To my parents, who have always been there to love and support me.

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3 III Abstract A vast amount of research has been conducted on blunt impacts on the thorax from the lateral direction; however, significantly less has been conducted from oblique directions. Since most vehicular impacts are not directed in the purely lateral direction, there is a desire to better understand how the thoracic response differs between these two loading conditions. The Ohio State University Injury Biomechanics Research Laboratory (IBRL) has been testing thoracic response from impacts in both lateral and oblique directions on fresh postmortem human subjects (PMHS). Pendulum tests have been used to impact the thorax from oblique directions. While pendulum tests can form accurate results, sled tests are much more lifelike and provide a closer representation of a side impact crash. While there is already a sideimpact sled that can simulate lateral impact crashes, there isn t one that can simulate oblique to lateral crashes. The objective of this research is to design a sled that can simulate side impact crashes at multiple angles. 20 different sled models and sled tests were designed and simulated using a MADYMO simulation program. The T1 and T12 accelerometer data in the longitudinal and lateral directions were extracted from the simulations and compared to accelerometer data extracted from an ATD in an oblique pole impact test. Through correlating the simulations with the crash test, it was discovered that the best sled design involved a buck positioned laterally to the ATD s shoulders and accelerating into the dummy at an angle of 10 anterior to lateral.

4 To my parents, who have always been there to love and support me. IV

5 V Acknowledgements There are several people who have helped me greatly on this project. Most of all, I would like to thank Dr. John H. Bolte IV who served as an advisor for me. Dr. Bolte always made himself available to answer any questions I had and to give opinions and advice that guided me. His attitude and sense of humor helped to make the project both interesting and fun, and it was a great pleasure working with him. Dr. Bolte also supplied me with several resources when he did not have the means to help me himself. Joshua Shaw helped me a great deal by sending me crash test and sled test data. He was an excellent contact at VRTC. Since I could not make trips up there, Josh would send me or bring any information I needed. He also made time to meet with me and discuss the project. The folks at SEA Limited helped me by teaching me how to use the MADYMO simulation program during a summer co-op. Without their help, this project would have been impossible. Douglass Morr and Adam Ratliff also made time to meet with me, even after I was no longer working there, to advise me in the project. Adam Ratliff helped me a great deal with the MADYMO simulation program. He made time to meet with me or explain how to do certain things with the program whenever I asked him to, saving me a great deal of time and sanity. Finally, I would like to thank the folks at VRTC for their advice at quarterly meetings. Specifically, I would like to thank Bruce Donelly for sending me a MatLAB program that was used to compare data.

6 VI VITA May 2, Born- Mayfield Heights, Ohio FIELDS OF STUDY Major Field: Mechanical Engineering

7 VII Table of Contents Abstract... III Acknowledgements... V VITA... VI List of Figures... VIII List of Tables... X Chapter 1: Introduction... 1 Chapter 2: Previous Work... 3 Chapter 3: Objectives... 9 Chapter 4: Methods : Validation of an Existing Side-Impact Sled : Adding Longitudinal Components Trial 1: Accelerating the ATD Trial 2: Accelerating the Barrier in an Oblique Direction Trial 3: Changing the barrier angle and directing it laterally Trial 4: Rotating the barrier and directing it laterally with a constant velocity Trial 5: Cylindrical barrier directed laterally : Signal processing and Analysis Chapter 5: Results : Validation of the Side-Impact Buck : Results for Simulating an Oblique Pole Impact Test Chapter 6: Discussion Chapter 7: Conclusions References Appendix A. Dual Occupant Side Impact Sled Dimensions B. MatLAB Files B.1 Side Impact Validation B.2 Script for plotting MADYMO and crash test data B.3 NISE Analysis C. Crash Test Data: T1, T12, and Pelvis Accelerometer Crash Test Data; Test Number D. MADYMO File for -10 Oblique Acceleration E. Accelerometer plots for -10 oblique acceleration... 79

