KEYWORDS: Data modeling, Rockets, Solids, Performance modeling, Deep learning, Education and training, Convolution, Overfitting, Mathematical optimization, Design and modelling
Performance indicators such as thrust and pressure of solid rocket motors (SRMs) are essential for rocket monitoring and design. However, measuring these signals requires high economic and time costs, and thrust data is difficult to measure accurately in practice. To address this challenging problem, we propose a deep learning-based cross-modal data prediction method that uses pressure data to predict the thrust data of SRMs. By building a novel RepVGG deep neural network architecture, it automatically learns features from the original data and predicts new time-series data with different modes. We verified the effectiveness of the proposed method by calculating the error between predicted and actual data, which was less than 3% as a percentage error between the predicted and actual data. The predicted data can supplement the SRM ground experiment data and reduce the cost of data measurement.
Total impulse is highly related to the performance of solid rocket motors. Accurate prediction of total impulse is essential for both design and operation purposes. However, the traditional methods heavily rely on expert knowledge and are incapable of analyzing modern sophisticated equipment. In this paper, a novel total impulse prediction method based on deep learning is proposed. We established a CNN-LSTM-Attention deep neural network model, which can automatically process raw data of highly nonlinear for feature extraction and prediction with high accuracy. Practical rocket data are used for validations which are collected in ignition process. We compared the proposed method with the other popular algorithms to verify the effectiveness and superiority of this method. The outcomes indicate that the proposed data processing and prediction method can achieve promising performance, with the average percentage error of under 2%. By using the downsampling method in data processing, the dependency of the deep learning based method on the data amount is largely reduced. In this way, the proposed method has good application prospects in engineering problems.
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