1533/ijcr.2003.0217.
7.
Gidlewski M., Żardecki D.: Simulation-Based Sensitivity Studies of a Vehicle Motion Model. Proceedings of the 20th International Scientific Conference Transport Means 2016. 2016, 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(4), 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(3), 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, 920602, 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(6), 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(4), 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 & Prevention. 1993, 25(2), 147–152, DOI: 10.1016/0001-4575(93)90054-z.
18.
Mannering F., 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, Cocoa Beach, Florida.
20.
Mchenry R.R.: Computer Program for Reconstruction of Highway Accidents. SAE Technical Paper.1973, DOI: 10.4271/730980.
21.
Nelson W.D.: The History and Evolution of the Collision Deformation Classification SAE J224 MAR80. SAE Technical Paper. 1981, 90232, DOI: 10.4271/810213.
22.
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.
23.
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. XI International Science-Technical Conference Automotive Safety. 2018, 136991, 1–5, DOI: 10.1109/AUTOSAFE.2018.8373302.
24.
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(4), 901–922, DOI: 10.1007/s11012-020-01274-3.
25.
Neptune J.A.: Crush Stiffness Coefficients, Restitution Constants, and a Revision of CRASH3 & SMAC. SAE Technical Paper. 1998, 90116, DOI: 10.4271/980029.
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.
27.
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, Lyon, France.
28.
Siddall D.E., Day T.D.: Updating the Vehicle Class Categories. SAE Technical Paper. 1996, 90323, DOI: 10.4271/960897.
29.
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(3), 587–593, DOI: 10.1007/s13177-021-00266-1.
30.
Vangi D., Cialdai C.: Evaluation of Energy Loss in Motorcycle-to-Car Collisions. International Journal of Crashworthiness. 2014, 19(4), 361–370, DOI: 10.1080/13588265.2014.899072.
31.
Vangi D.: Simplified Method for Evaluating Energy Loss in Vehicle Collisions. Accident Analysis & Prevention. 2009, 41(3), 633–641, DOI: 10.1016/j.aap.2009.02.012.
32.
Vangi D.: Vehicle Collision Dynamics: Analysis and Reconstruction. Butterworth-Heinemann, 2020.
33.
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.
34.
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.
35.
Wach W.: Reconstruction of Vehicle Kinematics by Transformations of Raw Measurement Data. XI International Science-Technical Conference Automotive Safety 2018. 2018, 136991, 1–5, DOI:.
37.
Wach W.: Spatial Impulse-Momentum Collision Model in Programs for Simulation of Vehicle Accidents. XII International Science-Technical Conference Automotive Safety 2020. 2020, 9293494, 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 & Prevention. 2002, 34(1), 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.
Udostępnij
ARTYKUŁ POWIĄZANY
Korzystamy z plików cookie (oraz innych podobnych technologii) i przetwarzamy unikalne identyfikatory oraz dane przeglądarki – w celu umożliwienia funkcjonowania naszej strony internetowej. Więcej informacji o tym jak przetwarzamy Twoje dane osobowe znajdziesz w
