numerical crashworthiness simulation for vehicleand occupant safety protection

1. A d o p t e d M e t h o d o l o g y
In order to enable progress toward improved predictive
modelling of defor-mation and failure of lightweight materials
subjected to impact loading, thefollowing integrated
experimental/numerical methodology has
beenadopted by researchers in industry and academia interested
in automotivecrashworthiness.1.New experimental programs for
selected advanced materials must be devised in order to provide
required information on the depen-dency of material behavior to
process history, temperature, and rateof loading. Such programs must
encompass intelligent-selective test-ing rather than mass testing, and
must provide manageable proce-dures and necessary data that enable
determination of requiredconstitutive parameters. New methodologies
must be established tocharacterize the deformation and failure of
jointing systems.2.New constitutive models that can include
process history, tempera-ture effects, and can adequately describe the
evolution of materialstate at required scale during loading, must be
developed and imple-mented into existing software. Appropriate criteria
for crack initia-t i o n a n d c r a c k p r o p a g a t i o n
( t r a n s i t i o n f r o m c o n t i n u u m t o discontinuum) must be
incorporated into constitutive models andnovel techniques, just as
automatic remeshing around evolving dis-continuities must be used for
improved accuracy. Extensive valida-tion against experimental
data should be carried out duringdevelopment and
implementation in order to ensure that mathemat-ical models
adequately represent the observed physical behavior.3.Inverse
modelling techniques must be employed to fully
quantifythe response of different materials to impact loading

2. with respectto newly developed numerical algorithms, as not all
constitutiveparameters will be directly measurable in
experiments he development framework representing the basis
of such design meth-odology can be described as an
iterative process, part of which can be greatlyautomated
by employing inverse modelling techniques based upon
opti-mization and stochastic analysis. An example of such
development frame-work for research into behavior of
automotive materials subjected to impactloading is
illustrated in Figure 6.2. In this approach, the
investigation startswith small-scale laboratory
experiments in order to provide both qualitativeand
quantitative information on the observed behavior. The next step
com-prises the theoretical (mathematical) abstraction of
the selected phenomenaand is followed by development
and implementation of numerical algo-rithms into chosen
computational modelling framework. Direct
comparison between the results of experimental measurements
and numerical simula-tions can be used to determine non-
measurable modelling parameters, andcan also provide
information on the accuracy of newly developed
numericalmodelling tools.Moreover, the inverse modelling
for identification of nonmeasurable mod-elling parameters can
establish the adequacy of newly developed algorithmsfor
solving given problems. This follows from the ability of
optimizationand stochastic methods to separate regions within
analyzed parametricspaces if the employed models cannot
adequately represent the observed behavior.

3. FIGURE

4. Tensile split Hopkinson-bar apparatus

7. An example of large-scale crash simulation