Citation

Kubiak P, Siczek K, Dabrowski A, Szosland A. Int. J. Crashworthiness 2016; 21(6): 532-541.

Copyright

(Copyright © 2016, Informa - Taylor and Francis Group)

DOI

10.1080/13588265.2016.1194566

PMID

unavailable

Abstract

Existing methods of determining vehicle velocity based on measurements of body deformation have been based on linear models. This means that the linear relationship between the equivalent energy speed (EES), which is a measure of the kinetic energy lost during the collision of cars, and the value of the ratio Cs, representing the average value of the body deformation, has been sought. The aim of this publication is to present a nonlinear and innovative quick method, or its simpler form, using linearisation in the seven consecutive ranges, for determining EES on the basis of the averaged deformation ratio Cs. The most critical factor for this analysis has been the coefficient bk, representing the slope of the linear relationship. Its fixed value is appropriate only for the rare case of a linear response of a system of cars interacting during a collision with the internal forces generated in the collision, and carrying out the work of deformation. In a more general case, the assumption of a fixed value of the coefficient bk, based on the tables for each class of vehicles, can lead to an EES estimation error greater than 30%. This results in a significant difference between the calculated speed and actual speed. The use of a nonlinear model can be a better solution but, on the other hand, it can result in an increase in computation time due to a very small iteration time step and problems of solution convergence due to computation instability. The innovative compromise for such a situation has been the use of linear estimation with different slopes for each of the consecutive seven ranges, which together contain values of the deformation ratio Cs from 0.24 to 0.7. With such an assumption, and using the polynomial regression equations to calculate the bk coefficient, the authors have obtained an improvement in the accuracy of EES estimation with an error of 2%. Additionally, this enables the use of a quicker computation algorithm.

Language: en