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This paper presents a systematic study to assess maturity of Corpuscular Particle Method (CPM) to accurately predict airbag deployment kinematics and its overall responses. The study was performed in three phases: (1) a correlation assessment of CPM predicted inflator characteristics to closed tank tests; (2) a correlation assessment of CPM predicted airbag deployment kinematics, airbag pressure, reaction force from a static deployment of a Driver Airbag (DAB) and (3) a correlation prediction of the impactor force by CPM versus impactor force from physical drop tower tests. These studies were repeated using the Uniform Pressure Method (UPM), to compare the numerical methods for their accuracy in predicting the physical test, computational cost, and applicability. Results from the study suggest that CPM satisfies the fundamental energy laws, and accurately captures the realistic airbag deployment kinematics, especially during the early deployment stage, unlike UPM.

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.

Two methods of assessing the similarity of a set of impact test signals have been proposed and used in the literature, which are cumulative variance-based and cross correlation-based. In this study, a normalized formulation unites these two approaches by establishing a relationship between the normalized cumulative variance metric (v), an overall similarity metric, and the normalized magnitude similarity metric (m) and shape similarity metric (s): v=1 − m · s. Each of these ranges between 0 and 1 (for the practical case of signals acquired with the same polarity), and they are independent of the physical unit of measurement. Under generally satisfied conditions, the magnitude similarity m is independent of the relative time shifts among the signals in the set; while the shape similarity s is a function of these.