Evaluation of a singular value decomposition approach for impact dynamic data correlation

Abstract

Impact dynamic tests are used in the automobile and aircraft industries to assess survivability of occupants during crash, to assert adequacy of the design, and to gain federal certification. Although there is no substitute for experimental tests, analytical models are often developed and used to study alternate test conditions, to conduct trade-off studies, and to improve designs. To validate results from analytical predictions, test and analysis results must be compared to determine the model adequacy. Correlation of models for crash analysis often compares individual sensor responses to analytical predictions; a successful correlation effort is one that matched individual sensor magnitudes and times for peak values to occur. The mathematical approach evaluated in this paper decomposes observed time responses into dominant deformation shapes and their corresponding contribution to the measured response. To correlate results, orthogonality of test and analysis shapes is used as a criterion. Data from an impact test of a composite fuselage is used and compared to finite element predictions. In this example, the impact response was decomposed into multiple shapes but only two dominant shapes explained over 85% of the measured response. Test and analysis results revealed very similar deformation shapes (orthogonality values over 0.8). Using a vector norm criterion, maximum response values and peak time are also computed for all sensors. The norm criterion is contrasted with the commonly used single sensor criterion.