8 VIII List of Figures Figure 1: Belted occupants by seriously injured body region in nearside crashes [4]... 1 Figure 2: Primary direction of force in a 90 car-to-car impact [5]... 2 Figure 3: Pendulum test [5]... 4 Figure 4: Mid-sternum in the transverse plane [5]... 5 Figure 5: Instrumentation in the transverse plane [5]... 6 Figure 6: Subject O-T0503 chestband contours [5]... 7 Figure 7: Normalized forces vs. deflection curves for lateral and oblique loading [5]... 8 Figure 8: Lateral and oblique cross sections of force vs. deflection curves [5]... 8 Figure 9: Applied side impact pulse Figure 10: Side-impact sled and MADYMO model of side-impact sled Figure 11: Pre-impact oblique pole impact crash overview [7] Figure 12: Post-impact oblique pole impact overview [7] Figure 13: Trial 1: Accelerating the ATD Figure 14: Trial 2: Accelerating the barrier in an oblique direction Figure 15: Trial 3: changing the barrier angle and directing it laterally Figure 16: Trial 4: Rotating the barrier and directing it laterally with constant velocity Figure 17: Post-impact oblique pole impact test [7] Figure 18: Trial 5: Cylindrical barrier directed laterally Figure 19: Frontal view of the side-impact Madymo model Figure 20: Side view of the side-impact Madymo model Figure 21: T1 Y axis accelerometer curve for validation of the side-impact buck Figure 22: T12 Y axis accelerometer curve for validation of the side-impact buck Figure 23: T1 X accelerometer curves for altered barrier angle at Figure 24: T1 Y accelerometer curves for altered barrier angle at Figure 25: T12 X accelerometer curves for altered barrier angle at Figure 26: T12 Y accelerometer curves for altered barrier angle at Figure 27: T1 Y accelerometer curves for oblique acceleration directed at Figure 28: T1 Y accelerometer curves for oblique acceleration directed at Figure 29: T12 X accelerometer curves for oblique acceleration directed at Figure 30: T12 Y accelerometer curves for oblique acceleration directed at Figure 31: T1 X accelerometer curves for oblique acceleration directed at Figure 32: T1 Y accelerometer curves for oblique acceleration directed at Figure 33: T12 X accelerometer curves for oblique acceleration directed at Figure 34: T12 Y accelerometer curves for oblique acceleration directed at Figure 35: Frontal view of the oblique acceleration model at Figure 36: Side view of the oblique acceleration model at Figure 37: Implementation of the oblique acceleration model Figure 38: Position profile Figure 39: Dual occupant side impact sled dimensions in mm [3] Figure 40: Oblique acceleration -10 ; T1-X Figure 41: Oblique acceleration -10 ; T1-Y Figure 42: Oblique acceleration -10 ; T1-Z... 80

9 Figure 43: Oblique acceleration -10 ; T1-RES Figure 44: Oblique acceleration -10 ; T12-X Figure 45: Oblique acceleration -10 ; T12-Y Figure 46: Oblique acceleration -10 ; T12-Z Figure 47: Oblique acceleration -10 ; T12-RES Figure 48: Oblique acceleration -10 ; PEL-X Figure 49: Oblique acceleration -10 ; PEL-Y IX

10 X List of Tables Table 1: Pendulum test matrix [5]... 5 Table 2: Normalized impact results [5]... 7 Table 3: Test matrix Table 4: Final scores for 20 simulations Table 5: Comparison to correlation scores between two pole tests... 36

11 1 Chapter 1: Introduction In a report filed by the National Highway Traffic Safety Administration [4], it was reported that in the years , for nearside impacts, the majority of seriously injured (AIS3+), belted occupants suffered from injuries to the chest or back. Figure 1 displays the distribution of serious injuries by body region for modern vehicles (MY 95+) and older models (MY 94-). While the percentage of injuries to the chest and back did drop with the new models, it still is the most common body region that is injured in nearside impacts. Figure 1: Belted occupants by seriously injured body region in nearside crashes [4] In the past there has been vast amount of research conducted on the thoracic response when being struck from the lateral direction. The information acquired from this research has been used to design better side-impact anthropomorphic test dummies [ATDs] which are used to test the safety of automobiles. In much of the previous research, it has been assumed that the thoracic responses when being struck from an oblique direction were similar enough to use the side-impact ATDs for safety testing. However, recently, it has been discovered that the thoracic stiffness response is significantly different when being struck from an oblique angle. The most

12 2 common side-impact crash involves a 90 car-to-car impact in which the struck vehicle s preimpact velocity is 15 mph and the impacting vehicle s pre-impact velocity is 30 mph [5]. This situation dictates that the struck vehicle s post-impact primary direction of force (PDOF) is oblique in nature causing oblique loading to the occupant(s). [site Josh s paper] (Figure 2) Figure 2: Primary direction of force in a 90 car-to-car impact [5] Since the loading on the occupant(s) in many car accidents is not oriented completely laterally, there is a desire to discover precisely how the thoracic responses differ in order to design an ATD that is more biofidelic for multiple angled impacts.

