CHARACTERIZATION AND EVALUATION OF FRONTAL CRASH PULSES ...0eca4fe331aaaa0387ab-39017777f15f755539d3047328d4a990.r16.… · J. Metzger, L. Kübler, S. Gargallo: Characterization and

1

CHARACTERIZATION AND EVALUATION

OF FRONTAL CRASH PULSES FOR USNCAP 2011

Jürgen Metzger, Lars Kübler and Simon Gargallo

TRW Automotive GmbH

Industriestr. 20, 73553 Alfdorf, Germany

[juergen.metzger;lars.kuebler;simon.gargallo]@trw.com

Keywords: Frontal Crash, Crash Severity, Crash Pulse Criterion, USNCAP 2011.

Abstract. Crash pulse characterization is of great importance in many fields of occupant

restraint system development. It allows assessment of severity of a specific crash pulse with

respect to the dummy. A measure for the quality of pulse criteria is given by the correlation

between dummy values and pulse criteria values. In this paper focus is given on the modified

USNCAP rating starting 2011. At first it is analyzed whether it is generally possible to find

one single pulse criterion that gives a sound assessment regarding all relevant dummy values

of the USNCAP 2011 rating. Then the correlation of existing pulse criteria to those dummy

values is evaluated. Further, an approach is proposed how to derive specific criteria for the

new rating and it is discussed how such criteria could be used in future to support restraint

system development.

1 INTRODUCTION

Occupant restraint systems are essential parts of today’s vehicles to reduce occupant

injuries during collisions. In order to evaluate the restraint performance, computer

simulations, sled tests and vehicle crash tests are conducted for several frontal collision types.

A substantial parameter in this context is the acceleration field, effective on occupants during

a crash test, the so-called crash pulse. Crash pulses are input for sled tests and simulations and

strongly influence the development of restraint systems, since their variations have significant

influence on the overall system responses.

Pulse characterization is of great importance in many fields of occupant restraint system

development. In order to allow a sound comparison of pulse “severity” TRW proposed an

enhanced criterion: OLC++

[1,2]. In addition, the possibility to estimate OLC++

threshold

values, which give an indication for restraint component selection, has been discussed in

[1,2].

The USNCAP rating will change in 2011 with modifications in utilized dummies and

considered dummy values. In particular the 5th

percentile female is used on passenger side,

chest deflection is evaluated instead of chest acceleration and the new rating comprises neck

Airbag 2010 – 10th International Symposium and Exhibition

on Sophisticated Car Occupant Safety Systems,

December 6-8, 2010

J. Metzger, L. Kübler, S. Gargallo: Characterization and Evaluation of Frontal Crash Pulses for USNCAP 2011

2

dummy values. An open question is, whether existing criteria and OLC++

are suitable for the

new rating or if modifications are necessary to cover the mechanisms driving the modified

dummy values for both dummies.

At first the correlation between dummy values themselves is analyzed. Purpose of this

investigation is to answer the question, if it is possible to establish a general crash pulse

criterion which shows good correlation to all relevant dummy values. This provides more

insight in the driving mechanisms and supports further criteria development.

An important question is also, to which extent existing pulse criteria derived for the current

USNCAP rating and other load-cases are capable to assess pulse strength with respect to the

relevant dummy values of the USNCAP 2011 rating. A measure for the quality of pulse

criteria is given by the correlation between dummy values and pulse criteria values. Therefore,

OLC++

and other existing criteria for pulse characterization are compared regarding their

correlation to dummy values in the new USNCAP load-case for a large number of crash

pulses over a range of vehicle types. The investigation is carried out in different vehicle

environments for driver and passenger.

Thereafter, an approach is proposed how to derive specific criteria for the new rating, and it

is discussed how such criteria could be used to support future restraint system development.

2 CORRELATIONS OF DUMMY VALUES AMONG THEMSELVES

In [1, 2] one single criterion has been established in order to evaluate pulse severity for the

current USNCAP rating, i.e. one criterion was feasible to give an indication for the relevant

dummy values: chest acceleration and HIC36 for the 50th

perc. male dummy.

