Application of a genetic algorithm to the optimization of hybrid rockets

A genetic algorithm optimization technique has been successfully applied to the design of a large hybrid rocket booster. Optimizations to minimize gross liftoff weight or total inert weight have been carried out using a hybrid rocket sizing code developed at Purdue University. The genetic algorithm was able to find optimal or near-optimal designs that contained both continuous and discrete variables. Discrete variables included the propellant combination and the number of fuel ports, whereas the continuous variables, tank pressure, chamber pressure, and oxidizer massflux level, were simultaneously optimized using the genetic algorithm. Design solutions have been obtained from a discontinuous design space that contains a very broad, shallow minimum in weight. The resulting designs are discussed with some detail, illustrating their feasibility and some significant differences with previously published designs. The use of a genetic algorithm with ii rocket sizing code appears to offer great potential to designers of rocket systems.