METHODOLOGY FOR DESIGN OF VEHICLE FRONT OF AN URBAN CAR
FOR SAFETY OF V ULNERABLE R OAD
U SERS
HARIHARAN SANKARA SUBRAMANIAN
DEPARTMENT OF MECHANICAL ENGINEERING INDIAN INSTITUTE OF TECHNOLOGY DELHI
NOVEMBER 2015
©INDIAN INSTITUTE OF TECHNOLOGY,DELHI,NEW DELHI,2015
ALL RIGHTS RESERVED
METHODOLOGY FOR DESIGN OF VEHICLE FRONT OF AN URBAN CAR
FOR SAFETY OF V ULNERABLE R OAD
U SERS
by
HARIHARAN SANKARA SUBRAMANIAN Department of Mechanical Engineering
Submitted
In fulfilment of the requirements of the degree of DOCTOR OF PHILOSOPHY
to the
Indian Institute of Technology Delhi
November 2015
i
CERTIFICATE
This is to certify that the thesis entitled Methodology for Design of vehicle front of an urban car for safety of vulnerable road users being submitted by Mr. Hariharan Sankarasubramanian to the Indian Institute of Technology Delhi for the award of Doctor of Philosophy in the Department of Mechanical Engineering is a record of bonafide research work carried out by him. He has worked under our guidance and has fulfilled the requirements for the submission of thesis, which, in our opinion, has reached the requisite standard.
The results contained in this thesis have not been submitted in part or full, to any University or Institute for the award of degree or diploma.
(Prof. Anoop Chawla) Henry Ford Chair Professor
Department of Mechanical Engineering Indian Institute of Technology Delhi, New Delhi – 110016
(Prof. Sudipto Mukherjee) Volvo Chair Professor
Department of Mechanical Engineering Indian Institute of Technology Delhi, New Delhi – 110016
(Prof. Dr.-Ing. Dietmar Göhlich) Department Chair
Methods for Product Development and Mechatronics Technische Universitaet Berlin
Berlin - 10623
iii
ACKNOWLEDGEMENTS
Words fall short in expressing my utmost respect and gratitude for the constant support and inspiration provided by my parents, Dr. H Sankara Subramanian and Mrs. Jayanthi Sankarasubramanian, to pursue my educational aspirations.
I wish to convey my special thanks to my doctoral supervisors Prof. Anoop Chawla, Prof.
Sudipto Mukherjee and Prof. Dietmar Goehlich, who guided me in my research endeavours. They provided me with the right environment to evolve as an independent researcher.
I would also like to sincerely thank my Student Research Committee (SRC) - Prof. S. P.
Singh (SRC chairman), Prof. Harish Hirani (Internal Expert) and Prof. Dinesh Mohan (External Expert) - for their critical and valuable suggestions that helped me look at research from a larger perspective, contributing to improvement in my problem understanding skills.
Thanks are also due to all the faculty members of Mechanical Engineering Department, and special mention for my TA supervisors during the course in IITD, Prof. J.K.Dutt, Prof. K. Gupta and Prof. S.K. Saha, who supported me at various stages of research.
I am also thankful to Mr. J.S. Kwatra, Mr. S. Babu, Mr. J.S. Rawat, Mr. Mahesh Gaur and Mr. R.V. Bhatt for their assistance during my stay at I.I.T Delhi.
I wish to express my gratitude to various colleagues /well-wishers - Dr. Anurag Soni, Dr.
Dhaval Jani, Dr. Hemant Warhatkar, Dr. Ratnakar Marathe, Dr. Mike W.J. Arun, Dr.
Debasis Sahoo, Mr. Devendra Kumar P, Mr. Wondwosen Ayelework, Mr. Piyush Gaur, Ms. Khyati Verma, Mr. Karan Devane, Mr. Devendra kumar, Mr. Kuldeep Singh, Mr.