The situation gets more complicated for the new USNCAP rating with 5th

perc. and 50th

perc. dummies and an increasing number of dummy values: HIC15, chest deflection, Nij, neck

tension/compression, and compressive femur forces. The question arises, whether it is still

possible to find one common pulse criterion for all dummy values or not. To answer that

question, in this section correlation of the relevant dummy values among themselves is

investigated for both dummies. A necessary condition for the possibility to derive one single

crash pulse criterion is that each injury parameter shows significant correlation to all other

dummy values under variation of the crash pulse.

For the correlation analysis approximately 400 USNCAP crash pulses from the TRW

database are imposed on MADYMO [9] occupant restraint system models. The crash pulse

database includes pulses of all vehicle classes and manufacturers. Potential influence of

vehicle structure, interior, restraint system, occupant and its position is taken into account by

using different types of cars and both 5th

perc. and 50th

perc. dummy on passenger and driver

side.

In the following, exemplary passenger results for a middle class sedan vehicle with a

standard restraint system, consisting of passenger airbag, constant load limiter and standard

retractor pretensioner, are discussed. Focusing on US market, the airbag is configured in order

to fulfill US legal requirements in the unbelted load cases. To ease the simulation effort, the

airbag vent definition was done once for one of the most severe unbelted crash pulses in TRW

database. Load limiter configuration was set in order to prevent bottoming out or head contact

to the instrument panel in USNCAP load case for all crash pulses in the TRW database.

For the evaluation relevant correlation was assumed for quadratic correlation coefficients

of 0.7 or larger. Strong correlation is indicated by values larger than 0.9. In Figure 2.1

correlation coefficients are given for the relevant dummy values for the 50th

perc. dummy.


3

Because of its relevance to the FMVSS 208, chest acceleration is also observed. Green color

indicates a correlation higher than 0.9, yellow color represents values between 0.7 and 0.9.

Values without significant correlation are not highlighted.

Figure 2.1 (left) shows all dummy values. Obviously not for all values significant

correlation is given to all other values. In Figure 2.1 (right) values without sufficient

correlation to most of the other criteria are removed: NTE, NCE, NCF and neck tension. For

femur forces sufficient correlation to HIC15 and chest deflection are given, however not for

neck values. In principle this result indicates for the 50th

perc. male dummy:

1) Correlation of one criterion to all values in same quality is not possible.

2) For HIC15, chest deflection, neck compression, chest acceleration, and NTF it might

be possible to find a single criterion1

for crash pulse assessment.

3) For femur forces it might also be possible to be covered by such a criterion, if

reduced correlation to femur is accepted.

4) The remaining neck values: NCE, NCF, NTE and neck tension even do not fully

correlate to each other. That could indicate that all neck values could not even be

covered by a separate criterion. This will later be observed in more detail.

In Figure 2.2 correlation coefficients are given analogously for 5th

perc. female dummy.

Figure 2.2 (left) shows all dummy values. Again not for all values significant correlation is

given to all other values. In Figure 2.1 (right) values without sufficient correlation to most of

the other values are removed: NTE, NTF, Ncf, neck tension and femur right. For femur forces

left and NCE correlation to some other values are given, not to all though.

Regarding femur forces, in the USNCAP rating only compressive forces are considered.

This explains low correlation for femur right, where compressive forces are almost not excited

for all pulses for this vehicle. For femur left compressive forces increase with pulse severity,

but over the full pulse range the values are still relatively low.

1 Significant correlation is only a necessary condition, i.e. that does not mean that such a criterion necessarily

exists.