13 3 Chapter 2: Previous Work The amount of research published on the thoracic response due to blunt impact directed at an oblique angle is very limited. Thus far, the testing done at The Ohio State University Injury Biomechanics Research Laboratory, has been thoracic component testing by use of a pneumatic impactor. To date, two main studies have been conducted involving blunt pendulum impacts to the thorax. Eppinger et al. (1984) conducted four pendulum tests to the lateral thorax as part of a larger study. In his testing, he never attempted to determine thoracic stiffness response. Viano et al. (1989) conducted 16 pendulum tests on the thorax directed 30 of lateral, all of which sustained fractures, even at the lowest speed of 3.6 m/s. The results were all scaled to a 50 th percentile male at a nominal impact speed of 4.3 m/s. Viano determined that the peak force response was 2.67 kn (+/ kn) and the peak compression was 26.1% (+/- 4.1%). These magnitudes matched the values that Eppinger discovered through his testing and thus he concluded that the thoracic responses due to oblique and lateral loading are comparable. The results from these tests were used in the design of side-impact ATDs. The pendulum test setup used by Shaw et al. at The Ohio State Injury Biomechanics Research Laboratory was based off of Viano s test setup. The setup involved a PMHS whose arms were placed at 90 of flexion, thus moving them above the impact location of the pendulum but not enough to raise the thoracic cage. A 23 kg linear pneumatic ram would travel at a nearly constant velocity until striking the thorax. The impacting surface of the ram was a 6 in. diameter aluminum plate and the impacting velocity for nearly all the subjects was 2.5 m/s. Immediately prior to impact, the harness holding the subject upright would be released, eliminating any possible tension. (Figure 23)

14 4 Figure 3: Pendulum test [5] Seven subjects were impacted twice; once with an impact directed laterally (90 or 270 ) and once obliquely (60 or 300 ). The test matrix (Table 1) was created so that oblique loading occurred on one side of the transverse plane while lateral loading occurred on the other; the PMHS would be struck at 90 and 300 or 270 and 60 (Figure 4). The test matrix was designed this way in order to avoid the impact of the first trial affecting the second. Also, all of the impacts were low speed (2.5 m/s) in order to avoid fractures that might influence the results of subsequent impacts. The order in which oblique and lateral impacts occurred was also alternated..

15 5 Table 1: Pendulum test matrix [5] Figure 4: Mid-sternum in the transverse plane [5] During each impact, each PMHS was instrumented with accelerometers and a chestband. The accelerometers were mounted directly onto the ribcage whereas the chestband was wrapped around the chest externally at the same rib level that the accelerometers were mounted. The chestband was used to measure chest deformation in time. Figure 5 diagrams the setup in the transverse plane. After the final impact on each subject, an autopsy was conducted in which each

16 6 rib was carefully analyzed in order to detect possible fracture and internal organs in the upper portion of the abdominal cavity were also inspected Figure 5: Instrumentation in the transverse plane [5] For each test, the maximum chest deflection and measured force on the thoracic cage were recorded and normalized to the 50 th percentile male using effective mass and scaling factors. The normalized results for each test are displayed in Table 2. The results show that the average maximum force for oblique loading is N (+/ N) while the average maximum force for lateral loading is significantly greater at N (+/ N). The average maximum chest deflection for oblique loading is mm (+/ mm) where as the average maximum chest deflection for lateral loading is significantly less at mm (+/ mm). Figure 6 displays an example of the chestband data for both lateral and oblique impact for one of the test subjects.

17 7 Table 2: Normalized impact results [5] Figure 6: Subject O-T0503 chestband contours [5] Using accelerometer and chestband data, normalized force and deflection curves were obtained for lateral and oblique impacts (Figure 7). The average forces vs. deflection curves for oblique and lateral loading can be easily compared in Figure 7. This Figure displays the force vs. deflection targets calculated from the averages and standard deviations of the normalized subjects. It is evident from this plot that when being struck from oblique directions, less force is needed to cause a specific deflection than when being struck in the lateral direction.

18 8 Figure 7: Normalized forces vs. deflection curves for lateral and oblique loading [5] Figure 8: Lateral and oblique cross sections of force vs. deflection curves [5] To date, the only testing that has been done to measure thoracic response when being struck from oblique angles is component testing. There is much more to be gained by doing whole body impacts. The thoracic cage is typically the first part of the body struck in a car accident, and it transmits forces to the neck and head. Unlike component testing, full body sled testing can be very useful for simulating this response.

19 9 Chapter 3: Objectives In order to determine the full body thoracic response due to oblique loading on PMHS, a sled test that can accurately simulate the type of oblique loading that would be seen in a typical car accident would be a useful tool. The objective of this research is to Create and validate a Madymo computer simulated model of an existing side-impact buck Adjust the Madymo model so that oblique loading can occur Validate the Madymo model of the oblique angle sled buck with crash test data

20 10 Chapter 4: Methods 4.1: Validation of an Existing Side Impact Sled When designing the multiple angle side impact sled, a computer simulation program called Madymo was used. The software allows the user to input validated ATDs into an environment that they created and then apply forces or accelerations in order to see how the ATD will react in virtual space with virtual surroundings. Since the original plan of design was to model the initial design off of a pre-existing sideimpact sled test, the first step was to validate the pre-existing sled in Madymo. The sled buck that the Madymo model was based off of is used on a Dual Occupant Side Impact Buck at the Transportation Research Center [TRC] in Marysville, Ohio. Two sled tests were used to validate the model and both had rigid, flat wall configurations and the SID IIs 5 th percentile female ATD was used. For both tests, the impact speed was 6.7 m/s and was acquired by placing an acceleration pulse on the buck that peaked at m/s^2 and had a duration of 80 ms. Sled tests are typically designed so that the buck impacts the dummy at a defined velocity and the buck is no longer accelerating at impact (Figure 9).

