ROBUST PROJECT CIDAUT

WP5 - Computational Mechanics transducers on FE vehicle models for crash with PAM-CRASH MAIN REPORT Volume 1 of 1

November 2005 Doc. No.: ROBUST-5-011b – Rev. 0

Main Report

Report title: WP5 - Computational Mechanics. Acceleration transducers on FE vehicle models for crash simulations with PAM-CRASH TM

Client: Doc. no.: CIDAUT ROBUST-5-011b

Project no.: Reporter(s): 202.813023 / ROBUST-GRD1-2002-70021 J García

Abstract: The Robust Project aims to improve scientific and technical knowledge on the main issues still open in the new European standards on the road restraint system EN1317.

The knowledge acquired will form the basis of updated standards for EN 1317 and lead to more advanced road restraint systems and improve road-users safety.

This report is part of the deliverables from Work Package 5 – Computational Mechanics.

This report documents simulations performed by CIDAUT to analyse the use of the entities available in the code PAM-CRASH for the acquisition of acceleration data. The simulations were performed on the B5 temporary vertical concrete safety barrier.

Keywords: Restricted Free distribution Road restraint system, Finite Element simulation, accelerometer Internal Ref. allowed

Rev. no. Date Prepared by Checked by Approved by Reason for revision 0 22/5/6 J Garcia Public release

286-2-1-no-en 286-2-1-no-en

ROBUST project Page i CIDAUT ROBUST-5-011b – Rev. 0 WP5 - Computational Mechanics Acceleration transducers on FE vehicle MAIN REPORT models for crash simulations with PAM-CRASH

CONTENTS 1 INTRODUCTION...... 1 2 SUMMARY AND CONCLUSIONS ...... 2 3 ACCELERATION MEASUREMENT IN PAM-CRASH ...... 3 4 SAMPLING FREQUENCY ...... 5 5 ACCELEROMETER LOCATION...... 23 6 REFERENCES...... 26

ROBUST project Page 1 CIDAUT ROBUST-5-011b – Rev. 0 WP5 - Computational Mechanics Acceleration transducers on FE vehicle MAIN REPORT models for crash simulations with PAM-CRASH

1 INTRODUCTION

The Robust Project aims to improve scientific and technical knowledge on the main issues still open in the new European standards on road restraint system EN 1317 [1-2]. The knowledge acquired will form the basis of updated standards for EN1317 and lead to more advanced road restraint systems and improve road-users safety. This report is part of the deliverables from Work Package 5 - Computational Mechanics..

The objectives of the WP5 project are: • Evaluation and enhancement of the use of computational mechanics to complement experimental activity • Criteria and procedures for the validation of computational mechanics results through comparison with test results • Reconstruction of real life accidents • Identification of activity needed for further enhancement of the use of computational mechanics

This report documents the influence of the sampling rate over the acquisition of acceleration data using Finite Element vehicle models for crash simulations. The influence of the location of the accelerometers has been investigated, as well. The code used to perform the simulations is PAM-CRASH. The simulations were performed on the B5 temporary vertical concrete safety barrier. This document should be read in conjunction with ROBUST-05-007, “Acceleration transducers on Finite Element vehicle models for crash simulations with Ls-Dyna”.

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2 SUMMARY AND CONCLUSIONS

Finite element simulation can be used to model impacts between vehicles and road restraint systems. By the analysis of simulation results, it is possible to assess the behaviour of modelled road restraint systems under the same criteria as full-scale crash tests, which are defined by the EN1317 Standard [1], [2]. An important part of those criteria are the severity indices, based on the measurement of in the vehicle.

In actual full-scale crash tests, accelerometer sensors are placed on the vehicle, generally close to the centre of gravity. These sensors provide a measurement of accelerations, which are obtained in three components, in a reference frame that is moving and rotating with the vehicle. Then, the accelerations are stored and processed in order to obtain the severity indices.

