Abstract
Improving the identification accuracy of internal ballistic parameters in the solid rocket motor(SRM) is of great significance in guaranteeing that missiles fulfill their intended operational missions. In practice, the internal ballistic performance is according to the inverse calculation burning area obtained by the measured pressure data of the SRM and the measured burning rate, which still has ascending space for optimization in the prediction accuracy. Accordingly, a genetic algorithm-based method for the identification of internal ballistic parameters and performance prediction for SRMs was proposed. Based on the measured, data of limited test runs, the initial identification of the burning rate coefficient, pressure exponent and propellant density was carried out by GA (Genetic Algorithm). The model was updated on the basis of the inverse calculation burning area obtained by identification results. Then the secondary identification was carried out to modify the key parameters. The Φ50mm laboratory-scale test SRM was analyzed as an example. The internal ballistic performance in the SRM was predicted. The calculation results show that the prediction results obtained by the method are in high agreement with the measured pressure data, which verifies the effectiveness of the method in improving the prediction accuracy of the internal ballistic performance.
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