Modeling of Ordnance-Induced Pyrotechnic Shock Testing

August 1, 2016

pyro shock plateAuthors: Logan McLeod and Santina Tatum

Design of an ordnance-induced pyrotechnic shock test to meet a specific acceleration based Shock Response Spectrum (SRS) test requirement for a given test article has traditionally been an empirical process. Based on experience, the test engineer will determine a potential test configuration and then, through a trial-and-error process, modify the test parameters and configuration until acceptable SRS levels have been achieved. As a complement to this approach, National Technical Systems (NTS) has developed an explicit finite element based modeling approach to simulate an ordnance-induced pyrotechnic shock test. This tool may be used to assist with test configuration design for particularly challenging test requirements or to streamline the process of arriving at acceptable test levels during the calibration phase of a test program.

While others have recognized the value of modeling ordnance-induced pyrotechnic shock, the majority of these efforts have utilized more traditional linear implicit finite element based approaches. The implicit approach suffers from two major challenges: determining a suitable spatio-temporal force/pressure distribution on the resonating plate induced by the explosive charge detonation; and accounting for non-linear material response such as plastic deformation in the primary resonating plate which commonly occurs during an ordnance-induced pyrotechnic shock event. The explicit approach inherently overcomes both of these challenges.

The NTS-developed explicit finite element modeling approach for ordnance-induced pyrotechnic shock testing will be presented along with model predictions for specific test configurations. Predicted results will include the acceleration-time history and corresponding SRS levels for a given location on the mounting shelf. Test data for these test configurations will be presented for comparison with model predictions. Post-processing of the model results in order to facilitate comparison with measured test data will also be discussed.

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