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The SISAME methodology is a system for extracting one-dimensional lumped parameter vehicle crash models from non-oblique crash test data, and for simulation of such models. Model extraction is based on constrained least squares optimization of an overdetermined system of target equations for the model parameters. The SISAME computer program performs extraction and simulation with a number of features that allow user control of the computations and outputs. Additional computer programs perform data assessment/correction and filtering. Experience has shown that the SISAME methodology can efficiently produce predictively useful models that accurately capture the motions of the actual crash event. The essential formulation of SISAME and some sample applications are presented in this paper.

A formulation is presented that augments a recently developed method for determining the nonlinear stiffness and damping characteristics of structures subjected to crash-loading environments. The system identification is accomplished using adaptive time domain, constrained minimization techniques. The structural characteristics are idealized with piecewise linear segments and, in the new formulation, the stiffness and damping characteristics are postulated to be functionally dependent. The inertial and material strain rate effects, which constitute the damping characteristics, are modeled as predefined, rate dependent factors applied to the stiffness characteristics. The motivation for this research is to develop lumped mass models of automobiles from acceleration and barrier load data collected during frontal barrier crash testing. The underlying approach in this method is being applied in a global system identification methodology.