Optimizing crash box design to meet injury criteria: a protocol for accurate simulation and material selection
Author
Other authors
Publication date
2024ISSN
1615-1488
Abstract
The design of a deformation element or crash box that meets a given injury criterion based on deceleration requires careful consideration of physical properties and space requirements. Variations in material yield stress or geometry can result in statistical variations in the injury criterion output. Optimizing the crash box to fulfil two different injury criteria and two different energy levels may require more space than initially specified. In this study, we propose a protocol where the crash box is collapsed, and force–displacement is fitted to an equation. This fit is carried out with just two simulations and compared to 30 possible scenarios, obtaining a maximum error of 38.9%. With this initial fit, the appropriate thickness and yield stress can be chosen to perform crashes with two energy levels and monitor four injury values. With the ideal yield stress and sheet metal thickness, we introduce real statistical distributions using Monte Carlo design to perform 200 simulations and obtain 400 injury values for each design proposal. This technique ensures that the design will meet injury requirements for any possible combination of thickness and yield stress accepted by quality inspection. If only one simulation is performed, all designs meet the requirements, but only the last proposed design decreased the average injury to 9.2 g with a standard deviation of 2.68 g and a maximum value of 14.4 g, which is less than the required 15 g. This technique minimizes the risk of finding combinations of yield stress and thickness that produce an undesirable injury criterion.
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
531/534 - Mechanics
621 - Mechanical engineering in general. Nuclear technology. Electrical engineering. Machinery
Keywords
Crash
Monte Carlo
FEM
HIC
a3ms
Montecarlo, Mètode de
Pages
p.17
Publisher
Springer
Is part of
Structural and Multidisciplinary Optimization 2024 67,156
Grant agreement number
info:eu-repo/grantAgreement/ACM/Ayudas a Proyectos de Investigación/ACM2023_03
This item appears in the following Collection(s)
Rights
© L'autor/a
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/