Sanyam Sharma, Mr. Kanhaiyalal Mishra, Dr. Garima Chawla, Mr. Pit Schwanitz, Mr.
iv
Mathias Stein, Mr. Sebastian Werner and various Masters and Bachelors Students with whom I interacted during my TA, for their thought provoking discussions.
Thanks also to Dr. Suhail M. Vakhil, St. Stephen Hospital, Delhi, for his support.
A special thanks to my brother Mr. Sivaramakrishnan, my parents-in-law, Mr. N Nagarajan and Mrs. Ranjani Nagarajan, my cousins and family members for keeping me in good spirits all through my journey.
I wish to thank DAAD for providing me with funding to visit TU Berlin and undertake research work. I also wish to thank TRIPP, IIT Delhi for two fellowships from MoUD and Department of Science and Technology, Government of India for their support in financing my visits to international conferences.
Thanks cannot be enough for my wife, Mrs. Nithya Hariharan, who kept her patience through the thesis editing process and put a smile back on my face, when data played hide and seek with me!
Finally, I thank all those people, who knowingly or unknowingly inspired me in many ways throughout these years.
Looking back, I am reminded of the words written by the famous Tamil poetess Avvaiyar, “கற்றதுககமண்அளவு, கல்லாதது உலகளவு”- Katrathu Kai Mann Alavu, Kallathathu Ulagalavu”, which translates to “known is a handful, unknown is size of universe”
(Hariharan Sankarasubramanian) Indian Institute of Technology Delhi
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ABSTRACT
To contribute for VRU safety by designing safer vehicles, this work addresses the research question, “Is there a front-end shape of urban cars that causes minimal injury threat to pedestrians in a given urban population?”
Estimation of the effect of crash injury to the body of a pedestrian has been quantified by a unitary scale as Injury Cost (IC). The IC measure was extended to quantify potential injury to a pedestrian population with anthropometric variation using frequency of occurrence reported in crash data and anthropometric data of population in a particular geographical area to a weighted IC (WIC). With a measure for threat to a population formulated, parameter driven CAE based techniques were used to run simulation of crashes to set up an optimization problem. Vehicle front shape was divided into 14 parameters and single objective optimization problem formulated for reduction of WIC. A reverse Monte Carlo technique based evaluation of pre-crash variables based on post- crash data available was developed to address issues of sparsity of reconstructed pedestrian crash data. The optimization studies yielded at least one conventionally
“feasible” shape other than existing vehicle shapes with significantly better crash performance.
A methodology to search for least threat to pedestrian population within desired constraints and data variations was hence demonstrated in this work.
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TABLE OF CONTENTS
CERTIFICATE I
ACKNOWLEDGEMENTS III
ABSTRACT V
TABLE OF CONTENTS VII
LIST OF FIGURES XV
LIST OF TABLES XIX
LIST OF ABBREVIATIONS XXI
INTRODUCTION 1
CHAPTER 1
Safety of Vulnerable Road Users 1
1.1
Vehicle design and pedestrian safety 2
1.2
Regulatory and Rating tests for cars 3
1.2.1
Conflicting factors in Vehicle Design 4
1.2.2
Role of environment and other factors 5
1.3
Measure to pedestrian threat from vehicle 6
1.4
Abbreviated Injury Scale 6
1.4.1
Injury Measures 7
1.4.2
Cost implication as a measure of threat 8
1.4.3
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Characterizing vehicle-VRU crash 8
1.5
Simulation techniques for vehicle-VRU crash 9
