HYBRID OPTIMIZATION OF STAR GRAIN PERFORMANCE PREDICTION TOOL

ABSTRACTIn solid propellant rocket propulsion, the design of the propellant grain is a decisiveaspect. The grain design governs the entire motor performance and, hence, thewhole rocket mission. The ability to decide, during design phase, the proper graindesign that satisfies the predefined rocket mission with minimum losses is theultimate goal of solid propulsion experts. This study enables to predict the pressuretime curve of rocket motor with star grain configuration and also to optimize theperformance prediction tool through optimization methods to maximize its predictionefficiency. A hybrid optimization technique is used. Genetic Algorithm (GA) is firstimplemented to find the global optimum followed by Simulated Annealing (SA)optimization method to find the accurate local optimum. A program for predicting thepressure 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 isvalidated against satisfying data. It is found that the developed optimized program iscapable of predicting rocket motor performance (including the effect of erosiveburning) with acceptable accuracy for preliminary design purposes.