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 paper, a deep learning-based total impulse prediction method is proposed for the ignition process of solid rocket motor. A CNN-LSTM-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.

Solid rocket motors (SRMs) have been popularly used in the current aerospace industry. Performance indicators, such as pressure and thrust, are of great importance for rocket monitoring and design. However, the measurement of such signals requires high economic and time costs. In many practical situations, the thrust measurement error is large and requires manual correction. In order to address this challenging problem, a lightweight RepVGG-based cross-modality data prediction method is proposed for SRMs. An end-to-end data prediction framework is established by transforming data across different modalities. A novel RepVGG deep neural network architecture is built, which is able to automatically learn features from raw data and predict new time-series data of different modalities. The effectiveness of the proposed method is extensively validated with the field SRM data. The accurate prediction of the thrust data can be achieved by exploring the pressure data. After calculation, the percentage error between the predicted data and the actual data is less than 5%. The proposed method offers a promising tool for cross-modality data prediction in real aerospace industries for SRMs.

The hydrocarbon liquid rocket engine working environment is harsh; the thrust chamber needs to withstand high temperatures and a high-pressure working environment, and the thrust chamber wall material is difficult to bear, so it is necessary to design the cooling structure to reduce the gas damage to the chamber wall. Liquid film cooling is a common cooling method for hydrocarbon rocket engines, and numerical simulation is an important method for studying liquid film cooling. Most of the liquid film cooling numerical simulation is for a fixed model. This paper proposes a liquid film cooling numerical calculation method for a variable-configuration hydrocarbon liquid rocket engine, based on the secondary development of Fluent software (ANSYS Fluent 2022) to form a high-energy hydrocarbon liquid rocket engine design software, which can be realized on the Qt platform. The visualization interface can be for different engine injection port locations, numbers, angles, mass flow rates, and other parameters, to calculate and improve design efficiency and reduce operating difficulty.

During the ignition process of a solid rocket motor, the pressure changes dramatically and the ignition process is very complex as it includes multiple reactions. Successful completion of the ignition process is essential for the proper operation of solid rocket motors. However, the measurement of pressure becomes extremely challenging due to several issues such as the enormity and high cost of conducting tests on solid rocket motors. Therefore, it needs to be investigated using numerical calculations and other methods. Currently, the fundamental theories concerning the ignition process have not been fully developed. In addition, numerical simulations require significant simplifications. To address these issues, this study proposes a solid rocket motor pressure prediction method based on bidirectional long short-term memory (CBiLSTM) combined with adaptive Gaussian noise (AGN). The method utilizes experimental pressure data and simulated pressure data as inputs for co-training to predict pressure data under new operating conditions. By comparison, the AGN-CBiLSTM method has a higher prediction accuracy with a percentage error of 3.27% between the predicted and actual data. This method provides an effective way to evaluate the performance of solid rocket motors and has a wide range of applications in the aerospace field.