Figure 2.1: Quadratic correlation matrix between dummy values for 50th perc. dummy (yellow: relevant

correlation, green: strong correlation, white correlation not significant)

Left: all criteria, Right: reduced to criteria with overall significant correlation

Che

st

de

flectio

n

NT

F

Fe

mu

r le

ft

Fem

ur

rig

ht

Che

st

acce

lera

tio

n

Ne

ck c

om

pre

ssio

n

HIC

15

HIC 15 0,91 0,92 0,81 0,80 0,92 0,82 1,00

Neck compression 0,88 0,95 0,57 0,58 0,90 1,00 0,82

Chest acceleration 0,93 0,93 0,68 0,69 1,00 0,88 0,92

Femur right 0,77 0,69 0,97 1,00 0,69 0,59 0,80

Femur left 0,77 0,69 1,00 0,97 0,69 0,57 0,82

NTF 0,93 1,00 0,69 0,69 0,91 0,91 0,93

Chest deflection 1,00 0,93 0,76 0,77 0,92 0,87 0,92

Che

st

defle

ctio

n

NT

E

NT

F

NC

E

NC

F

Fe

mu

r le

ft

Fe

mu

r rig

ht

Ch

est a

cce

lera

tio

n

Ne

ck ten

sio

n

Ne

ck c

om

pre

ssio

n

HIC

15

HIC 15 0,91 0,57 0,92 0,56 0,72 0,81 0,80 0,92 0,74 0,82 1,00

Neck compression 0,88 0,55 0,95 0,71 0,72 0,57 0,58 0,90 0,59 1,00 0,82

Neck tension 0,62 0,47 0,69 0,17 0,63 0,85 0,79 0,64 1,00 0,60 0,81

Chest acceleration 0,93 0,66 0,93 0,68 0,72 0,68 0,69 1,00 0,57 0,88 0,92

Femur right 0,77 0,48 0,69 0,37 0,56 0,97 1,00 0,69 0,69 0,59 0,80

Femur left 0,77 0,51 0,69 0,33 0,54 1,00 0,97 0,69 0,68 0,57 0,82

NCF 0,68 0,55 0,76 0,32 1,00 0,55 0,56 0,76 0,64 0,79 0,76

NCE 0,68 0,35 0,69 1,00 0,32 0,34 0,37 0,67 0,20 0,71 0,58

NTF 0,93 0,52 1,00 0,72 0,67 0,69 0,69 0,91 0,58 0,91 0,93

NTE 0,64 1,00 0,58 0,34 0,56 0,50 0,49 0,68 0,46 0,59 0,57

Chest deflection 1,00 0,61 0,93 0,71 0,63 0,76 0,77 0,92 0,54 0,87 0,92


4

To support that conclusion, in Figure 2.3 femur forces over time are given for three pulses

(compressive forces have negative sign). A very soft (green), a moderate (blue) and a very

strong pulse (red) are chosen exemplarily.

In summary it follows for 5th

perc. female dummy:

1) Correlation of one criterion to all values in same quality is most likely not possible.

2) For HIC15, chest deflection, chest acceleration and neck compression it might be

possible to find a single criterion1

for crash pulse assessment.

3) For femur forces and NCE it might also be possible to be covered by such a

criterion, if reduced correlation is accepted.

4) Again between all other neck criteria relevant correlation is not fully given. That

could indicate that also for 5th

perc. dummy the neck could not be fully covered

even by a separate neck crash pulse assessment criterion.

In order to get a better insight into the root cause for neck results for both dummies, in

Figure 2.4 NTF and NCF are shown over time for the 50th

perc. dummy for three pulses

analogous to Figure 2.3.

Figure 2.2: Quadratic correlation matrix between dummy values for 5th perc. dummy (yellow: relevant

correlation, green: strong correlation, white correlation not significant)

Left: all criteria, Right: reduced to criteria with overall significant correlation

Figure 2.3: Femur forces over time:

