Performance prediction method of solid rocket motor based on the ground test

Abstract Solid rocket motor (SRM) is a core component of rockets. Considering the high cost of solid rocket motors and the high demand for transportation and storage, there is an urgent need to predict the performance of solid rocket motors before the ground ignition test to lower the cost of tests. Using data mining technology to predict various parameters of rocket motors with historical data has become a new development direction. In order to improve the data fitting effect and prediction accuracy, a novel prediction model was proposed, which tries to combine the Whale Optimization Algorithm (WOA) with the Error Correction Gated Recurrent Unit neural network (ECGRU) to correct the basic prediction performance of the Long Short-Term Memory neural network model (LSTM). Compared with traditional methods, experiments showed that the novel model was beneficial in improving the reliability of the prediction of the thrust and specific impulse.