In Computational Mechanics, there are also entities that allow obtaining results such as accelerations and from simulated crash tests. Acceleration data obtained from such “Computational Mechanics acceleration transducers”, together with the rotation velocities, are the input for the procedures to compute the severity indices ASI, THIV and PHD. The way in which they are registered and stored can have an effect on the values obtained for the severity indices. For this reason, the output parameters that can be set in the simulation process have been studied and their influence on the results have been analysed.

It has been found that simulation output and the post-processing of the results should fulfil certain requirements in terms of data recording and data filtering in order to obtain a reliable calculation of the severity indices using the same procedures as in full-scale crash tests.

Computational mechanics have also been used to study the effect of differences in the position of the acceleration transducers during an impact. It is possible to obtain and compare the acceleration data that would theoretically be measured by accelerometers placed on different locations on the vehicle. A comparative analysis has been performed and the resulting variations have been determined.

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3 ACCELERATION MEASUREMENT IN PAM-CRASH

In impact simulations, to collect the acceleration and history of the vehicle in order to assess occupant risk factors, an entity analogous to an accelerometer sensor must be included in the vehicle model. The PAM-CRASH TM code provides the possibility to obtain the accelerations of nodal points in the model and store them in an ASCII simulation output file.

In order to obtain the accelerations in a point of the vehicle, in PAM-CRASH there is not a specific accelerometer entity, but the user can select any node in the mesh, or also a free node that is not connected to the mesh. Using the entity “Nodal time history”, which is defined by the card “THLNO”, the nodal accelerations, velocities, displacements, and coordinates are obtained in the output file. To obtain these measurements in a local coordinate system moving with the sensor, as a physical accelerometer does, the entity “Local coordinates output” associated to the card “THLOC” allows the selection of the nodes, and of the local moving axis frame in which the accelerations are expressed.

When modelling the vehicle, the approach used in the PAM-CRASH TM simulations performed by CIDAUT for the TB11 test considered a vehicle composed of two main sections: the deformable parts which interact with the road restraint system, and an inner rigid core which does not deform. This assumption is consistent with the theoretical basis of EN 1317. The measuring node is fixed to the rigid core. It is located close to the position of the centre of gravity, actually not as part of the model mesh but as a free node. Its position is in agreement with the criteria of EN 1317 (Figure 1).

Figure 1. Vehicle model and location of the acceleration sensor.

The acceleration data obtained from the above mentioned Computational Mechanics acceleration transducers, together with the rotation velocities, are the input for the procedures to compute the severity indices ASI, THIV and PHD. For this reason, the output parameters that can be set in the simulation process have been studied and their influence on the results have been analysed.

Once that the nodal point(s) required for acceleration measurement have been selected by means of the cards “THLNO” and “THLOC”, then the main parameters to be defined are the output frequency and the prefiltering option. The output frequency is set in the control cards, by means of the keyword “TIOD”, “ Time intervals between time history plot data outputs” . It fixes an equivalent to the sampling frequency in experimental testing acquisition. The prefiltering option is activated introducing the keyword “PREFILTER” in the control section of the header of the model

ROBUST project Page 4 CIDAUT ROBUST-5-011b – Rev. 0 WP5 - Computational Mechanics Acceleration transducers on FE vehicle MAIN REPORT models for crash simulations with PAM-CRASH file. The prefiltering option modifies the output of the accelerations on the time history files in order to avoid aliasing of the signal.

The choice of the output frequency is important to avoid aliasing phenomena. If the sampling frequency is not sufficient to capture the high-frequency oscillation phenomena taking place in the crash, then non-realistic acceleration signals may be obtained and wrong data analysis may result. From this point of view, for a safe choice of the output frequency, the sampling period should be lowered to a value of the same order of magnitude of the time step.

The general indication from PAM-CRASH manual [3] is that the time interval should be selected such as to obtain several hundred to a few thousand data points, depending on the filters applied during post-processing. This suggests that values of the sampling frequency similar to those used in experimental crash tests (e.g. 10, 20 KHz) may be a convenient choice.

Results from the simulations and comparative analyses are reported in the following sections.

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4 SAMPLING FREQUENCY

The frequencies considered were 10 KHz, 100 KHz, and 400 KHz (corresponding to an approximate value of the timestep between 2 and 3 microseconds).