green: very soft pulse, blue: moderate pulse, red: very strong pulse

Che

st

de

flectio

n

NC

E

Fe

mu

r le

ft

Ch

est a

cce

lera

tion

Neck

com

pre

ssio

n

HIC

15

HIC 15 0,84 0,75 0,75 0,88 0,85 1,00

Neck compression 0,81 0,72 0,73 0,91 1,00 0,89

Chest acceleration 0,72 0,89 0,84 1,00 0,84 0,89

Femur left 0,67 0,80 1,00 0,84 0,69 0,77

NCE 0,63 1,00 0,79 0,88 0,80 0,77

Chest deflection 1,00 0,58 0,64 0,75 0,81 0,84

Che

st

deflectio

n

NTE

NTF

NC

E

NC

F

Fem

ur

left

Fe

mu

r righ

t

Che

st accele

ratio

n

Neck tensio

n

Neck c

om

pre

ssio

n

HIC

15

HIC 15 0,84 0,35 0,51 0,75 0,60 0,75 0,28 0,88 0,54 0,85 1,00

Neck compression 0,81 0,32 0,67 0,72 0,87 0,73 0,19 0,91 0,39 1,00 0,89

Neck tension 0,55 0,77 0,56 0,74 0,05 0,70 0,31 0,75 1,00 0,53 0,77

Chest acceleration 0,72 0,44 0,55 0,89 0,55 0,84 0,26 1,00 0,69 0,84 0,89

Femur right 0,21 0,43 0,15 0,05 0,24 0,07 1,00 0,05 0,29 0,06 0,26

Femur left 0,67 0,46 0,58 0,80 0,47 1,00 0,21 0,84 0,67 0,69 0,77

NCF 0,67 0,25 0,59 0,58 1,00 0,62 0,20 0,84 0,26 0,86 0,81

NCE 0,63 0,56 0,54 1,00 0,48 0,79 0,31 0,88 0,73 0,80 0,77

NTF 0,56 0,51 1,00 0,54 0,50 0,61 0,14 0,61 0,48 0,64 0,51

NTE 0,44 1,00 0,55 0,55 0,02 0,45 0,31 0,42 0,80 0,49 0,56

Chest deflection 1,00 0,28 0,57 0,58 0,67 0,64 0,22 0,75 0,35 0,81 0,84


5

The observed good correlation of NTF to other dummy values can be explained by relatively

high values in comparison to the other Nij and more important continuity, i.e. maxima do

appear in the same time ranges and do not jump between peaks. For other Nij to some extent

discontinuities appear, i.e. discrete changes between dominating peaks are found. For example

in Figure 2.4 (right) with increasing pulse severity NCF are more and more excited at approx.

80ms (no effect for weak, low effect for moderate and strong effect for strong pulse) and the

effect reduces respectively at approx. 140ms. The maximum for NCF changes from peak at

140 ms to peak at 80 ms for the strong pulse.

Analogous effects can be found for the 5th

perc. dummy, compare Figure 2.5. For NCE

excitation maxima appear only at one time frame while for NTF the maximum changes from a

first excitation time range (50-70ms) to a later one (120ms) for the weak pulse.

It is expected that these discontinuities are the main reason for limited correlation. As

potential cause for the discrete effects, a strong sensitivity of Nij to changes in dummy

kinematics and, hence, different interaction with seat belt, seat, etc. and in particular the

airbag is assumed. In other words: small changes in dummy kinematics due to strong pulse

variations lead to bifurcations between excitation of different mechanisms. In order to analyze

these effects in more detail, correlation of the Nij components neck force and neck moment

will be observed in more detail. This will be subject of further investigation.

Figure 2.4: Nij values over time for 50th

perc. male dummy.

Figure 2.5: Nij values over time for 5th

perc. female dummy.


6

The analysis of correlation between dummy values in this section leads to the following

conclusions for 5th

perc. and 50th

perc. dummies:

1) Correlation of one criterion to all values in same quality is most likely not possible.

2) For HIC15, chest deflection and chest acceleration it might be possible to find a

single criterion for crash pulse assessment.

3) For femur forces it might also be possible to be covered by criterion in (2), if

reduced correlation of femur values is accepted.

4) To find one criterion that covers all neck values over a large pulse range is

unlikely, due to strong influence of small changes in dummy kinematics under

pulse variation. Still a generic indication and pulse comparison seems possible also

for neck under the assumptions:

- Bifurcations are not considered in the criterion. It just gives a relative

change assuming continuous effects.

- Highly non-linear effects like bifurcations are expected to be reduced in a

typical pulse range during development of a specific vehicle, i.e. within that

range good correlation to dummy values seems possible for robust airbag

definition over its working range.