21 Acceleration (g) Peak Acceleration g Point of Impact Vel = 6.7 m/s 0 2 Pulse Duration ms Time (s) Figure 9: Applied side impact pulse In Madymo, it is common to accelerate the dummy into the environment because this allows the user to ignore the masses of the environment. The reaction of the dummy will then be dictated only by the defined contact characteristics. For the case of the sled, where most of the materials are made of a hard metal, the contact characteristics are defined by the material properties of the ATD itself. These contact characteristics are already defined in Madymo. Figure 10 displays the relationship between an actual side-impact sled and my Madymo model of the sled.

22 12 Figure 10: Side-impact sled and MADYMO model of side-impact sled For validation of the side-impact buck, the y-axis or lateral accelerometer results of T1 and T12 of the Madymo model were compared to the same accelerometers in the actual ATD from the two sled tests. Matching the relative shape, peaks and duration of the impact acceleration pulses was the overall goal. 4.2: Adding Longitudinal Components In order to validate a model of a sled buck for use in multiple angled impacts, there needs to be some results to validate the exactness of the model. An oblique pole impact crash test conducted by Vehicle Research and Testing Center; Test Number was chosen for validation. This test involved a 2002 Chevrolet Impala accelerating into a pole at an oblique angle of 15 of lateral at approximately 37 kph. Figure 11 displays the setup. Figure 12 shows how the geometry of the car door has changed and it is evident from this image that both longitudinal and lateral loading were present on the ATD, which was seated in the driver s seat.

23 13 15 Figure 11: Pre-impact oblique pole impact crash overview [7] Figure 12: Post-impact oblique pole impact overview [7]

24 Trial 1: Accelerating the ATD The first attempt of the design of the Madymo model consisted of merely changing the direction in which the dummy was accelerated so that there was some longitudinal component (Figure 13). Figure 13: Trial 1: Accelerating the ATD Trial 2: Accelerating the Barrier in an Oblique Direction The second attempt involved accelerating the barrier into the dummy. Instead of inputting an acceleration pulse, the position of the door was inputted into the MOTION.JOINT_POS command. This data was obtained by double integrating acceleration data. A position profile was used instead of an acceleration profile is because with a position profile, the forces on the dummy are determined only by the contact characteristics defined between the ATD and the barrier. This allows for the mass of the barrier to be neglected.

25 15 The lateral acceleration profile was separated into two parts, the pre-impact profile and post-impact profile; impact defined as the when the ATD first makes contact with the left car door. The pre-impact profile included the lateral door acceleration data plus the lateral acceleration data of the CG of the vehicle. The post-impact profile included only the lateral door acceleration data. Once the position profile in the lateral direction was obtained, the longitudinal position profile was calculated for different impact angles. Simulations were run and evaluated for seven different impact angles (-15 to 15 of lateral in 5 intervals). Figure 14 displays what the simulation and direction of impact would look like for the impact angle of 15. Lateral position profile - Longitudinal position profile Resultant position component Figure 14: Trial 2: Accelerating the barrier in an oblique direction Trial 3: Changing the barrier angle and directing it laterally Instead of adding a longitudinal position profile to the barrier to account for oblique loading, another option is to simply alter the angle of the barrier. For this method, the lateral

26 16 position profile would be the same as it was in the previous method. This idea has one major benefit over changing the direction of impact; it would be much easier to implement into an actual sled test and it would be easier to adjust for different angles. Simulations were run and evaluated for 9 different door angles (-20 to 20 in 5 intervals). Figure 15 displays the simulation, barrier angle and direction of impact for the -15 barrier angle case. +θ -θ Figure 15: Trial 3: changing the barrier angle and directing it laterally Trial 4: Rotating the barrier and directing it laterally with a constant velocity When watching the video of the oblique impact pole test, it is clear that there is an element of rotation implemented onto the door. Figure 12 displays how the pole impacting the vehicle at an angle causes the door to rotate. This element of rotation may be necessary in a sled to accurately simulate the oblique pole impact test. For this case, the door will rotate at approximately 142 rpm clockwise. This rate was determined by measuring the door angle at specific points in time during the video. Simulations will be run and evaluated for 4 different lateral translation profiles (Figure 16). Because the lateral acceleration profile used for the

27 17 previous two methods included rotational elements, it is not necessary to use the same profile. The new profiles will be set at constant velocities. The 4 lateral translation profiles include 2, 2.5, 3, and 4 m/s. θ Figure 16: Trial 4: Rotating the barrier and directing it laterally with constant velocity Trial 5: Cylindrical barrier directed laterally In oblique pole impact tests, the geometry of the door changes drastically during impact. Madymo cannot simulate this change in geometry, and even if it could, implementing a design in which the barrier s geometry could change in time would be very complex. However, the shape of the barrier does not need to be a flat wall. Figure 17 shows the post-impact outer door geometry. The curvature is almost cylindrical and so the final method will include a cylindrical shaped barrier with the same lateral profile used for Trials 2 and 3. Figure 18 displays the cylindrical barrier simulation.