Each of the frequencies was analysed with and without the “Prefiltering” feature. The pre-filtering option modifies the output of the accelerations on the time history files in order to avoid aliasing of the signal. The raw acceleration signal is sent through a linear phase low pass FIR (Finite Impulse Response) pre-filter. It performs the following algorithms [3]: − Generation of an equidistant sample. Using a cubic spline, an equidistant "fine" sample is computed by interpolation from the raw data. The frequency of this sample equals 100 the final output frequency. − First filter sweep with data reduction. The FIR filter is applied to the equidistant fine sample. Of the first sweep filtered signal, only one point out of every ten is kept, yielding a reduced signal with ratio 1:10. − Second filter sweep with data reduction. The FIR filter is applied again to the reduced signal. Again, only one point out of ten is kept, leading to the final pre-filtered signal, reduced by ratio 1:100. This signal is written to the output file and the output time is corrected to ensure equidistance.

The algorithms imply that of a final output sampling frequency of 10 KHz, the period of the samples that will be internally computed is of 1 microsecond, thus of the same order of magnitude of the time step.

Regarding the FIR filter characteristics, PAM-CRASH reference [3] indicates that the data reduction low pass filter has the pass band 0-1.5 KHz, pass band ripple 0.02 dB, stop band > 8.5 KHz and stop band attenuation > 80 dB. These specifications are considered sufficient for this low pass filter to be utilized as a pre-processing filter for the subsequent implementation of the SAE filters.

The effect of prefiltering and the effect of the sampling frequency are studied and discussed in the following pages. For the PAM-CRASH simulations, the first comparison is made between data obtained with and without prefiltering.

On the output acceleration curves obtained from the software, the CFC 180 filter that is implemented in the post-processing tool is applied, so that the resulting curves are treated in the same way as experimental ones. This way they would be prepared of severity indices calculation. The curves obtained with 10kHz, 100 KHz and 400KHz sampling frequencies are shown in Figure 2. Among them, the ones showing a better agreement are represented in Figure 3.

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Figure 2. Longitudinal acceleration time history sampled at different frequencies, with and without prefiltering.

Figure 3. Longitudinal acceleration time history sampled at different frequencies, with and without prefiltering. Selection of the curves showing best agreement in magnitude.

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Analysing the different curves for different sampling frequencies and prefiltering usage, it can be observed that the curves showing good agreement in magnitude and time scale are the ones for 10KHz and 100 KHz (when prefiltered) and 400 (when not prefiltered). For the rest of curves various differences appear: − The prefiltered curve sampled at 400 KHz shows a fair agreement in magnitude but some compression in the abscissa axis. This compression effect in the time scale has been identified in PAM-CRASH: “The time interval between Time Histories output must be larger than the time step, otherwise the time scale may be compressed.” [3] − The non-prefiltered curves sampled at 10KHz and 100KHz show great differences in shape and magnitude.

This means that, -at least with the CFC 180 filter implemented in PAM-VIEW post-processor that has been used for this comparison-: − When the signal output is not prefiltered, to avoid errors due to aliasing the sampling frequency should of the same order of magnitude as the time step. − When the signal output is prefiltered, to avoid errors due to aliasing it is not necessary to use such a high sampling frequency. In fact, 10KHz and 100KHz have shown good agreement, and if the sampling frequency becomes higher than the time step, then errors appear. Therefore, a sampling frequency of 10 KHz is very convenient.

As a conclusion, a good choice for the acquisition of vehicle accelerations of the simulation is to use a 10 KHz sampling frequency with the Prefiltering option. It has to be noted that this simulation features rigid bodies (with quite large sizes) impacting with very stiff contact interaction, which is a source of high frequency vibrations. On the other hand, the large mass of the rigid body in which the acceleration measurement is performed helps reduce the local vibration effects that may be caused by very high frequency modes.