5) Separate criteria with high correlation are recommended at least for neck and

femur forces. The other values could potentially be assessed by a combined

criterion as in (2) or also separately. All criteria can then be combined by the

weighted criteria method or utilizing a Pareto approach.

3 CORRELATION OF EXISTING PULSE CRITERIA

After the analysis of necessary conditions for a crash pulse criterion for the new USNCAP

rating, in the following chapter existing crash pulse criteria are evaluated regarding their

correlation to the relevant dummy values over approx. 400 USNCAP crash pulses from the

TRW database. Purpose is to gain more insight in the possibility to utilize existing pulse

criteria with respect to the USNCAP 2011 rating, i.e. to find out what indications are possible

and where are the limitations.

3.1 Existing pulse criteria

In literature many crash pulse criteria are known and thereof several are used in the

automotive industry. A detailed description is given in [1] and [4]. In this paper it will be

focused on the criteria that were already identified in [1]: occupant load criterion (OLC), point

in time when the vehicle velocity is zero Tv=0, sliding mean SM25 and SM35 (window size for

averaging 25ms and 35ms) and the OLC++

criterion proposed by TRW for current USNCAP

rating. Further, the maximum deformation motion of the vehicle smax is investigated.

3.2 Approach

In this study three different simulation models are utilized for the correlation analysis

Vehicle 1: System model of a middle class sedan vehicle, driver side

Vehicle 2: Same vehicle as 1, passenger side

� comparison to vehicle 1 for analysis of influence driver vs. passenger

side

Vehicle 3: System model of a sports car, passenger side

� comparison to vehicle 2 for analysis of influence of vehicle type


7

Before using the three system models for the correlation study, the restraint system

configuration was accomplished as discussed in Chapter 2.

The approach for correlation analysis follows [1]. It is illustrated in Figure 3.1. A number

of N USNCAP crash pulses from the TRW-database are imposed on each of the three system

models. The system models are set up in MADYMO [9] including 5th

perc. and 50th

perc.

Hybrid III dummy. In parallel all crash pulse criteria are calculated for each crash pulse.

Finally, dummy values generated by a system model are displayed versus corresponding crash

pulse criteria.

Figure 3.2 shows a generic example of the resulting correlation diagrams. The dots

represent N pairs of dummy values and crash pulse criterion values. Further, regression curves

are calculated for each combination of crash pulse criterion and dummy value. In a first step

three regression curves are build by polynomial 1st, 2

nd and 3

rd order least square regression.

As an assessment of correlation quality, the root mean square according to the regression

curve and N value pairs is calculated by

Figure 3.1: Approach of correlation analysis

Figure 3.2: Example of correlation diagram with regression curve


8

,))((

1

2

N

XfYRMS

N

k jkijik

ij

∑=

−

=

where

function. Regression :f

k, pulsefor jcriterion pulseCrash :

k, pulsefor iparameter Injury :

ij

jk

ik

X

Y

(3.1)

The lower the RMS value, the higher the quality of correlation. Two conditions have to be

satisfied for an ideal crash pulse criterion

• RMSij = 0,

• fij monotonically increasing or decreasing.

Out of the three calculated regression curves of different order consequently the regression

curve fij which is monotonically increasing or decreasing in the overall pulse range and has the

lowest RMSij is used for further analysis.

3.3 Correlation Analysis

In order to compare correlation to all criteria, in the following RMS values of the

correlation between each specific dummy value and all evaluated pulse criteria are visualized

for the observed vehicles. Figure 3.3 shows the correlation of HIC15 versus pulse criteria for

the 50th

perc. male dummy on the left and for the 5th

perc. female dummy on the right side.

The lower the RMS value the better the correlation.

For the 50th

perc. dummy OLC++

shows the best correlation for all three vehicles. For the

5th perc. dummy over all three vehicles Tv=0, OLC++

and smax show the best correlation to

HIC15.