28 18 Figure 17: Post-impact oblique pole impact test [7] Figure 18: Trial 5: Cylindrical barrier directed laterally 4.3: Signal processing and Analysis The crash test accelerometer data acquired from the physical oblique pole impact test was taken at Hz and was pre-filtered. In order for comparisons from all simulations to be

29 19 constant, the Madymo accelerometer data was also acquired at Hz and was filtered using a 300 Hz Butterworth low pass filter, which is standard practice for these types of tests. For every simulation, only 45 ms of data was evaluated. This is because at about 45 ms into the pole test, the seat belt appeared to engage, adding a variable that would not be present in a sled test. Two accelerometers were evaluated for every simulation. The T1 and T12 accelerometers were both evaluated in the X and Y directions (longitudinal and lateral directions respectively) allowing for a total of four accelerometer curves to be evaluated. The MADYMO curves were compared to the oblique pole impact curves using a MatLAB program that was designed to score the likeness of two curves in three parameters; shape, amplitude and phase. For each parameter, the likeness of the two curves were scored between 0 and 1; 0 meaning the curves were identical and 1 meaning the curves did not correlate at all. The scores for the three parameters were then added together so that the final score lied between 0 and 3. Through this process, each simulation was given four final scores, one for each accelerometer curve. The four scores were averaged for each simulation and compared to other simulations. The simulation with the lowest score would be the test that was mostly representative of the oblique pole impact test. A total of 20 simulations were run, and evaluated using the aforementioned method. The test matrix can be seen in Table 3. Since the oblique acceleration at 0 and the altered barrier angle at 0 are the same simulation it was only run once but shows up twice on the test matrix as Test no. 4.

30 20 Table 3: Test matrix Trial # Trial # Trial # Oblique Acceleration Altered Barrier Angle Rotating Barrier Impact Direction Barrier Angle Velocity m/s m/s m/s m/s Cylindrical Barrier

31 21 Chapter 5: Results 5.1: Validation of the Side Impact Buck The MADYMO model used to validate the side-impact buck was based off of the actual side-impact buck. The dimensions of the model can be seen in Figures 19 and 20. The input acceleration pulse on the ATD in the MADYMO model was the same as the input acceleration pulse on the buck in the sled test. The ATD was positioned away from the buck so that impact occurred at the end of the applied pulse as in the actual sled test. The buck in the MADYMO model was much longer in the longitudinal direction than the buck used for actual sled tests. This was done because the ATD s legs in MADYMO would wrap around the buck and get caught. By making the buck more like a wall, the response of the ATD in MADYMO was much more similar to the response of the ATD in real life m 0.7 m Figure 19: Frontal view of the side-impact Madymo model

32 22 a = m b = m c = m d = m e = m 15 d c b a 0.41 m e 8 Figure 20: Side view of the side-impact Madymo model Only the T1 and T12 accelerometers oriented in the lateral direction were reviewed for validation. This is because the sled test was directed purely laterally with no longitudinal components. The results can be seen in Figures 21 and 22. It is clear from these plots that the MADYMO results are very similar to the sled test results in amplitude, phase and shape.

33 23 T1 Y acceleration Sled Test No. S Sled Test No. S MADYMO Validation Acceleration (g) Time (ms) Figure 21: T1 Y axis accelerometer curve for validation of the side-impact buck T12 Y acceleration Sled Test No. S Sled Test No. S MADYMO Validation 40 Acceleration (g) Time (ms) Figure 22: T12 Y axis accelerometer curve for validation of the side-impact buck

34 24 5.2: Results for Simulating an Oblique Pole Impact Test After running 20 simulations, each one was compared to the accelerometer data of the oblique pole impact test and evaluated. The final score from each simulation can be seen in Table 4. The results for accelerating the ATD into the barrier are not shown. Simulations from that trial were ruled out because unlike sled tests, in crash test, the door continues to accelerate post-impact.