For the signals in the other directions (transversal and vertical accelerations), the same tendencies have been observed

A second comparison was performed between the different signals obtained for nodal velocities and displacements against their corresponding curves obtained by integrating the nodal accelerations. The comparison was performed for each output frequency, with and without the prefiltering option. In addition, the comparison was made both prior to and after the CFC180 filtering of the acceleration output. The three acceleration components (longitudinal, lateral, vertical) were considered. It has to be noted that this analysis was performed on accelerations referred to a fixed reference system, for better accuracy.

Thus, velocities and are compared for: − 10 KHz, without prefiltering. Figure 4 to Figure 9 − 100 KHz, without prefiltering. Figure 10 to Figure 15 − 400 KHz, without prefiltering. Figure 16 to Figure 21 − 10 KHz, with prefiltering. Figure 22 to Figure 27 − 100 KHz, with prefiltering. Figure 28 to Figure 33 − 400 KHz, with prefilteringFigure 34 to Figure 39.

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Figure 4. Longitudinal -time history sampled at 10-kHz, without prefilter, and corresponding integrated history. Without (left) and with (right) CFC 180 filtering

Figure 5. Lateral velocity-time history sampled at 10-kHz, without prefilter, and corresponding integrated history. Without (left) and with (right) CFC 180 filtering

Figure 6. Vertical velocity-time history sampled at 10-kHz, without prefilter, and corresponding integrated history. Without (left) and with (right) CFC 180 filtering.

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Figure 7. Longitudinal displacement history sampled at 10-kHz, without prefilter, and corresponding integrated history. Without (left) and with (right) CFC 180 filtering

Figure 8. Lateral displacement history sampled at 10-kHz, without prefilter, and corresponding integrated history. Without (left) and with (right) CFC 180 filtering

Figure 9. Vertical displacement history sampled at 10-kHz, without prefilter, and corresponding integrated history. Without (left) and with (right) CFC 180 filtering

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Figure 10. Longitudinal velocity-time history sampled at 100-kHz, without prefilter, and corresponding integrated history. Without (left) and with (right) CFC 180 filtering

Figure 11. Lateral velocity-time history sampled at 100-kHz, without prefilter, and corresponding integrated history, with and without CFC 180 filtering

Figure 12. Vertical velocity-time history sampled at 100-kHz, without prefilter, and corresponding integrated history, with and without CFC 180 filtering

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Figure 13. Longitudinal displacement-time history sampled at 100-kHz, without prefilter, and corresponding integrated history, with and without CFC 180 filtering

Figure 14. Lateral displacement-time history sampled at 100-kHz, without prefilter, and corresponding integrated history, with and without CFC 180 filtering

Figure 15. Vertical displacement-time history sampled at 100-kHz, without prefilter, and corresponding integrated history, with and without CFC 180 filtering

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Figure 16. Longitudinal velocity-time history sampled at 400-kHz, without prefilter, and corresponding integrated history, with and without CFC 180 filtering

Figure 17. Lateral velocity-time history sampled at 400-kHz, without prefilter, and corresponding integrated history, with and without CFC 180 filtering.

Figure 18. Vertical velocity-time history sampled at 400-kHz, without prefilter, and corresponding integrated history, both with and without CFC 180 filtering

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Figure 19. Longitudinal displacement-time history sampled at 400-kHz, without prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 20. Lateral displacement-time history sampled at 400-kHz, without prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 21. Vertical displacement-time history sampled at 400-kHz, without prefilter, and corresponding integrated history, both with and without CFC 180 filtering

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Figure 22. Longitudinal velocity-time history sampled at 10-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 23. Lateral velocity-time history sampled at 10-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 24. Vertical velocity-time history sampled at 10-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

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Figure 25. Longitudinal displacement-time history sampled at 10-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 26. Lateral displacement -time history sampled at 10-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 27. Vertical displacement-time history sampled at 10-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

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Figure 28. Longitudinal velocity-time history sampled at 100-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 29. Lateral velocity-time history sampled at 100-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 30. Vertical velocity-time history sampled at 100-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

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Figure 31. Longitudinal displacement-time history sampled at 100-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 32. Lateral displacement-time history sampled at 100-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 33. Vertical displacement-time history sampled at 100-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