1.6
Statistical and computational techniques 11
1.7
Optimization Techniques 11
1.7.1
Statistical methods for addressing uncertainties 13
1.7.2
Conclusion 14
1.8
LITERATURE REVIEW 15
CHAPTER 2
Injury Measurement 15
2.1
Anatomical Scales 15
2.1.1
Injury Measures 16
2.1.2
Injury risk curves 17
2.1.3
Cost based measure of Injury 19
2.1.4
Crash Databases 21
2.2
History of Crash simulation 22
2.3
Multi-body Models 22
2.3.1
2.3.1.1 Modelling of Contacts 23
2.3.1.2 Validation studies on Pedestrian Models 24
Finite Element Models 25
2.3.2
2.3.2.1 Vehicle Models 26
2.3.2.2 Pedestrian models 26
Crash reconstruction studies 27
2.3.3
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Optimization studies 28
2.4
Optimization of vehicle components 28
2.4.1
Case studies on optimization methodology 29
2.4.2
Automotive Safety tests 31
2.5
Conclusions 33
2.6
RESEARCH QUESTION AND METHODOLOGY 35 CHAPTER 3
Motivation 35
3.1
Research Question 36
3.2
Objectives and scope 37
3.3
A measure for pedestrian friendliness 37
3.3.1
Estimation of most likely vehicle-pedestrian crash scenario 37 3.3.2
Interaction of vehicle shape and pedestrians 38
3.3.3
Optimization methodology 38
3.3.4
Methodology 38
3.4
Task 1: Formulation of a single unitary measure to be used for quantifying 3.4.1
the threat from vehicle to a pedestrian population 39
Task 2: Develop a computational crash model 39
3.4.2
Task 3: An optimization problem for minimization of threat to VRU from 3.4.3
the vehicle front profile 40
Overview of the research methodology 41
3.5
Concluding remarks 41
3.6
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A MEASURE OF THREAT TO PEDESTRIANS – WEIGHTED INJURY COST
CHAPTER 4
43
Introduction 43
4.1
Estimation of threat to pedestrians from computer simulations 43 4.2
Injury Cost (IC) as a measure of threat 44
4.3
Computing IC measure from simulation results 46
4.3.1
Methodology to address a pedestrian population 47
4.4
Weighted IC factors 53
4.4.1
Weighting factor for Indian population 55
4.4.2
Simulation based studies on IC measures 56
4.5
IC with Euro-NCAP points 56
4.5.1
WIC as a measure of pedestrian threat 59
4.5.2
4.5.2.1 Coupled LS-DYNA / MADYMO simulation 59
4.5.2.2 ISS and MAIS comparison 62
4.5.2.3 Analysis of Injury threat 64
4.5.2.4 Injury cost as a measure of threat from vehicle profile 65
4.5.2.5 Discussion on simulation results 66
4.5.2.6 WIC measure 68
Concluding remarks 68
4.6
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FORMULATION OF VEHICLE-PEDESTRIAN CRASH SIMULATION INPUTS
CHAPTER 5
69
Introduction 69
5.1
Development of a parametric vehicle model 69
5.2
Vehicle front model 70
5.2.1
Design space formulation 74
5.2.2
5.2.2.1 Indian vehicle fleet representation 74
5.2.2.2 Parameterization of multibody vehicle front model 75
5.2.2.3 Vehicle model in 3D 85
Constraints on shape of the vehicle 86
5.3
Study on under-bonnet packaging 86
5.3.1
Verification measures for optimization study 90
5.3.2
Specifying vehicle-pedestrian contact characteristics 91
5.4
Braking characteristics 93
5.5
Method to estimate pre-crash uncertainties in vehicle-pedestrian crash 94 5.6
Reverse Monte Carlo methodology 95
5.6.1
Head hit data from GIDAS 98
5.6.2
Vehicle –Pedestrian crash computational Model 100
5.6.3
Monte Carlo Implementation 101
5.6.4
RESULTS 104
5.6.5
5.6.5.1 Variation of output variable µ 104
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5.6.5.2 Variation of Output – head hit location 105
5.6.5.3 Variation of Input Variable – Pedestrian Location 107
Discussion 110
5.6.6
5.6.6.1 Head-hit data and simulation results comparison 110
5.6.6.2 Implication of µ factor 110