35 25 Table 4: Final scores for 20 simulations T1_X T1_Y T12_X T12_Y Final Score Oblique Acceleration nd Place st Place Altered Barrier Angle rd Place Rotating Barrier Vel.=2 m/s Vel.=2.5 m/s Vel.=3 m/s Vel.=4 m/s Cylindrical Barrier Average Score Standard Deviation Across the board, the results show that the simulations do a much better job recreating the lateral thoracic response than the longitudinal thoracic response. For both T1_Y and T12_y curves, the majority of scores lie below 0.1, whereas for the T1_X and T12_X curves, the majority of scores lie above 1. The results that display the best simulation was the oblique acceleration directed at -10. The second best simulation was the oblique acceleration directed at -5 and the third best simulation was the altered barrier angle, altered at -20. The three best simulations will be

36 26 considered and analyzed in regards to their final scores, as well as how each of the curves faired independently. The altered barrier angle set at -20 had a final score of This equates to about one standard deviation below the average score and one standard deviation above the lowest score. The plots of the MADYMO curves and oblique pole impact curves for this simulation can be seen in Figures This simulation did very well in the longitudinal direction with the T1_X curve scoring nearly one standard deviation below the average and the T12_X curve scoring over one standard deviation below the average. This simulation did not score as well in the lateral direction. Both the T1_Y and T12_Y curves had scores above the average and the T12_Y curve had one of the three highest scores. T1 X acceleration 150 MADYMO Pole Test 100 Acceleration (g) Time (ms) Figure 23: T1 X accelerometer curves for altered barrier angle at -20

37 T1 Y acceleration MADYMO Pole Test Acceleration (g) Time (ms) Figure 24: T1 Y accelerometer curves for altered barrier angle at -20

38 T12 X acceleration MADYMO Pole Test Acceleration (g) Time (ms) 200 Figure 25: T12 X accelerometer curves for altered barrier angle at -20 T12 Y acceleration MADYMO Pole Test 150 Acceleration (g) Time (ms) Figure 26: T12 Y accelerometer curves for altered barrier angle at -20

39 29 The second best final score was the -5 oblique acceleration simulation. Its final score was over one standard deviation below the average. The plots of the MADYMO curves and oblique pole impact curves for this simulation can be seen in Figures This simulation scored very well with the T1 accelerometer. Its best scored curve was the T1_X curve which had the lowest score. The T1_Y and T12_X curves also scored below the average. The T12_Y curve was the only one that scored above the average. T1 X acceleration 150 MADYMO Pole Test 100 Acceleration (g) Time (ms) Figure 27: T1 Y accelerometer curves for oblique acceleration directed at -5

40 T1 Y acceleration MADYMO Pole Test 150 Acceleration (g) Time (ms) Figure 28: T1 Y accelerometer curves for oblique acceleration directed at -5 T12 X acceleration 150 MADYMO Pole Test Acceleration (g) Time (ms) Figure 29: T12 X accelerometer curves for oblique acceleration directed at -5

41 T12 Y acceleration MADYMO Pole Test Acceleration (g) Time (ms) Figure 30: T12 Y accelerometer curves for oblique acceleration directed at -5 The lowest final score was the -10 oblique acceleration simulation. Its final score was nearly two standard deviations below the average and it did substantially better than the rest of the simulations. The plots of the MADYMO curves and oblique pole impact curves for this simulation can be seen in Figures Both the T12 accelerometer curves scored lowest and the T1 accelerometer curves each scored well below the average as well.

42 32 T1 X acceleration 150 MADYMO Pole Test 100 Acceleration (g) Time (ms) Figure 31: T1 X accelerometer curves for oblique acceleration directed at T1 Y acceleration MADYMO Pole Test Acceleration (g) Time (ms) Figure 32: T1 Y accelerometer curves for oblique acceleration directed at -10

43 33 T12 X acceleration 150 MADYMO Pole Test Acceleration (g) Time (ms) Figure 33: T12 X accelerometer curves for oblique acceleration directed at T12 Y acceleration MADYMO Pole Test Acceleration (g) Time (ms) Figure 34: T12 Y accelerometer curves for oblique acceleration directed at -10

44 34 The bench setups for all of the tests were identical with the only exception being the position of the dummy relative to the buck. The size and shape of the buck was also identical for all the simulations (other that the impacting cylinder). The positioning of the buck was different in every simulation. Adjustments involving the distance away from the ATD, the angle of the buck were altered. The dimensions of the environment for the -10 oblique acceleration model can be seen in Figures 35 and 36. All dimensions not shown can be assumed to be the same as the Side-Impact validation model m Figure 35: Frontal view of the oblique acceleration model at 10

45 m 0.66 m 83 Figure 36: Side view of the oblique acceleration model at 10 Chapter 6: Discussion The simulation that best matched the results for the T1 and T12 accelerometers in the longitudinal and lateral directions was the oblique acceleration model directed at -10. This design scored significantly better than the others; however, there are several factors to consider before design. While a good comparison exists amongst all of the simulations, and it is clear which simulations scored the best, it is still unknown whether these scores are good enough or if other design ideas should be investigated. Another oblique pole impact test conducted by the Vehicle Research and Test Center (Test Number: ) was obtained in which the parameters were identical as that of Test Number: This test also involved a 2002 Chevrolet Impala accelerating into a pole at an oblique angle of 15 of lateral at approximately 37 kph. The T1 and T12 accelerometer data was obtained for this test for both the longitudinal (X) and Lateral (Y)

