Impact detection for smart automotive damage mitigation systems

Occupant safety and severity of vehicle damage are important factors in automotive vehicle design. Smart automobiles of the future could potentially use distributed smart material sensors and actuators in order to identify impact and take appropriate evasive or mitigative actions. This provides the motivation for this study. The first part of this study is focused on detecting the location and magnitude of impact, particularly for the case where the automotive structure is subjected to minimal damage. This is accomplished by developing a generalized algorithm using the Reissner-Mindlin plate theory, the Rayleigh-Ritz energy approach, and the Lagrangian-Hamilton principle. The level of performance of this methodology is demonstrated for impacts on a simply supported rectangular plate. Different case studies for static as well as impact loading with point as well as area contacts are presented. An algorithm using deconvolution for identifying impact location and magnitude has been developed and implemented. Additionally, the influence of damage on the structural vibratory content is studied via a frequency analysis. Modal analyses for undamaged and damaged plates, with nine different damage locations and six different damage sizes, are performed. Changes in frequency and mode shapes are observed as regards the severity of the damage.