A design optimization approach of a solid propellant rocket motor is considered. A genetic algorithm (GA) optimization method has been used. The optimized solid rocket motor (SRM) is intended to use as a booster of a flight vehicle, and delivering a specific payload following a predefined prescribed trajectory. Sensitivity analysis of the optimized solution has been conducted using Monte Carlo method to evaluate the effect of uncertainties in design parameters. The results show that the proposed optimization approach was able to find the convergence of the optimal solution with highly acceptable value for conceptual design phase.
In this paper, a multidisciplinary design optimization (MDO) approach of a solid propellant kick rocket motor is considered. A genetic algorithm optimization method has been used. The optimized kick solid rocket motor (KSRM) is capable of delivering a small satellite of 200 kg to a circular low earth orbit (LEO) of 600 km altitude. The KSRM should accelerate from the initial apogee velocity of 5000 m/s up to the orbital insertion velocity of 7560 m/s. The KSRM design variables and the orbital insertion trajectory profile variables were optimized simultaneously, whereas the mass characteristics of the payload deployment module were assigned. A depleted shutdown condition was considered, to avoid the necessity of a thrust termination device, resulting in a reduced total mass of the KSRM. The results show that the proposed optimization approach was able to find the convergence of the optimal solution with highly acceptable value for conceptual design phase.
This paper describes the optimization approach of a three stage solid propellant launch vehicle configuration from existing solid rocket motors (SRM). The optimal launch vehicle (LV) is capable of delivering a small satellite of 100 kg to a circular low earth orbit of 400, 500 and 600 km altitude. The overall LV configuration and the trajectory profile were optimized simultaneously, thus the existing SRM parameters for first, second and three stages, vertical flight time, launch maneuver variable, maximum angle of attack, coasting time between first and second stage and the second coasting time between second and third stages were optimized. A genetic algorithm global optimization method has been implemented to perform the analysis, the algorithm consider mixed integer continuous variables. The results show that the proposed optimization approach was able to find the optimal solution for all three variants with very acceptable values, and the approach proved to be reliable for conceptual design level.
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