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Reliable testing of a mechanical system requires the procedures used for the evaluation to be repeatable and reproducible. However, it is never possible to exactly repeat or reproduce the tests that are used for evaluation. To overcome this limitation, a statistical evaluation procedure can generally be used. However, most of the statistical procedures use scalar values as input without the ability to handle vectors or time-histories. To overcome these limitations, two numerical/statistical methods for determining if the impact time-history response of a mechanical system is repeatable or reproducible are evaluated and elaborated upon. Such a system could be a vehicle, a biological human surrogate, an Anthropometric Test Device (ATD or dummy), etc. The responses could be sets of time-histories of accelerations, forces, moments, etc., of a component or of the system. The example system evaluated is the BioRID II rear impact dummy.

A comparison of the NHTSA advanced dummy and the Hybrid III is presented in this paper based on their performance in twenty four frontal impact sled tests. Various time histories pertaining to accelerations, angular velocities, deflections and forces have been compared between the two dummies in light of their design differences. This has lead to some understanding about the differences and similarities between the NHTSA advanced dummy and the Hybrid III. In general, the chest as well as the head motion in the NHTSA advanced dummy are greater. The lumbar moments in the NHTSA advanced dummy are lower than that in the Hybrid III. The upper and lower spine segments in the NHTSA advanced dummy, generally rotate more than the spine of the Hybrid III.

Developing an effective side impact ATD for assessing vehicle impact responses requires a method for evaluating that ATD's bio-fidelity. ISO/TR9790 has been in existence for some years to serve that purpose. Recently, NHTSA sponsored a research project on the post-mortem human subjects (PMHS) responses subjected to side impact conditions. Based on those newly available PMHS data, Maltese generated a new approach for creating bio-fidelity corridors for human surrogates. The approach incorporates the time factor into the evaluation equation and automates the process (Maltese et al. 2002). This paper serves as the first attempt to look closely at the new bio-fidelity corridor generation process (hereafter referred as the Maltese approach) with respect to its validity, effectiveness, as well as its practicality. The effect of mass scaling was first examined in order to ensure the integrity of the data. The time alignment scheme and the formation of the corridors were then tested.

Head-to-knee interaction of the right front passenger dummy can occur in some 30-35 MPH crash barrier tests. The biofidelity and significance of these interactions as related to predicting human response was addressed in this study. In a series of laboratory experiments an instrumented headform was dropped on the dummy knee to simulate the barrier interactions. These test results were then related to the human by dropping the same headform on the cadaver leg. The instrumented headform was dropped from three heights to impact the Part 572 dummy knee at three velocities. Two impact sites and two impact angles were used. These test parameters bracketed the barrier conditions. Measurements from headform accelerometers permitted calculation of HIC value for comparison to barrier values. Comparable experiments were subsequently performed with three unembalmed cadaver subjects using the same headform and test procedures.

A comparison of the NHTSA advanced dummy and the Hybrid III is presented in this paper based on their performance in repeated sled tests under 3 different restraint systems. The restraint systems considered are: the airbag alone, the 3-point belt alone, and a combined use of the airbag and the 3-point belt. Various time-histories pertaining to accelerations, angular velocities, deflections and forces have been compared between the two dummies in order to study their repeatability. The Hybrid III appears to be more repeatable than the NHTSA advanced dummy in its response in one case, that of restraint with the 3-point belt alone. The response of the NHTSA advanced dummy in other two restraint modes, the airbag alone and the combination of 3-point belt and airbag, appears to be no less repeatable than that of Hybrid III in this series of tests.

A new approach for improving estimates of the kinematic response of ATDs (anthropomorphic test devices) to vehicle crash events has been developed. This approach employs the Kalman Filter; a state model based estimation approach that has been widely applied to system dynamics problems ranging from navigation to missile guidance. The Kalman Filter approach combines measurements of crash event phenomena (acceleration and displacement), kinematic models of ATD behavior and statistics of sensor noise to create precise estimates of ATD motion during a crash. This paper presents an implementation of a state model and Kalman Filter for a sensor data collected from the chest of an ATD during an out-of-position airbag deployment test. Favorable comparisons are made between the Kalman Filter model approach and traditional methods involving numerical integration and differentiation.