Regarding chest acceleration, Figure 3.4, OLC++

shows the best correlation for 50th

perc.

dummy, even though SM35/25 have a slightly better correlation in the case of vehicle 3. Here

the criterion smax gives worst results for both dummies in almost all vehicles. Again, there is

no obvious best criterion for the 5th

perc. dummy regarding chest acceleration. OLC, SM35 and

OLC++

are the pulse criteria with the lowest RMS values.

OL

C+

+

OL

C+

+

OL

C+

+

OL

C

OL

C

OL

C

Tv

=0

Tv

=0

Tv

=0

SM

35

SM

35

SM

35

SM

25

SM

25

SM

25

s max

s max

s max

0

20

40

60

80

100

120

140

Vehicle 1 Vehicle 2 Vehicle 3

HIC15 - 50th perc. dummy

OL

C+

+

OL

C+

+

OL

C+

+

OL

C

OL

C

OL

C

Tv

=0

Tv

=0

Tv

=0

SM

35

SM

35

SM

35

SM

25

SM

25

SM

25

s max

s max

s max

0

20

40

60

80

100

120

140

160

180


HIC15 - 5th perc. dummy

Figure 3.3: RMS of correlation HIC15 vs. pulse criteria for three vehicles,

50th

perc. dummy (left) / 5th

perc. dummy (right).


9

Analogous trends follow for chest deflection, femur forces, neck compression and tension.

In Figure 3.5 exemplary chest deflection and femur forces are shown.

In Figure 3.6 exemplary the best correlation to one of the Nij values is shown for each

dummy. RMS values of correlations between NTF and pulse criteria are visualized for the 50th

perc. male dummy and NCE to pulse criteria for the 5th

perc. female dummy. Regarding the

correlation to NTF, OLC++

again turns out to be the best pulse criterion to assess a dummy

value for 50th

perc. dummy. SM25,35 show benefits for 5th

perc. female.

OL

C+

+

OL

C+

+

OL

C+

+

OL

C

OL

C

OL

C

Tv

=0

Tv

=0

Tv

=0

SM

35

SM

35

SM

35

SM

25

SM

25

SM

25

s max

s max

s max

0

1

2

3

4

5

6


Chest acceleration - 50th perc. dummy

OL

C+

+

OL

C+

+

OL

C+

+

OL

C

OL

C

OL

C

Tv

=0

Tv

=0

Tv

=0

SM

35

SM

35

SM

35

SM

25

SM

25

SM

25

s max

s max

s max

0

1

2

3

4

5

6


Chest acceleration - 5th perc. dummy

Figure 3.4: RMS of correlation chest acceleration [g] vs. pulse criteria for three vehicles,

50th



OL

C+

+

OL

C+

+

OL

C+

+

OL

C

OL

C

OL

C

Tv

=0

Tv

=0

Tv

=0

SM

35

SM

35

SM

35

SM

25

SM

25

SM

25

s max

s max

s max

0,0

0,5

1,0

1,5

2,0

2,5


Chest deflection - 50th perc. dummy

OL

C+

+

OL

C+

+

OL

C+

+

OL

C

OL

C

OL

C

Tv

=0

Tv

=0

Tv

=0

SM

35

SM

35

SM

35

SM

25

SM

25

SM

25

s max

s max

s max

0,0

0,2

0,4

0,6

0,8

1,0

1,2

1,4


Chest deflection - 5th perc. dummy

OL

C+

+

OL

C+

+

OL

C+

+

OL

C

OL

C

OL

C

Tv

=0

Tv

=0

Tv

=0

SM

35

SM

35

SM

35

SM

25

SM

25

SM

25

s max

s max

s max

0,0

0,2

0,4

0,6

0,8

1,0

1,2

1,4

1,6


Femur forces - 50th perc. dummy

OL

C+

+

OL

C+

+

OL

C+

+

OL

C

OL

C

OL

C

Tv

=0

Tv

=0

Tv

=0

SM

35

SM

35

SM

35

SM

25

SM

25

SM

25

s max

s max

s max

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7


Femur forces - 5th perc. dummy

Figure 3.5: RMS of correlation chest deflection [mm] and femur forces [kN] vs. pulse criteria for three

vehicles, 50th




10

In summary it follows for 50th

perc. male dummy: Among all evaluated pulse criteria and

vehicles the OLC++

criterion gives lowest RMS values.

