Abstract:
The prediction of aging and serviceability in single base propellants has become remarkably effortless owing to the rapid advancement of new analytical techniques, specifically High-performance liquid chromatography (HPLC). However, HPLC only provides momentary situations. To address this issue and to obtain real time aging prediction of SBPs this research explores the use of machine learning (ML) and genetic algorithms (GA). Aging refers to the deterioration of the propellant over time, which can affect its functionality and performance. The study uses predictive ML models to optimize, automate, and surveil single-base propellants in combination with GA to enhance their performance. Widely used machine learning models include support vector machines (SVM), ensemble trees (ET), Gaussian process regression (GPR), and regression trees (RT). Several criteria are used in this study to evaluate the models' accuracy and probability for prediction. The study is significant with 0.89 coefficient of determination for the optimum performing ML technique namely ET-GA to forecast the effects of aging on propellants, contributing to the advancement of testing and surveillance techniques for single-base propellants. An optimized ML model ET-GA shows maximum of 5% deviation with experimentation is used to create a Graphical User Interface (GUI) that simplifies the calculation of the remaining effective stabilizer percentage.
