Prediction of Solid Rocket Motor Performance Based on Deep Learning and Ignition Experimental Data

Total impulse is one of the most important parameters which is highly related to the performance of solid rocket motors. Predicting the total impulse accurately is necessary for both design and operation purposes. However, the traditional methods greatly rely on expert knowledge and are less capable of analyzing modern sophisticated equipment. In this article, a deep learning-based total impulse prediction method is proposed for the ignition process of a solid rocket motor. A convolutional neural network-long short-term memory network-attention deep neural network model is established, which can automatically process raw data for feature extraction, efficiency improvement, and prediction with high accuracy. Practical rocket data are used for validations which are collected in the ignition process. We compared the proposed method with the other popular algorithms to verify the effectiveness and superiority of this method. The results show that the proposed data processing and prediction method can achieve promising performance. The best result of average percentage error on the test set is below 2%. The dependency of the deep learning-based method on the data amount is largely reduced by using the downsampling method in data processing. In this way, the proposed method is available even with very few training data, which has good application prospects in engineering problems and provides an approach for combining artificial intelligence and solid rocket motor research.