Hybrid Optimization of Star Grain Performance Prediction Tool

Abstract

In solid propellant rocket propulsion, the design of the propellant grain is a decisive aspect. The grain design governs the entire motor performance and, hence, the whole rocket mission. The ability to decide, during design phase, the proper grain design that satisfies the predefined rocket mission with minimum losses is the ultimate goal of solid propulsion experts. This study enables to predict the pressure time curve of rocket motor with star grain configuration and also to optimize the performance prediction tool through optimization methods to maximize its prediction efficiency. A hybrid optimization technique is used. Genetic Algorithm (GA) is first implemented to find the global optimum followed by Simulated Annealing (SA) optimization method to find the accurate local optimum. A program for predicting the pressure time curve of the rocket motor is created on MATLAB and then linked to GA-SA optimizers as an application on a case study. The purposed approach is validated against satisfying data. It is found that the developed optimized program is capable of predicting rocket motor performance (including the effect of erosive burning) with acceptable accuracy for preliminary design purposes.