scholarly journals On the assessment of the macro-element methodology for full vehicle crashworthiness analysis

2017 ◽  
Vol 23 (3) ◽  
pp. 336-353 ◽  
Author(s):  
Georgia Georgiou ◽  
Tayeb Zeguer
Designs ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 43 ◽  
Author(s):  
Bernard B. Munyazikwiye ◽  
Dmitry Vysochinskiy ◽  
Mikhail Khadyko ◽  
Kjell G. Robbersmyr

Estimating the vehicle crashworthiness experimentally is expensive and time-consuming. For these reasons, different modelling approaches are utilised to predict the vehicle behaviour and reduce the need for full-scale crash testing. The earlier numerical methods used for vehicle crashworthiness analysis were based on the use of lumped parameters models (LPM), a combination of masses and nonlinear springs interconnected in various configurations. Nowadays, the explicit nonlinear finite element analysis (FEA) is probably the most widely recognised modelling technique. Although informative, finite element models (FEM) of vehicle crash are expensive both in terms of man-hours put into assembling the model and related computational costs. A simpler analytical tool for preliminary analysis of vehicle crashworthiness could greatly assist the modelling and save time. In this paper, the authors investigate whether a simple piecewise LPM can serve as such a tool. The model is first calibrated at an impact velocity of 56 km/h. After the calibration, the LPM is applied to a range of velocities (40, 48, 64 and 72 km/h) and the crashworthiness parameters such as the acceleration severity index (ASI) and the maximum dynamic crush are calculated. The predictions for crashworthiness parameters from the LPM are then compared with the same predictions from the FEA.


1992 ◽  
Author(s):  
Michael Y. Sheh ◽  
John D. Reid ◽  
Stephen M. Lesh ◽  
Wichai Cheva

Author(s):  
Karim Hamza ◽  
Kazuhiro Saitou

This paper presents a new method for designing vehicle structures for crashworthiness using surrogate models and a genetic algorithm. Inspired by the classifier ensemble approaches in pattern recognition, the method estimates the crash performance of a candidate design based on an ensemble of surrogate models constructed from the different sets of samples of finite element analyses. Multiple sub-populations of candidate designs are evolved, in a co-evolutionary fashion, to minimize the different aggregates of the outputs of the surrogate models in the ensemble, as well as the raw output of each surrogate. With the same sample size of finite element analyses, it is expected the method can provide wider ranges potentially high-performance designs than the conventional methods that employ a single surrogate model, by effectively compensating the errors associated with individual surrogate models. Two case studies on simplified and full vehicle models subject to full-overlap frontal crash conditions are presented for demonstration.


2016 ◽  
Vol 105 ◽  
pp. 121-134 ◽  
Author(s):  
Shengyin Wu ◽  
Guangyao Li ◽  
Guangyong Sun ◽  
Xin Wu ◽  
Qing Li

Author(s):  
Alberto Parra ◽  
Dionisio Cagigas ◽  
Asier Zubizarreta ◽  
Antonio Joaquin Rodriguez ◽  
Pablo Prieto

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