A simple spring-mass model of the impact response of the side impact dummy (SID) is established. The spring and mass constants of the model are established through system identification methodology based on data from impact tests. The tests are performed in laboratory with hydraulically driven impactors impacting the chest and pelvis of the SID. The input data to the model consist of measured contact force or impactor velocity time histories, and the output data are accelerations on the rib, spine, and pelvis of the SID. The established model appears to predict the test results with reasonable accuracy. The main purpose of this study, however, is to use this simple model to carry out parametric studies of the response of the dummy with changing impact parameters, the result of which would be useful in understanding vehicle crash tests using the SID.

For the purposes of analyzing and understanding the general effects of a set of different vehicle attributes on overall crash outcome a fleet model is used. It represents the impact response, in a one-dimensional sense, of two vehicle frontal crashes, across the frontal crash velocity spectrum. The parameters studied are vehicle mass, stiffness, intrusion, pulse shape and seatbelt usage. The vehicle impact response parameters are obtained from the NCAP tests. The fatality risk characterization, as a function of the seatbelt use and vehicle velocity, is obtained from the NASS database. The fatality risk is further mapped into average acceleration to allow for evaluation of the different vehicle impact response parameters. The results indicate that the effects of all the parameters are interconnected and none of them is independent. For example, the effect of vehicle mass on fatality risk depends on seatbelt use, vehicle stiffness, available crush, intrusion and pulse shape.

The response of the spine acceleration to rib and pelvis acceleration input of the side impact dummy (SID) is modeled using system identification methods. The basis for the modeling is a simplified representation of the SID by a 3-mass, 2-spring system. Based on this spring-mass representation, two types of response models are established. The first is a "gray-box" type with rib/pelvis-spine relationship modeled by Auto Regression with eXogeneous (or eXtra) input (ARX) type system models. The structure of these models is partially based on the spring-mass simplified representation, hence the notion "gray- box." The parameters of these models are identified through linear regression from test data. The second type of models is noted "physical model" here, since it is strictly a state- space form of the equation of motion of the simple spring-mass representation.

One method of understanding the general mechanical response of a complex system such as a vehicle, a human surrogate, a bridge, a boat, a plane, etc., is to subject it to an input, such as an impact, and obtain the response time-histories. The responses can be accelerations, velocities, strains, etc. In general, when experiments of this type are run the responses are contaminated by sample-to-sample variation, test-to-test variability, random noise, instrumentation noise, and noise from unknown sources. One common method of addressing the noise in the system to obtain the underlying response is to run multiple tests on different samples that represent the same system and add them together obtaining an average. This functionally reduces the random noise. However, if the fundamental response of each sample is not the same, then it is not altogether clear what the average represents. It may not capture the underlying physics.

The objective of this study is to analytically model the fatality risk in frontal vehicle-to-vehicle crashes of the current vehicle fleet, and its sensitivity to vehicle mass change. A model is built upon an empirical risk ratio-mass ratio relationship from field data and a theoretical mass ratio-velocity change ratio relationship dictated by conservation of momentum. The fatality risk of each vehicle is averaged over the closing velocity distribution to arrive at the mean fatality risks. The risks of the two vehicles are summed and averaged over all possible crash partners to find the societal mean fatality risk associated with a subject vehicle of a given mass from a fleet specified by a mass distribution function. Based on risk exponent and mass distribution from a recent fleet, the subject vehicle mean fatality risk is shown to increase, while at the same time that for the partner vehicles decreases, as the mass of the subject vehicle decreases.

Simple rigid body dynamics models are created to analyze the curb- and soil-trip types of rollover events and experimental methods that are used to simulate these events. Equations for the models are given, and they are integrated numerically to obtain the solution. Solutions of the models provide a break down of the energy during these events, which exposes the importance of energy absorption, unloading, and friction during the impact-and-roll process. Furthermore, the models are used to derive the critical sliding velocity under different test parameters. They are also used to understand near-critical state responses of the vehicle, and the corresponding characteristics of the signals in the phase space.