5.6.6.3 Population of car front profiles as a weighted sum 111
5.6.6.4 Car front profiles and µ 111
5.6.6.5 Known Limitations 111
Conclusions 112
5.6.7
Concluding Remarks 113
5.7
OPTIMIZATION OF VEHICLE PROFILE WITH MINIMAL THREAT TO
CHAPTER 6
PEDESTRIAN 115
Overview 115
6.1
Preliminary Optimization Studies 115
6.2
Optimization function formulation 116
6.3
Constraints for the optimization problem 117
6.3.1
6.3.1.1 Verification of vehicle front shape: 118
OptiSlangTM based Optimization implementation 119
6.4
Optimization Study 119
6.4.1
Significant Results 121
6.4.2
6.4.2.1 Feasibility of designs during optimization: 121
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6.4.2.2 Variation of parameters during GA process: 122
6.4.2.3 WIC measure based observations: 137
6.4.2.4 Vehicle profile with minimal WIC: 139
6.4.2.5 WIC measure for existing vehicle profiles: 141
6.4.2.6 Discussion on injury assessment to optimal profile 143 Discussion of the observed optimal vehicle front profile 147 6.4.3
6.4.3.1 Feasibility of the shape: 147
6.4.3.2 Threat to crash population: 147
6.4.3.3 Limitations: 148
Conclusions 148
6.4.4
Concluding Remarks 149
6.5
CONTRIBUTIONS AND CONCLUSIONS 151
CHAPTER 7
Contributions 151
7.1
Limitations 153
7.2
Future Scope 155
7.3
REFERENCES 159
APPENDIX 167
A.1 Injury measures to AIS levels 167
A.2 MATLAB code Implementation of the limits 170
A.3 AIS TO IC 172
A.4 Implementation in MATLAB 173
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A.5 Parametric Finite Element Model 176
A.3.1 SFE MACRO code 177
A.6 Sensitivity study on vehicle profile 189
Objective of the work 189
Methodology 189
Simulation Scenario: 190
Parameterization of the vehicle front model 191
Measure of threat to pedestrians 192
OptiSlangTM problem formulation 192
Results 193
Results of Sensitivity Study 193
Conclusions 196
A.7 R code for reverse MC simulation 197
A.8 Optimization using MATLAB – With WIC (no braking) 201
Discussion on optimization results for “pedestrian safe” urban vehicle 202
PUBLICATIONS FROM THIS THESIS 207
BRIEF BIO-DATA OF THE AUTHOR 209
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LIST OF FIGURES
Figure 1.1 Multibody Vehicle-VRU crash simulation model ... 10
Figure 2.1 Parameterized vehicle front profile reproduced with permission from (Linder et al., 2004) ... 30
Figure 2.2 Parameterized vehicle front model reproduced with permission from (Carter et al., 2005) ... 31
Figure 2.3 Variation of objective function reproduced with permission from (Carter et al., 2005) ... 31
Figure 3.1 Simplified Research methodology ... 38
Figure 3.2 Detailed research methodology ... 42
Figure 4.1 Injury Cost calculation ... 46
Figure 4.2 Methodology for calculation of weighting ratio for IC ... 49
Figure 4.3 Ellipsoid model of simplified Car profile with pedestrian ... 56
Figure 4.4 Force-deflection plots used to model crash simulation ... 57
Figure 4.5 Correlation of "Injury cost" with Euro NCAP pedestrian points ... 58
Figure 4.6 Simplified scenario of pedestrian -vehicle crash ... 60
Figure 5.1 Development of vehicle model ... 70
Figure 5.2 Multibody Vehicle Front model ... 72
Figure 5.3 Parameterized vehicle-front profile ... 72
Figure 5.4 Range of car profiles for optimization... 74
Figure 5.5 Three dimensional vehicle front multibody model ... 85
Figure 5.6 Section of FE Toyota Yaris model with solid engine (Opiela et al., 2011) ... 87
Figure 5.7 Measurement of clearance: Engine top to bonnet in Toyota Yaris model ... 90
Figure 5.8 Force-Deflection characteristics for Bumper ... 91
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Figure 5.9View of BLE ellipsoids ... 92