46 36 directions. The accelerometer curves obtained from this oblique pole impact test were evaluated against the accelerometer curves from the original oblique pole impact test in the same way that the simulation accelerometer curves were evaluated. The results of this evaluation show that there is a significantly closer correlation between the accelerometer curves in the lateral direction than in the longitudinal direction. The T1_Y and T12_Y curves both scored under The T12_X curve scored worse of all the curves with a score of For these three curves, the best simulation (oblique acceleration -10 ) had scores that were very comparable. For the T1_X curve, the correlation score between the two pole tests was significantly lower than the scores for any of the simulations. These results can be found in Table 5. Table 5: Comparison to correlation scores between two pole tests T1_X T1_Y T12_X T12_Y Final Score Oblique Acceleration nd Place Oblique Acceleration st Place Altered Barrier Angle rd Place Oblique Pole Impact Test ( ) The final score correlating the two pole tests is Since a sled test doesn t carry the same parameters that a crash test has; including material properties of the seat and door, and the geometry of the seat and door, it would be unreasonable to expect a score as low as The only correlation curve that is lacking is the T1_X curve. Changing the arm angle or the way the ATD is seated may be ways to improve this number. While the score is higher for the -10 oblique acceleration model than the -20 altered barrier angle model, the implementation of the latter into a physical sled would be much simpler and more practical than the implementation of the former. The design for the -20 altered barrier

47 37 model could allow for only minor adjustments to the side-impact buck. The side-impact buck would only need to be altered so that a hinge joint could be added to the bottom of the buck. This would allow for different buck angles so that several oblique angles could be tested with one buck. The oblique acceleration model would require a more complex design in order to implement the concept into a physical sled design. Both the barrier and the bench would need a hinge where their orientations could be manipulated so that as the barrier impacts the ATD, it is always oriented perpendicular to the ATD s shoulders. Figure 37 shows one possible design. The top picture shows what the side-impact buck looks like from an overhead view and the bottom picture shows how it could be oriented so that oblique loading can occur. The most obvious problem is clearance. At negative and large positives angles, there are clearance issues between the barrier and the bench. Hinge Joint Buck/Barrier Bench ATD θ Track θ Figure 37: Implementation of the oblique acceleration model

48 38 Even though the oblique acceleration sled design would be much more difficult to implement, the results were substantially better for this design than any others. The position profile used can be seen in Figure 38. The position profile file can be found in the Appendix. Position (meters) Position Profile in the 10 Direction y = 9E+08x 6 + 1E+08x 5 5E+06x x x x R² = Time (sec) Figure 38: Position profile

49 39 Chapter 7: Conclusions The sled test that best simulated the Oblique Pole Impact Test conducted by Vehicle Research and Transportation Center; Test Number , include a buck positioned perpendicular to the ATD s shoulders and accelerating inward at an angle of 10 of lateral.

50 40 References 1. Eppinger, R. (1979) Prediction of thoracic injury using measurable experimental parameters. Seventh International Technical Conference on Experimental Safety Vehicles (ESV). Pp U.S. Department of Transportation, Washington, D.C. 2. Eppinger, R., Marcus, J. and Morgan, R. (1984) Development of dummy and injury index for NHTSA s thoracic side impact protection program. Proc. SAE Government/Industry Meeting. Pp Society of Automotive Engineers, Warrendale, PA. 3. Rhule, H., Hagedorn, A. (2004) Repeatability and reproducibility analysis of the SID-IIs FRG dummy in the sled test environment. Vehicle Research and Test Center. 4. Samaha, RR, Elliott, DS. (2003) NHTSA side impact research: motivation for upgraded test procedures. 5. Shaw, JM, Herriott, RG, McFadden, JD, Donnelly, BR, and Bolte, JH. (2006) Oblique and Lateral Impact Response of the PMHS Thorax. 50 th Stapp Car Crash Journal. Society of Automotive Engineers, Warrendale, PA. 6. Transportation Research Center Inc. (2004) Vehicle Research and Test Center 2002 Chevrolet Impala into rigid pole barrier; TRC Inc. Test Number: Transportation Research Center Inc. (2004) Vehicle Research and Test Center 2002 Chevrolet Impala into rigid pole barrier; TRC Inc. Test Number: Viano, D. (1989) Biomechanical responses and injuries to blunt lateral impact. 33 rd Stapp Car Crash Conference. Pp Society of Automotive Engineers, Warrendale, PA.