For 5th

perc. female dummy it is not possible to identify one criterion which is the best over

all vehicles. OLC++

, OLC, Tv=0 and SM35 show comparable results.

In the next step, exemplary the quality of OLC++

correlation is visualized by several

correlation diagrams, as defined in section 3.1, see Figure 3.2. Each of the following figures

comprises four correlation diagrams. In the top row the best correlation diagram for the 50th

perc. dummy over all three vehicles is placed on the left side. The worst correlation diagram is

placed on the right. Thus the range of OLC++ quality over the three exemplary vehicles is

visualized in addition to the RMS values. The correlation diagrams for the 5th

perc. dummy

are placed in the same order, but in the lower row.

In Figure 3.7 results for HIC15 over OLC++

are illustrated.

OL

C+

+

OL

C+

+

OL

C+

+

OL

C

OL

C

OL

C

Tv

=0

Tv

=0

Tv

=0

SM

35

SM

35

SM

35

SM

25

SM

25

SM

25

s max

s max

s max

0,000

0,005

0,010

0,015

0,020

0,025

0,030

0,035

0,040


NTF - 50th perc. dummy

OL

C+

+

OL

C+

+

OL

C+

+

OL

C

OL

C

OL

C

Tv

=0

Tv

=0

Tv

=0

SM

35

SM

35

SM

35

SM

25

SM

25

SM

25

s max

s max

s max

0

0,01

0,02

0,03

0,04

0,05

0,06

0,07

0,08

0,09


NCE - 5th perc. dummy

Figure 3.6: RMS of correlation best Nij vs. pulse criteria for three vehicles,

50th



Figure 3.7: Best (left) and worst (right) correlation of HIC15 vs. OLC++

over all three vehicles for 50th

perc.

dummy (top) / 5th

perc. dummy (bottom).


11

The quality of the correlation between HIC15 and OLC++

for the 50th

perc. dummy is very

good with some limitations for the driver model. Correlation for the 5th

perc. dummy is worse

than for 50th

. Even if reasonable correlation is found for vehicles 2 and 3, results for vehicle 3

indicate the need of an improved criterion for 5th

perc. dummy.

Figure 3.8 shows the correlation results for chest acceleration.

OLC++

shows sound correlation to chest acceleration for both 50th

perc. and 5th

perc.

dummy. However, improvement for 5th

perc. would be beneficial, comparing results for driver

model in the lower right.

Even though OLC++

has not been developed with respect to chest deflection, a good

correlation is found in Figure 3.9 for the 50th

perc. dummy. Also for 5th

perc. dummy

correlation looks promising.

Analogous results are found for femur forces in Figure 3.10. For the 5th

perc. dummy

correlation is better than the RMS value would indicate, since the approx. bi-linear correlation

characteristic is not covered by the applied regression schemes. Over the full pulse range out

of the TRW database two effects occur for the 5th

perc. dummy, compare diagram in lower

right. First almost no contact femur to IP occurs. Starting from a specific pulse severity,

contact occurs with increasing amplitudes.

Figure 3.8: Best (left) and worst (right) correlation of chest acceleration vs. OLC++

over all three vehicles for

50th

perc. dummy (top) / 5th



12

As discussed in section 2 regarding Nij good correlation is expected if discontinuities do

not appear. This is for example the case in a wide range of NTF values for 50th

perc. male,

compare Figure 3.11. For 5th

perc. dummy it can be seen that correlation gets worse with

increasing discontinuities in the lower right.

Figure 3.9: Best (left) and worst (right) correlation of chest deflection vs. OLC++


50th



Figure 3.10: Best (left) and worst (right) correlation of femur forces vs. OLC++


50th




13

Analogous behavior can be observed for NCE in Figure 3.12.