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Figure 34. Longitudinal velocity-time history sampled at 400-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 35. Lateral velocity-time history sampled at 400-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 36. Vertical velocity-time history sampled at 100-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

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Figure 37. Longitudinal displacement-time history sampled at 400-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 38. Lateral displacement-time history sampled at 400-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

Figure 39. Vertical displacement-time history sampled at 400-kHz, with prefilter, and corresponding integrated history, both with and without CFC 180 filtering

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The analysis of these results can lead to similar conclusions as the previous ones. For the non- prefiltered signals, the greater the sampling frequency is, the better correspondence is obtained between the sampled and the integrated curves. However, a slight offset has appeared in the compared curves when the acceleration has been filtered with CFC 180 and then integrated. If this method is used, it should be checked that initial velocity conditions or contact forces do not cause sharp, high frequency impulses that may be not properly filtered.

Regarding the prefiltered signals, as in the previous comparison, when the sampling frequency corresponds to a time interval between data points of the same order of magnitude as the calculation time step, there are significant errors in the resulting curves. For sampling frequencies of 10 and 100 KHz, there is a good agreement. Again, it is found that if the integration is performed on acceleration signals that have already been filtered with a CFC180 class filter, an offset may appear due to the initial part of the curves.

The final comparison of the post-processing methods was regarding the ensuing results in terms of severity indices according to EN1317. The reference model used for comparison was the simulation of a TB11 impact against a rigid concrete barrier. The different simulations that were run, varying output parameters, were in the same impact conditions so no differences in results should be expected. The indices were calculated with TRAP.

A first round of comparisons was performed, with the raw data obtained. The results are shown in Table 1

Without prefiltering Prefiltering

10 KHz 100 KHz 400 KHz 10 KHz 100 KHz 400 KHz ASI 1.67 1.68 1.54 1.67 1.67 1.54

THIV 28.3 28.3 25.5 28.2 28.2 25.5 (km/h) PHD 26.2 26.0 41.5 22.2 22.9 65.5 (g’s)

Table 1. Comparison of the severity indices for different post-processing options with raw data.

Differences are very large and not acceptable, especially in the PHD values. Aliasing phenomena are affecting results. Moreover, the analysis led to the following additional conclusions: − TRAP computes input data curves assuming a constant time period for the abscissa. If this value is varying, errors may appear. This is the case if raw data from simulations are used. To solve this problem, input curves should be transformed so that the time sampling period is constant. − It seems that TRAP does not implement a routine for the CFC 180 filtering prior to the computing of the severity indices.

A second round was then performed. The curves were filtered with CFC180 and the severity indices were calculated with TRAP for comparison. (Table 2).

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Without prefiltering Prefiltering

10 KHz 100 KHz 400 KHz 10 KHz 100 KHz 400 KHz ASI 1.67 1.68 1.54 1.67 1.67 1.54

THIV 28.3 28.2 25.5 28.2 28.1 25.5 (km/h) PHD 22.8 22.3 23.1 22.2 22.3 21.5 (g’s)

Table 2. Comparison of the severity indices for different post-processing options. CFC 180 filtering before TRAP computing.

The aliasing effects were reduced, but still the non-prefiltered curves were showing certain disagreement. Differences due to the sampling frequency are still having some influence. Figure 40 shows a zoom image of the differences that can take place between different outputs from the same initial model.

Figure 40. Detailed picture of differences in curves depending on the output parameters.

Taking into account all the findings above, finally a third round was performed. A re-sampling operation was performed on the curves (note: this is not necessary with pre-filtered curves, as they fulfil constant time interval because of the internal procedure for pre-filtering). The results are shown in Table 3

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Without prefiltering Prefiltering

10 KHz 100 KHz 400 KHz 10 KHz 100 KHz 400 KHz ASI 1.67 1.68 1.68 1.67 1.67 1.54

THIV 28.3 28.2 28.2 28.2 28.1 25.5 (km/h) PHD 22.8 22.2 22.3 22.2 22.3 21.5 (g’s)

Table 3. Comparison of the severity indices for different post-processing options. Constant time interval and CFC 180 filtering before TRAP computing.