51 41 Appendix A. Dual Occupant Side Impact Sled Dimensions Figure 39: Dual occupant side impact sled dimensions in mm [3]

52 42 B. MatLAB Files B.1 Side Impact Validation %MADYMO Script %Side-Impact Validation n=1; r=1; while r<45009 time(n)=data(r); t1_res(n)=data(r+1)/9.8; t1_xacc(n)=data(r+2)/9.8; t1_yacc(n)=data(r+3)/9.8; t1_zacc(n)=data(r+4)/9.8; t12_res(n)=data(r+5)/9.8; t12_xacc(n)=data(r+6)/9.8; t12_yacc(n)=data(r+7)/9.8; t12_zacc(n)=data(r+8)/9.8; r=r+9; n=n+1; end time=time-580; figure(1) plot(time_sled,t1_yaccel,time_sled2, T1_yaccel2,time,t1_yacc) title('t1_y acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ]) figure(2) plot(time_sled,t12_yaccel,time_sled2, T12_yaccel2,time,t12_yacc) title('t12_y acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ])

53 43 B.2 Script for plotting MADYMO and crash test data %MADYMO_Script %Reads MADYMO Data and Plots against Crash Test Data n=1; r=1; while r<11310 end %Reading MADYMO Data time(n)=data(r); t1_res(n)=data(r+1)/9.8; t1_xacc(n)=data(r+2)/9.8; t1_yacc(n)=data(r+3)/9.8; t1_zacc(n)=data(r+4)/9.8; t12_res(n)=data(r+5)/9.8; t12_xacc(n)=data(r+6)/9.8; t12_yacc(n)=data(r+7)/9.8; t12_zacc(n)=data(r+8)/9.8; pel_res(n)=data(r+9)/9.8; pel_xacc(n)=data(r+10)/9.8; pel_yacc(n)=data(r+11)/9.8; pel_zacc(n)=data(r+12)/9.8; r=r+13; n=n+1; [b,a]=butter(7,300*2*.0001,'low'); %Low-pass filter time=time-595; T1_R=sqrt((T1_X).^2+(T1_Y).^2+(T1_Z).^2); T12_R=sqrt((T12_X).^2+(T12_Y).^2+(T12_Z).^2); figure(1) t1_res_f=filtfilt(b,a,t1_res); plot(time,t1_res_f,'--',time,t1_r,'r') title('t1_resultant acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ]) figure(2) t1_xacc_f=filtfilt(b,a,t1_xacc); plot(time,t1_xacc_f,'--',time,t1_x,'r') title('t1_x acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ]) figure(3) t1_yacc_f=filtfilt(b,a,t1_yacc); plot(time,t1_yacc_f,'--',time, T1_Y,'r') title('t1_y acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ]) figure(4)

54 44 t1_zacc_f=filtfilt(b,a,t1_zacc); plot(time,t1_zacc_f,'--',time,t1_z,'r') title('t1_z acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ]) figure(5) t12_res_f=filtfilt(b,a,t12_res); plot(time,t12_res_f,'--',time,t12_r,'r') title('t12_resultant acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ]) figure(6) t12_xacc_f=filtfilt(b,a,t12_xacc); plot(time,t12_xacc_f,'--',time,t12_x,'r') title('t12_x acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ]) figure(7) t12_yacc_f=filtfilt(b,a,t12_yacc); plot(time,t12_yacc_f,'--',time,t12_y,'r') title('t12_y acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ]) figure(8) t12_zacc_f=filtfilt(b,a,t12_zacc); plot(time,t12_zacc_f,'--',time,t12_z,'r') title('t12_z acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ]) figure(9) pel_xacc_f=filtfilt(b,a,pel_xacc); plot(time,pel_xacc_f,'--',time,pel_x,'r') title('pel_x acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ]) figure(10) pel_yacc_f=filtfilt(b,a,pel_yacc); plot(time,pel_yacc_f,'--',time, PEL_Y,'r') title('pel_y acceleration') xlabel('time (ms)') ylabel('acceleration (g)') axis([ ])

55 45 B.3 NISE Analysis %function [niseph,niseamp,niseshp] = nisetara(file1,file2); file1=t12xg; file2=t12_x; a=file1; b=file2; l=length(a); ac=xcorr(a); raa0=ac(l); bc=xcorr(b); rbb0=bc(l); abc=xcorr(a,b); rab0=abc(l); denom=(raa0+rbb0); nise=1-(2*rab0/denom); rabmax=max(abc); niseph=nise-1+(2*rabmax/denom); niseshp=1-(rabmax/(sqrt(raa0*rbb0))); niseamp=nise-niseph-niseshp; blank=' '; title=' NISE Comparison'; line1=' NISE(phase) '; line2=' NISE(amplitude) '; line3=' NISE(shape) '; line4=' NISE '; subtitle=' Series 1 versus Series 2'; nisephc=num2str(niseph); niseampc=num2str(niseamp); niseshpc=num2str(niseshp); nisec=num2str(nise); line1=[line1,nisephc]; line2=[line2,niseampc]; line3=[line3,niseshpc]; line4=[line4,nisec]; % clc disp(title) disp(subtitle) disp(blank) disp(blank) disp(line1) disp(line2) disp(line3) disp(line4)

56 46 C. Crash Test Data: T1, T12, and Pelvis Accelerometer Crash Test Data; Test Number Name TIME T1_X T1_Y T1_Z T12_X T12_Y T12_Z PEL_X PEL_Y

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