Figure 5.10 F-D characteristics BLE - green ... 92
Figure 5.11 FD characteristics BLE - yellow ... 92
Figure 5.12 FD characteristics BLE - Red ... 93
Figure 5.13 Car front based on Euro-NCAP pedestrian tests reproduced with permission from (Martinez et al., 2007) ... 93
Figure 5.14 Braking in MADYMO ... 94
Figure 5.15 Methodology of vehicle-pedestrian crash simulation for Monte Carlo Simulation ... 96
Figure 5.16 Vehicle pedestrian crash “Left” scenario ... 97
Figure 5.17 Vehicle pedestrian crash “Right” scenario ... 97
Figure 5.18 Vehicle figure from (Otte et al., 2012) edited for study ... 99
Figure 5.19 Head hit location frequency distribution along lateral direction of vehicle ( 0 to 200 cm) from GIDAS data in band located 100-150cm from front of vehicle ... 99
Figure 5.20 Four different vehicle profiles used in MC study with pedestrian orientation at left and right extreme positions ... 100
Figure 5.21 Angle limits of 50M used in MC study ... 102
Figure 5.22 Overview of Monte Carlo process implemented in R ... 103
Figure 5.23 Variation of µ over Monte Carlo process ... 105
Figure 5.24 Variation of head hit distribution coefficients ... 107
Figure 5.25 Variation of initial pedestrian positions with approximated normal distribution approximation (Generic) ... 108
Figure 5.26 Variation of initial pedestrian positions with approximated normal distribution approximation (Compact A) ... 108
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Figure 5.27 Variation of initial pedestrian positions with approximated normal
distribution approximation (Compact B) ... 109
Figure 5.28 Variation of initial pedestrian positions with approximated normal distribution approximation (Sedan D) ... 109
Figure 6.1 Constraint and verification dimensions for front end shape ... 119
Figure 6.2 Optimization methodology ( adapted from (Dynardo Gmbh, 2011)) ... 120
Figure 6.3 Variation in number of feasible designs across generations ... 122
Figure 6.4 Variation of P1 values during GA process ... 123
Figure 6.5 Variation of P2 values during GA process ... 124
Figure 6.6 Variation of P3 during GA process ... 125
Figure 6.7 Variation of P4 values during GA process ... 126
Figure 6.8 Variation of P5 values during GA process ... 127
Figure 6.9 Variation of P6 values during GA process ... 128
Figure 6.10 Variation of P7 values during GA process ... 129
Figure 6.11 Variation of P8 values during GA process ... 130
Figure 6.12 Variation of P9 values during GA process ... 131
Figure 6.13 Variation of P10 values during GA process ... 132
Figure 6.14 Variation of P11 values during GA process ... 133
Figure 6.15 Variation of P12 values during GA process ... 134
Figure 6.16 Variation of P13 values during GA process ... 135
Figure 6.17 Variation of P14 values during GA process ... 136
Figure 6.18 Variation of Median and Minimum IC measure for every generation during GA process ... 138
Figure 6.19 Two front end shapes [O1 and O2] shapes represented as multibody ellipsoid model... 139
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Figure 6.20 Comparison of optimal shape with some available simplified vehicle shapes –
(wireframe shape denotes other vehicle compared) ... 140
Figure 6.21 IC measures of simplified car profiles compared with “best” and “worst” profiles during GA optimization process ... 142
Figure 6.22 Comparison of crash kinematics for optimal shape ... 146
Figure 7.1 Summary of contributions and limitations ... 157
Figure A.1 Stages of development of a reduced FE front end model ... 176
Figure A.2 Simplified vehicle front model of Toyota Yaris in SFE Concept ... 188
Figure A.3 Methodology for the Study ... 190