From the last results it appears that, provided that a resampling is made on the different signals, a fairly good agreement is obtained except for the values in the last column, which showed the time-compression error. In particular, if output curves are obtained without prefiltering, a minimum frequency of 100 KHz would be recommended for better precision.

The conclusions of the analysis are that: − For acceleration curves obtained without the “prefilter” option, recommended sampling frequency would range between 100 KHz and the frequency corresponding to the simulation time step. In order to obtain reliable values for the severity indices, it should then be processed in order to have a constant time interval, and filtered with a CFC 180 prior to computing (e.g. with TRAP). − For acceleration curves obtained with the “prefilter” option, recommended sampling frequency would generally be 10 KHz, and should always be kept well below the frequency corresponding to the simulation time step. Then, it should then be filtered with a CFC 180 prior to computing (e.g. with TRAP).

In summary, the suggestion for a most convenient balance between accuracy in results and storage requirements in PAM-CRASH simulation is to use 10 KHz output frequency with the prefiltering option activated.

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5 ACCELEROMETER LOCATION

The influence of the sensor location was also studied. Measurements were taken in various positions of the vehicle (Figure 41). The objective was to study the effect of deviations in the position of accelerometer sensors in the accelerations and in the subsequent severity indices, due to rotational motions. The curves obtained are shown in Figure 42 to Figure 44.

32923287 3275 3270 3263 1583

COG

Figure 41 Measurement positions for the analysis of the influence of accelerometer location.

Figure 42. Longitudinal accelerations obtained in different sensor locations.

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Figure 43. Lateral accelerations obtained in different sensor locations

Figure 44. Vertical accelerations obtained in different sensor locations

In this comparison, the differences are owed to the rotation motions that the vehicle undergoes in the test, because all the measuring points are included.in the non-deformable core part of the vehicle. It is clear that if the measuring points are located in a more forward position than the centre of gravity, then the first impact - i.e. front part of the vehicle- against the wall has an increased effect on the acceleration measurement, whereas for measuring points located near the rear part of the vehicle the second impact becomes more relevant.

EN1317 Standard procedure for severity indices calculation includes instructions for compensation for instrumentation displaced from the vehicle centre of gravity. Hence, if the compensation is applied, eventual deviations of the accelerometer sensors from the centre of gravity should not necessarily cause errors in results.

However, in order to estimate the potential error that may be introduced if sensors are placed at certain distances form the actual centre of gravity and the above mentioned compensation is not taken into account, the different acceleration data obtained have been post-processed with

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TRAP. Signals have been acquired with a sampling frequency of 400 KHz, without the prefiltering option, and then filtered with a CFC180. The results are compared in Table 4

Position Displaced backwards Nominal Displaced forward

Point 3292 3287 3275 c o g 3270 3263 1583 ASI 1.19 1.35 1.52 1.68 1.95 2.17 2.38

THIV 20.7 23.2 25.8 28.2 32.1 35.4 38.5 (km/h) PHD 31.9 28.4 24.9 22.3 16.6 12.4 8.9 (g’s)

Table 4. Comparison of severity indices obtained from acceleration measurement in different locations if compensation is not performed.

It has to be noted that the differences in Table 4 are considerably high, but it is due to the fact that the points chosen for acceleration measurement in this study were clearly displaced from the centre of gravity, at distances over 20 cm, that should not be generally occur in testing without compensation. The comparison was made with the aim to demonstrate and quantify the potential effects that such displacements could cause.

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6 REFERENCES

Ref. 1 EN 1317-1: Road restraint systems – Part 1: Terminology and general criteria for test methods. European Committee for Standardization, April 1998. Ref. 2 EN 1317-2: Road restraint systems – Part 2: Performance classes, impact test acceptance criteria and test methods for safety barriers . European Committee for Standardization, April 1998 Ref. 3 PAM-CRASH TM User Notes Manual. PAM SYSTEM INTERNATIONAL, 2000