Reverse design of solid propellant grain based on deep learning: Imaging internal ballistic data

Abstract

The reverse design of solid rocket motor (SRM) propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve. While existing reverse design methods are feasible, they often face challenges such as lengthy computation times and limited accuracy. To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape, this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network (CNN). First, a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space. Next, the internal ballistic time-series data is encoded into three-channel images, establishing a potential relationship between the ballistic curves and their image representations. A CNN is then constructed and trained using these encoded images. Once trained, the model enables efficient inference of propellant grain dimensions from a target internal ballistic curve. This paper conducts comparative experiments across various neural network models, validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images, as well as its generalization capability across different CNN architectures. Ignition tests were performed based on the predicted propellant grain. The results demonstrate that the relative error between the experimental internal ballistic curves and the target curves is less than 5%, confirming the validity and feasibility of the proposed reverse design methodology.