Figure A.4 Simulation Scenario ... 191
Figure A.5 Pedestrian Ellipsoid models from TNO (TNO and TassB.V., 2012) ... 191
Figure A.6 Parameterization of vehicle front profile ... 191
Figure A.7 Vehicle Front Model - MADYMO ... 191
Figure A.8 OptiSlangTM problem formulation - Sensitivity / MOP... 193
Figure A.9 Input parameters with their Range in OptiSlangTM ... 193
Figure A.10 Co-efficient of Importance - parameter 01 ... 194
Figure A.11 Coefficient of Importance - Parameter 04 ... 194
Figure A.12 Significance factors Matrix ... 195
Figure A.13 Exponential function(PDF) fitted to WIC values ... 195
Figure A.14 Normal function (PDF) fitted to EIC values ... 195
Figure A.15 Optimization methodology for MATLAB based optimization ... 201
Figure A.16 Variation of WIC across 10 generations ... 203
Figure A.17 "Infeasible" optimal shapes ... 204
Figure A.18 Three “feasible” optimal shapes found for "non-braking" scenario ... 205
Figure A.19 CVF profile interaction with pedestrian models ... 206
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LIST OF TABLES
Table 1.1 Haddon's matrix [adapted from (Norton et al., 2006)] for VRU crash injuries ... 2
Table 2.1 Injury severity measurement scales ... 16
Table 2.2 A short compilations of some significant “Injury measures” ... 17
Table 2.3 “Injury measures” mapped to AIS ... 19
Table 2.4 Major MB pedestrian Models ... 25
Table 2.5 Human CAE models summarized from (EURO-NCAP, 2013) ... 26
Table 4.1 Sample Calculation of the Injury Cost ... 47
Table 4.2 Michigan data on pedestrian crashes ... 49
Table 4.3 Selection of scaled model for simulation - 'Z' scores from ... 51
Table 4.4 Selection of MADYMO pedestrian Models ... 52
Table 4.5 Distribution of adult crash population represented by pedestrian models in percentages of CP ... 53
Table 4.6 Representation of Indian population by pedestrian models ... 55
Table 4.7 Comparison of kinematics of Profile1 and Profile2 ... 62
Table 4.8 Comparison of MAIS and ISS ... 62
Table 4.9 Calculation of IC – Profile1 ... 63
Table 4.10 Calculation of IC – Profile2 ... 63
Table 4.11 Injury Measured and observed with Profile 2 ... 65
Table 4.12 Injury Measured and observed with Profile 1 ... 65
Table 4.13 Comparison of Total IC with weighted IC (USD) ... 66
Table 5.1 Parameters for vehicle front model ... 73
Table 5.2 Semi-axis dimensions and orientations of ellipsoids in MADYMO vehicle model... 75
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Table 5.3 Parameterization of sedan type vehicle profiles ... 77
Table 5.4 Location and Dimensions of Bumper Ellipsoids ... 79
Table 5.5 Location and Dimensions of Bonnet Edge ellipsoids ... 80
Table 5.6 Location and Dimensions of Bonnet related ellipsoids ... 82
Table 5.7 Location and Dimensions of Windscreen related ellipsoid ... 83
Table 5.8 Parameterization of SUV type vehicle profiles ... 84
Table 5.9 Ratios obtained from analysis of VC-Compat vehicle structure data ... 87
Table 5.10 Packaging volume calculation for Sedan segment vehicle (Ford Taurus) ... 88
Table 5.11 Packaging volume calculation for Compact segment vehicle (Toyota Yaris) . 89 Table 5.12 Comparison of density coefficients obtained for various vehicle profiles with GIDAS ... 106
Table 5.13 Comparison of density coefficients obtained for various vehicle profiles with GIDAS ... 110
Table 6.1 Variation of IC measures for shape "O1" ... 143
Table 6.2 Variation of IC measures for shape "O2" ... 143
Table 6.3 Variation of IC measures for shape “V6” profile ... 144
Table A.1 HIC to AIS levels ... 167
Table A.2 Neck Loads To AIS ... 167
Table A.3 Thorax injury measures to AIS ... 168