Figure 3.11: Best (left) and worst (right) correlation of NTF vs. OLC++


perc.

dummy (top) / 5th


Figure 3.12: Best (left) and worst (right) correlation of NCE vs. OLC++


perc.

dummy (top) / 5th



14

In summary, even if OLC++

shows good correlation to many dummy values, it can be said

that no criterion gives sufficient correlation to all of the values relevant for the USNCAP 2011

rating, especially with respect to 5th

perc. female.

4 DEVELOPMENT OF NEW CRITERIA

In chapter 2 it has been identified that separate criteria for each dummy value might be

beneficial to cover all effects. At least separate criteria for femur and neck and one combined

criterion for HIC15, chest deflection and chest acceleration came out to be favorable. The

analysis of correlation for existing criteria in chapter 3 supports this conclusion.

Consequently TRW is currently working on separate criteria for specific dummy values.

Two approaches are investigated in parallel. Based on signal attributes, e.g. TV=0

characteristic values of pulses are extracted. Additionally, the development of simplified

mechanical models representing dynamics of dummy or dummy regions excited by pulses in a

generic environment is continued.

Once separate criteria for particular dummy values are established, it is investigated

whether a combination by a weighted criteria approach gives sufficient overall correlation. If

this compromise is not acceptable, a Pareto approach will be applied in order to cover the

multi-criteria assessment task. Here all information remains available and a combination per

user preferences or focused body region is possible.

5 CONCLUSIONS AND OUTLOOK

With the changing USNCAP rating, complexity of crash pulse assessment increases due to

the higher number of applied dummy values, further body regions and additional dummies.

The target of this investigation was to assess whether existing pulse criteria like OLC++

are

also applicable for the new rating and to understand, if modifications are recommended to

cover the mechanisms driving the additional dummy values.

At first correlation between dummy values were analyzed to check whether it is generally

possible to establish a generic crash pulse criterion, or if separate crash pulse criteria are

required for some dummy values, or even if some mechanisms can not be covered by pulse

assessment at all. Different vehicle types were taken into account and it was observed if

differences between driver and passenger appear.

It was found that HIC15, chest deflection and chest acceleration can potentially be assessed

by one single crash pulse criterion. If some limitation in femur correlation is accepted, also

femur could be added. Also a generic indication of neck values could potentially be added to

such a criterion. However, that might require significant compromises.

This is also supported by looking at the correlation of dummy values with existing pulse

criteria. It was investigated to what extent pulse criteria derived for the current USNCAP

rating and other load-cases are capable to assess pulse severity with respect to the relevant

dummy values of the USNCAP 2011 rating.

Both the results from the dummy value correlation study and the investigation of

correlation of existing pulse criteria indicate that separate criteria for specific body regions,

covering the underlying physical effects for each dummy value, could be beneficial. Those

criteria could be combined either by using a weighted criteria approach with some limitations

in specific body regions or by applying a Pareto type approach that gives the user more

information but on the other hand increases complexity of standardization.


15

The investigation is on-going. Criteria are established via pulse signal attributes or

mechanical models approximating underlying physical effects for each injury parameter.

Those criteria can then be combined by weighted criteria or a Pareto approach. In a further

step threshold values could be defined for the new pulse criterion, that give an indication for a

pre-selection of restraint components depending on the specific vehicle pulse. The feasibility

of the approach and specific criteria combination for other load-cases like EuroNCAP would

then be a possible next step.

REFERENCES

[1] L. Kübler, S. Gargallo, K. Elsäßer: Characterization and Evaluation of Frontal Crash Pulses with

Respect to Occupant Safety, Proceedings Airbag 2008 – 9th International Symposium and

Exhibition on Sophisticated Car Occupant Safety Systems, 2008.

[2] L. Kübler, S. Gargallo, K. Elsäßer: Bewertungskriterien zur Auslegung von

Insassenschutzsystemen, ATZ Automobiltechnische Zeitschrift, 111, 06/2009, S. 426-433, 2009.

[3] TNO Automotive Safety Solutions: MADYMO Theory Manual, Release 7.2, 2010.

[4] M. Huang: Vehicle Crash Mechanics. Florida: CRC Press LLC, 2002.

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