Table A.4 Pelvis Injury Criteria Tolerance Levels ... 169
Table A.5 Leg Fracture Index Criteria ... 169
Table A.6 AIS to IC from ISO: 13232:part 5 ... 172
Table A.7 Description of vehicle front profile parameters – refer Figure A.6 and Figure A.7 ... 191
Table A.8 Measured injury measures from pedestrian Model in MADYMO ... 192
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LIST OF ABBREVIATIONS
6C - 6-year-old child
5F - 5th percentile Female
50M - 50th percentile Male 95M - 95th percentile Male
AAAM - Association for the Advancement of Automotive Medicine
AIS - Abbreviated Injury Scale
ASCII - American Standard Code for Information Interchange B (1/2) - Bonnet (1 /2) ellipsoid
BA - Bumper Actual ellipsoid
BAW - Bumper Actual width (semi axis in Y direction) BAX - Bumper Actual ellipsoid CG location in X-axis BAZ - Bumper Actual ellipsoid CG location in Z-axis BE (1/2) - Bonnet Leading Edge ellipsoid
BE (1/2) X - Bonnet leading Edge (1/2) ellipsoid CG location in X-axis BE (1/2) Z - Bumper leading Edge (1/2) ellipsoid CG location in Z-axis
BL - Bumper Lower ellipsoid
BLE - Bonnet Leading Edge ellipsoid (3D profile)
BLW - Bumper Lower ellipsoid width (semi axis in Y direction) BLX - Bumper Lower ellipsoid CG location in X-axis
BLZ - Bumper Lower ellipsoid CG location in Z-axis
C - Cowl ellipsoid
CG - Center of Gravity
CP - Crash Population
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CX - Cowl ellipsoid CG location in X-axis
CZ - Cowl ellipsoid CG location in Z-axis
CAD - Computer Aided Design
CAE - Computer Aided Engineering
CAY - Angular orientation of Cowl about Y axis
EL - Location of lowest point of engine/ gearbox from ground
EU - Engine top height from ground
EBL - Effective Bonnet Length
EBH - Effective Bonnet Height
EBA - Effective Bonnet Angle
ECE - Economic Commission for Europe
EIC - Equi-weighted Injury Cost
EWH - Effective windscreen height
EWL - Effective windscreen length
FD - Force-Deflection
FE - Finite Element
FFC - Femur force Criterion
FARS - Fatality Analysis Reporting System FMVSS - Federal Motor Vehicle Safety Standards
GA - Genetic Algorithm
GCS - Glasgow Coma Scale
GIDAS - German In-Depth Accident Study
HIC - Head Injury Criterion
IC - Injury Cost
ICD - International Classification of Diseases
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ICS - Injury Cost Scale
ICSL - Injury Cost Scale Lethal
IHRA - International Harmonized Research Activities ISO - International Standards Organization
ISS - Injury Severity Score
JARI - Japan Automobile Research Institute
kmph - kilometre per hour
MB - Multibody
MC - Monte Carlo
MAIS - Maximum AIS
MCMC - Markov-Chain Monte Carlo
MPEE - Maximum permissible Engine elevation
NA - Not Available
Nij - Neck Injury Criterion
NCAC - National Crash Analysis Center
NCAP - New Car Assessment Program
O (1/2) - Optimal Solution (1/2)
P (1-14) - Parameter describing vehicle profile
PP - Pedestrian Population
PLM - Product Life-cycle Management
PMHS - Post Mortal Human Surrogate
RMS - Root Mean Square
SUV - Sports Utility Vehicle
ST_g - Sternum acceleration
TI - Tibia Index
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USD - US Dollars
V (1-19) - Vehicle profile ( 1-19)
VC - Viscous Criterion
VHZ - Vehicle height in Z axis
VRU - Vulnerable Road Users
W - Windscreen
W (1-2) - Worst WIC profile (1/2)
WX - Windscreen ellipsoid CG location in X axis WZ - Windscreen ellipsoid CG location in Z axis
WAD - Wrap Around Distance
WAY - Angular orientation of Windscreen about Y axis
WHO - World Health Organization
WIC - Weighted Injury Cost
ZB - Location of mean Z co-ordinates of B1 and B2 ellipsoids
